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Design and control of wheel-legged robots navigating high obstacles — University Of Southern California (US20250291353A1)

University Of Southern California · Google Patents

Google Patents · Patents · License: Open Access

patent, google patents, intellectual property, US20250291353A1, University Of Southern California, Quan Nguyen, en, 2025

ABSTRACT

Abstract

Methods and systems are provided for controlling wheel-legged quadrupedal robots using pose optimization and force control according to quadratic programming (QP) are disclosed. An example robotic system leverages the whole-body motion and the wheel actuation to roll over high obstacles while keeping the wheel torques to navigate the terrain. Wheel traction and balancing is employed for the robot body. Linear rigid body dynamics with wheels are used for real-time balancing control of wheel-legged robots. Further, an effective pose optimization method is implemented for locomotion over steep ramp and stair terrains. The pose optimization solves for optimal poses to enhance stability and enforce collision-fee constraints for the rolling motion over stair terrain.

Description

PRIORITY CLAIM

The present disclosure claims the priority to and benefit of U.S. Provisional Application Ser. No. 63/333,850, filed Apr. 22, 2022. The contents of that application are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present application generally relates to systems and methods for controlling and designing wheel-legged robots.

BACKGROUND

All publications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

The recent technological and theoretical developments in both robot design and controls have allowed the world to witness many successful and highly autonomous legged robots. With such hardware and software advancements, researchers in the robotics field are now facing a challenge to develop mobile legged robots that can conduct given tasks fully autonomously. Another challenge is providing a control framework that can perform robustly in terrains with uneven surfaces with obstacles.

In many real-life applications, robots are required to complete long-range operations over complex terrains with high energy efficiency. No single locomotion mechanism is ideally suited for this combination of requirements. Many bipedal and quadruped robots have demonstrated outstanding maneuverability and dynamic locomotion in unknown terrain over the last decade. These robots have proven to have great potential to be controlled autonomously. However, energy efficiency in the robot hardware remains one of the most important conditions that determines whether a mobile robot can perform real-life tasks that require extended periods of time while maintaining highly dynamical locomotion, such as for rescue and disaster-response missions. Legged robots rely on a gait sequence and proper foot placement to overcome obstacles and uneven surfaces. Due to their morphology, legged robots have a unique capability to navigate rough terrain. They can leverage different gait sequences by lifting and swinging certain legs while walking to place their feet in strategic positions and overcome high obstacles or uneven surfaces. Legged robots have also demonstrated the ability to jump onto an obstacle three or four times higher than their body height. However, while legged robots have advantages in navigating uneven terrain, they are not reliable at achieving high speeds and not energy efficient in traveling for long distances. Wheeled robots, in contrast, are generally much more energy efficient and capable of faster speeds on an even surface or flat ground. However, robots with only wheels have a very limited capability in navigating rough terrains. Therefore, methods and systems are provided herein for designing, operating, determining motion planning for and/or controlling a highly dynamic wheel-legged quadrupedal robot, a hybrid system of both wheels and legs that could leverage the advantages from both worlds. The wheel-legged robot described herein may run and jump over extreme terrain at high speed with high energy efficiency. Using legs for robots is a more effective method in rough terrain locomotion compared to only wheeled systems. However, wheeled systems generally require far less energy consumption and have faster speed to maneuver on an even surface.

In one example approach for the control of wheel-legged robots is a wheel-legged quadruped robot, ANY-mal, that utilizes a kinodyanmics model in whole-body model-predictive control for robust locomotion control. In another approach, the wheel-legged bipedal robot, Ascento, adopts the whole-body dynamics by using a linear-quadratic regulator (LQR) and Zero-moment point (ZMP) in balance control.

Further, force-based quadratic programming (QP) balancing control has been implemented on the MIT quadruped robot Cheetah 3 and allows the robot to balance even after performing very dynamical and aerial tasks, such as jumping. Modifying simplified dynamics in a QP balance controller has also gained success in controlling mobile legged robots. Trajectory optimization frameworks in motion planning have allowed legged robots to achieve dynamic locomotion. For example, certain robots utilize a nonlinear programming problem (NLP) solver to find optimal trajectories for a certain task. However, one important constraint in such optimization frameworks is the utilization of robot dynamics, either full body dynamics or simplified centroidal dynamics. Hence, a hybrid system of both wheels and legs leverages the advantages from both worlds, enabling maneuverability in rough terrain, energy efficiency, and speed.

Thus, there is a need for a robot that utilizes both the advantages of legs and wheels for traversing terrain with obstacles. There is a further need for a control system that allows pose optimization for a robot to navigate large obstacles in a terrain with minimal computational costs. There is also a need for a simplified combined control system for a robot with wheel-legs.

SUMMARY

Systems and methods for wheel-legged robot design and control are disclosed to address the above needs. In one example, a method for controlling wheel-legged robot leverages the wheel traction to traverse challenging terrains (i.e., instead of stepping over a high obstacle, the whole body motion and wheel actuation are leveraged to roll over the obstacle). The obstacle height a legged robot can step over is limited. In contrast, the example control method described herein allows the robot to roll over an obstacle that is higher than the robot's nominal standing height. Further, the method described herein includes performing pose optimization to solve for optimal and collision-free poses for the robot rolling up high stairs or other obstacles.

An example control paradigm provided herein for wheel-legged robots considers the wheel dynamics and terrain slope in the model. The control paradigm is also adopted for the rolling task instead of just a standing balance.

Further, while the previous approaches in motion planning of a wheel-legged robot utilize either full body dynamics or simplified centroidal dynamics, an example approach with pose optimization described herein uses kinematic constraints instead to guarantee collision-free terrain navigation and solves for favorable configurations to maintain balance. The advantage of the example pose optimization framework is that the robot can leverage and adapt to the shape and height of the obstacle to overcome the obstacle rather than simply stepping or rolling over it. Thus, the pose optimization framework allows the robot to overcome an obstacle with a height that exceeds its nominal standing height. Further, in the pose optimization framework, dynamics or Inverse Kinematics (IK) are not used to solve for joint angles by relative foot position (i.e., the technique used by kinodynamics models). Instead, the pose optimization framework directly uses joint angles, body center of mass (CoM) location in 2D, and body pitch angle as the only optimization variables. The example pose optimization also uses Forward Kinematics (FK) to constrain the relative foot position and collision-avoidance in a favorable pose, which allows a much faster solving time. The dynamics of the robot are considered in a force-based feedback controller for real-time motion planning and control. The optimal pose is then used in combination with a balance controller using QP-based force control to maintain balance and desired pitch angle. Pose optimization also happens to resonate with a crawling mode. In the crawling mode, due to only few critical poses being needed, the computation intensity is dramatically scaled-down compared to full trajectory optimizations. Unlike an approach which adapts a posture of a wheel-legged robot in rough terrain with feedback control, or an approach which adds passive suspension for pose adapting, the example method of finding the optimal poses or robot configurations herein is based on the terrain map.

In this way, the methods and systems described herein introduce a new rigid body dynamics with wheels dynamics that can be effectively used for force-based balancing control of wheel-legged robots. The pose optimization method with kinematic and collision-free constraints only requires solving a few critical poses in a task that consists of high obstacles, while maintaining wheel traction with the terrain. For example, only two poses are needed to solve in a single-stair task. The pose optimization is thus very efficient due to its small problem size. The solved optimal poses at a certain location can be linearly interpolated to obtain the joint trajectory at any given time during the task. Further, a hybrid control framework is utilized that includes force-based QP and joint proportional derivative (PD) control to track optimal poses in order to achieve stable locomotion of wheel-legged robots navigating terrain with high obstacles. Experimental validation based on a real robot demonstrates the capability of rolling up on a 0.36 m obstacle. The experimental robot also successfully rolled up and down multiple stairs without lifting its legs or colliding with the terrain.

One disclosed example is a method for operating a wheel-legged robot. One or more of a desired thigh joint torque for a thigh of a leg of the wheel-legged robot and a desired calf joint torque for a calf of the wheel leg of the wheel-legged robot is determined by a balance controller. The thigh is coupled to the calf via the calf joint. A desired wheel torque for a wheel of the wheel leg of the wheel-legged robot is determined by a rolling controller based on one or more of a wheel traction and yaw. The wheel is coupled to the calf via a wheel joint. One or more of a calf motor, a thigh motor, and a wheel motor of the wheel leg are operated according to the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque.

Another disclosed example is a wheel-legged robot with a set of wheel legs. Each wheel leg includes a thigh actuator rotating a thigh link, a calf actuator rotating a calf link coupled to the thigh link, and a wheel actuator rotating a wheel coupled to the calf link. An input accepts a command for the wheel-legged robot to traverse. A balancing controller is coupled to each of the wheel legs and coupled to the input. The balancing controller determines a desired thigh joint torque for each thigh link and a desired calf joint torque and operating the calf actuators and thigh actuators according to the desired torques. A rolling controller is coupled to each of the wheel legs and the input. The rolling controller determines a desired wheel torque for each wheel based on one or more of a wheel traction and yaw and operates the wheel actuators according to the desired wheel torque.

Another disclosed example is a control system for a wheel-legged robot having wheel-legs. Each of the wheel-legs include a thigh link, a thigh actuator, a calf link, a calf actuator, a wheel, and a wheel actuator. The control system includes an input controller accepting a command for traversing the robot. The input controller outputs a desired position and velocity of the robot. A balancing controller is coupled to the output of the input controller. The balancing controller determines a desired thigh joint torque for each of the thigh links and a desired calf joint torque for each of the calf links. A rolling controller is coupled to the input controller to determine a desired wheel torque for each of the wheels based on one or more of a wheel traction and yaw. A drive controller operates the calf actuator, thigh actuator and wheel actuator according to the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque.

Another disclosed example is a non-transitory, machine readable medium having stored thereon instructions for controlled a wheel-legged robot. The stored instructions comprise machine executable code, which when executed by at least one machine processor, causes the machine processor to determine one or more of a desired thigh joint torque for a thigh of a leg of the wheel-legged robot and a desired calf joint torque for a calf of the wheel leg of the wheel-legged robot, the thigh coupled to the calf via the calf joint. The code causes the machine processor to determine a desired wheel torque for a wheel of the wheel leg of the wheel-legged robot based on one or more of a wheel traction and yaw, the wheel coupled to the calf via a wheel joint. The code causes the machine processor to operate one or more a calf actuator, a thigh actuator, and a wheel actuator of the wheel leg according to the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque.

Another disclosed example is a method of controlling a legged robot to navigate obstacles in a path through a terrain. Terrain of the path is sensed via a terrain sensor. An obstacle from the path is determined from data from the terrain sensor. A series of poses to navigate the obstacle is determined via a pose optimization routine. A pitch angle of the robot, and joint angles of a thigh of a leg of the robot and a calf of the leg of the robot are output by the routine. One or more of a desired thigh joint torque for the thigh and one more of a desired calf joint torque from the pitch angle and joint angles are determined via a tracking controller. A calf actuator and a thigh actuator of the leg are operated according to the desired thigh joint torque and the desired calf joint torque.

Another disclosed example is a robot having a set of legs. Each of the legs includes a thigh link actuated by the thigh actuator, a calf actuator, and a calf link actuated by the calf actuator. The robot includes a terrain sensor and a pose optimization controller coupled to the terrain sensor. The pose optimization controller determines at least one pose to traverse an obstacle in the terrain and outputs a pitch angle and joint angles for the thigh links and calf links. A tracking controller accepts a desired joint angle for the thigh links and calf links and outputs torques for the thigh and calf actuators. A driver controller controls the actuators to position the thigh links and calf links according to the output torques.

Another disclosed example is a non-transitory, machine readable medium having stored thereon instructions for controlling a robot. The stored instructions comprise machine executable code, which when executed by at least one machine processor, causes the machine processor to sense terrain of the path via a terrain sensor. The code causes the machine processor to determine an obstacle from the path from data from the terrain sensor and determine via a pose optimization routine a series of poses to navigate the obstacle. The pose optimization routine outputs a pitch angle of the robot, and joint angles of a thigh of a leg of the robot and a calf of the leg of the robot. The code causes the machine processor to determine one or more of a desired thigh joint torque for the thigh and one more of a desired calf joint torque from the pitch angle and joint angles. The code causes the machine processor to operate a calf actuator and a thigh actuator of the leg according to the desired thigh joint torque and the desired calf joint torque.

The above advantages and other advantages, and features of the present description will be readily apparent from the following Detailed Description when taken alone or in connection with the accompanying drawings. It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any p

PRIORITY CLAIM

TECHNICAL FIELD

The present application generally relates to systems and methods for controlling and designing wheel-legged robots.

BACKGROUND

SUMMARY

BRIEF DESCRIPTION OF DRAWINGS

In order to describe the manner in which the above-recited disclosure and its advantages and features can be obtained, a more particular description of the principles described above will be rendered by reference to specific examples illustrated in the appended drawings. These drawings depict only example aspects of the disclosure, and are therefore not to be considered as limiting of its scope. These principles are described and explained with additional specificity and detail through the use of the following drawings:

FIG. 1 A shows a side view of an example wheel-legged robot rolling up an obstacle, according to an embodiment of the disclosure;

FIG. 1 B shows a perspective view of the example wheel-legged robot in FIG. 1 A , according to an embodiment of the disclosure;

FIG. 2 shows an example wheel-legged leg of the example robot in FIG. 1 A , according to an embodiment of the disclosure;

FIG. 3 shows a block diagram of a robotics system for the example robot in FIG. 1 A , according to an embodiment of the disclosure;

FIG. 4 shows a block diagram of an example hybrid control based architecture, according to an embodiment of the disclosure;

FIG. 5 shows a flow chart illustrating an example method for operating a wheel-legged robot, according to an embodiment of the disclosure;

FIG. 6 A is a diagram of the example wheel dynamics of the robot leg in FIG. 2 , according to an embodiment of the disclosure;

FIG. 6 B is a diagram of the example 2D simplified rigid body dynamics with the wheel in the robot leg in FIG. 1 A , according to an embodiment of the disclosure;

FIG. 7 A shows a series of pose optimization results determined for a single stair task;

FIG. 7 B show a series of pose optimization results determined for a multiple-stair task;

FIG. 8 A shows a series of experiment snapshots of rolling an example wheel-legged robot over a ramp with the pose optimization, according to an embodiment of the disclosure;

FIG. 8 B shows a series of experiment snapshots of rolling an example wheel-legged robot over a ramp without the pose optimization;

FIG. 9 A shows a series of example hardware experiment snapshots for the example robot driving up a single step, according to an embodiment of the disclosure;

FIG. 9 B shows a series of example hardware experiment snapshots for the example robot driving up three consecutive stairs, according to an embodiment of the disclosure;

FIG. 10 shows joint angle and pitch angle tracking plots in an experimental single-stair obstacle task with pose optimization;

FIG. 11 shows example operations of an example wheel-legged robot in different terrains, according to an embodiment of the disclosure;

FIG. 12 A shows a front perspective view of another example robot with two wheel-legs, according to an embodiment of the disclosure;

FIG. 12 B shows a back perspective view of the example robot with two wheel-legs in FIG. 12 A , according to an embodiment of the disclosure;

FIG. 12 C is a front view of the example robot with two wheel-legs in FIG. 12 A , according to an embodiment of the disclosure;

FIG. 12 D is a rear view of the example robot with two wheel-legs in FIG. 12 A , according to an embodiment of the disclosure; and

FIG. 12 E is a close up perspective view of the drive assembly for the example robot with two wheel-legs in FIG. 12 A , according to an embodiment of the disclosure.

DETAILED DESCRIPTION

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials specifically described.

In some embodiments, properties such as dimensions, shapes, relative positions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified by the term âabout.â

Various examples of the invention will now be described. The following description provides specific details for a thorough understanding and enabling description of these examples. One skilled in the relevant art will understand, however, that the invention may be practiced without many of these details. Likewise, one skilled in the relevant art will also understand that the invention can include many other obvious features not described in detail herein. Additionally, some well-known structures or functions may not be shown or described in detail below, so as to avoid unnecessarily obscuring the relevant description.

The terminology used below is to be interpreted in its broadest reasonable manner, even though it is being used in conjunction with a detailed description of certain specific examples of the invention. Indeed, certain terms may even be emphasized below; however, any terminology intended to be interpreted in any restricted manner will be overtly and specifically defined as such in this Detailed Description section.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations may be depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

The present disclosure is directed toward an example dynamic wheel-legged robot that is capable of extreme terrain mobility. The example dynamic wheel-legged robot includes both wheels and legs that can leverage the advantages from both leg based robots and wheel based robots. The example dynamic wheel-legged robot enables maneuverability, high energy efficiency, and high speed on rough terrain. As an example, the ROLLER1 (ROLing-with-LEgs Robot V1) that incorporates the principles herein can overcome a wide variety of extreme terrains using different functionalities. The example ROLLER1 robot can combine walking and rolling on rocky terrain, rolling while crawling to go through a small opening, rolling on a steep slope or jumping through a large gap.

Due to their morphology, legged robots have a unique capability to navigate rough terrain. However, while legged robots have advantages in navigating uneven terrain, they are not reliable at achieving high speeds and not energy efficient in traveling for long distances. Wheeled robots, in contrast, are generally much more energy efficient and capable of faster speeds on an even surface or flat ground. However, robots with only wheels have a very limited capability in navigating rough terrains. Therefore, methods and systems are provided herein for operating and/or controlling a highly dynamic wheel-legged quadrupedal robot. The wheel-legged robot is a hybrid system of both wheels and legs that leverages the advantages from both leg based robots and wheel based robots. The wheel-legged robot described herein may run and jump over extreme terrain at high speed with high energy efficiency.

With the capability of navigating rough terrain with high speed and high energy efficiency, the methods and systems described herein may be useful for applications such as last-mile delivery, space exploration, search and rescue, firefighting, inspection in construction, mining, and nuclear plant operation. Currently, mostly wheeled vehicles are used for space exploration and last-mile delivery. However, such solutions are limited as they cannot access places that require rough terrain navigation. Legged robots are expanding their roles in disaster response and the construction industry due to the complexity of the terrains in those scenarios. Nevertheless, slow navigation speeds and short operation times are disadvantages that limit the performance of legged robots in these applications. Thus, the wheel-legged robots described herein can effectively address these shortcomings while offering an improved capability of navigating rough terrain.

For example, the example wheel-legged robotic system may be used in off-world extreme lunar terrain applications. Compared to the traditional wheeled mobility systems, the example wheel-legged robot has the capability of traversing into deep, shadowed craters to search for resources. Further, the example wheel-legged robot can travel up steep slopes with rocky terrains to place communications or power generation systems. In addition, the example wheel-legged robot can travel deep into subterranean features or high porosity surfaces in search of lunar volatiles and surface samples.

Besides the space industry, the example wheel-legged robot may be used to navigate to and through difficult to access locations such as remote or rural areas. By controlling the ground reaction forces of all four legs, the example wheel-legged robot is also extremely effective in traversing slippery terrains such as snow.

Further, in various implementations, the wheel-legged robot is capable of utilizing different locomotion modes such as rolling, simultaneous walking-rolling, and pure walking modes to maximize mobility in various challenging terrains such as mud, grass, sand, and even snow.

FIG. 1 A shows a side view of an example wheel-legged robot 100 rolling up to an obstacle 150 . In this example, the obstacle 150 is taller than the robot 100 and requires the example control framework using pose optimization to traverse. FIG. 1 B shows a perspective view of the example wheel-legged robot 100 . The robot 100 may also be referred to as a robot, robotic device, or mobile robot, among other designations. The wheel-legged robot 100 includes a robot trunk enclosure 110 , and four sets of 3 degrees of freedom (DoF) legs 120 , 122 , 124 and 126 . The legs 120 , 122 , 124 and 126 are arranged in a quadruped with a front right leg 120 , a front left leg 122 , a back right leg 124 and a back left leg 126 . Each leg 120 , 122 , 124 and 126 includes a thigh, a calf, and a wheel that are all actuated by different groups of respective actuator assemblies 130 , 132 , 134 , and 136 controlled by the robot control system. In total, the example wheel-legged robot 100 has 12 actuators for the four legs 120 , 122 , 124 and 126 . The wheel actuators assemblies 130 , 132 , 134 , and 136 are strategically positioned near the trunk enclosure 110 . This allows delivery of power to the wheels via a two-stage timing belt system, rather than being directly mounted onto the wheel. This design minimizes inertia on the legs 120 , 122 , 124 and 126 , enabling enhanced precision in force-based control.

The trunk enclosure 110 encloses components such as a power supply, a control system, a transceiver, payload, and sensor support components. As will be explained, the control system allows the robot 100 to traverse uneven terrain with large obstacles such as the obstacle 150 .

FIG. 2 shows an example leg assembly and joints of a wheeled leg with three degrees of freedom such as the leg 120 in FIG. 1 A . The example leg 120 includes a thigh link 210 , a calf link 212 , and a wheel 214 . The thigh link 210 is rotatably coupled to a calf joint 216 . One end of the calf link 212 thus may be rotated on the calf joint 216 . An opposite end of the calf link 212 is coupled to the wheel 214 . The leg 120 is actuated by a thigh actuator 220 , a calf actuator 222 , and a wheel actuator 224 . In this example, the actuators 220 , 222 , and 224 are torque-controller AI motors that can provide 21.0 rad/s maximum angular speed and 33.5 Nm maximum torque output that are available from Unitree. Other types of actuators with different angular speeds and torques may be used.

In this example, the actuator assembly 130 includes a transmission box 230 that supports the actuators 220 , 222 and 224 . One end 240 of the thigh link 210 opposite the calf joint 216 is rotatably supported by the transmission box 230 . The thigh link 210 may thus be rotated by the actuator 220 . An opposite end 242 of the thigh link 210 supports a pin that allows the rotation of the calf link 212 around the calf joint 216 . The calf link 212 includes a linkage 244 that is rotatably attached to the calf actuator 222 and rotates the calf link 212 around the calf joint 216 . In this example, the leg mounting components for the actuators and transmission assemblies are achieved by using laser-cut parts. This design is thus low-cost and light-weight allowing energy savings for the actuators. The light-weight parts also reduces unwanted dynamic effect of the legs in the system and lowers motor torque limit requirements during balancing control and navigating high obstacles.

The end of the calf link 212 coupled to the wheel 214 includes an axle 250 that has one end that supports a hub 252 . The exterior surface of the hub 252 includes a set of treads 254 that contact the terrain surface. The opposite end of the axle 250 is attached to a pulley wheel 256 . The calf joint 216 includes a translational pulley wheel 258 that is mounted on opposite ends of an axle from a main pulley wheel 260 . The main pulley wheel 260 is rotated by an upper wheel drive belt 262 that is rotated by a drive wheel supported by the transmission box 230 . The drive wheel is rotationally powered by the wheel actuator 224 to rotate the upper wheel belt 262 . The rotation of the upper wheel drive belt 262 rotates the main pulley wheel 260 and thus the translational pulley wheel 258 . A lower wheel drive belt 264 that is proximate the calf link 212 rotates the pulley wheel 256 that in turn rotates the wheel hub 252 .

In this example, the mass (m) of the example robot 100 is 11.84 kg, the body inertia in the x dimension (I xx ) is 0.0214 kgÂ·m 2 , the body inertia in the y dimension (I yy ) is 0.0535 kgÂ·m 2 , and the body inertia in the z dimension (I zz ) is 0.0443 kgÂ·m 2 . The body length (I b ) is 0.247 m, the body width (w b ) is 0.194 m, and the body height (h b ) is 0.114 m. The thigh length (I 1 ) is 0.2 m and the calf length (I 2 ) is 0.2 m. The wheel radius (R wheel ) is 0.05 m. It is to be understood that the example dimensions may be modified for larger or smaller robots with different mass, and corresponding thigh and calf lengths and wheel radius.

A block diagram of an example robotic system 300 that may be used in connection with the implementations described herein of the example robot 100 is shown at FIG. 3 . Referring to FIG. 3 , the robotic system 300 may be configured to operate autonomously, semi-autonomously, and/or using directions provided by user(s). The robotic system 300 may be implemented in various forms, such as a wheel-legged robot which may be a biped robot, quadruped robot, such as the robot 100 , or some other arrangement.

As shown, the robotic system 300 may include processor(s) 302 , data storage 304 , and controller(s) 308 , which together may be part of a control system 310 . The robotic system 300 may also include sensor(s) 322 , power source(s) 324 , actuators 326 , and transceiver(s) 328 . In this example, the actuators 326 represent the actuator assemblies 130 , 132 , 134 , and 136 in FIGS. 1 A- 1 B . The robotic system 300 may further include mechanical components and/or electrical components. Nonetheless, the robotic system 300 is shown for illustrative purposes, and may include more or fewer components. The various components of robotic system 300 may be connected in any manner, including wired or wireless connections. Further, in some examples, components of the robotic system 300 may be distributed among multiple physical entities rather than a single physical entity. Other example illustrations of robotic system 300 may exist as well.

Processor(s) 302 may operate as one or more general-purpose hardware processors or special purpose hardware processors (e.g., digital signal processors, application specific integrated circuits, etc.). The processor(s) 302 may be configured to execute computer-readable program instructions 330 , and manipulate data 332 , both of which are stored in the data storage 304 . The processor(s) 302 may also directly or indirectly interact with other components of the robotic system 300 , such as sensor(s) 322 , power source(s) 324 , actuators 326 , transceiver 328 , mechanical components, and/or electrical components. The transceiver 328 may be used to communicate data or command signals with an external device.

The data storage 304 may be one or more types of hardware memory. For example, the data storage 304 may include or take the form of one or more computer-readable storage media that can be read or accessed by processor(s) 302 . The one or more computer-readable storage media can include volatile and/or non-volatile storage components, such as optical, magnetic, organic, or another type of memory or storage, which can be integrated in whole or in part with processor(s) 302 . In some implementations, the data storage 304 can be a single physical device. In other implementations, the data storage 304 can be implemented using two or more physical devices, which may communicate with one another via wired or wireless communication. As noted previously, the data storage 304 may include the computer-readable program instructions 330 and the data 332 . The data 332 may be any type of data, such as configuration data, sensor data, and/or diagnostic data, among other possibilities.

The controller 308 may include one or more electrical circuits, units of digital logic, computer chips, and/or microprocessors that are configured to (perhaps among other tasks), interface between any combination of the mechanical components, the sensor(s) 322 , the power source(s) 324 , the electrical components, the control system 310 , and/or a user of the robotic system 300 . In some implementations, the controller 308 may be a purpose-built embedded device for performing specific operations with one or more subsystems of the robotic device 100 .

The controller 308 may monitor and physically change the operating conditions of the robotic system 300 . In doing so, the controller 308 may serve as a link between portions of the robotic system 300 , such as between mechanical components and/or electrical components. In some instances, the controller 308 may serve as an interface between the robotic system 300 and another computing device. Further, the controller 308 may serve as an interface between the robotic system 300 and a user. The instance, the controller 308 may include various components for communicating with the robotic system 300 , including a joystick, buttons, and/or ports, etc. The example interfaces and communications noted above may be implemented via a wired or wireless connection, or both. The controller 308 may perform other operations for the robotic system 300 as well.

During operation, the controller 308 may communicate with other systems of the robotic system 300 via wired or wireless connections, and may further be configured to communicate with one or more users of the robot 100 . As one possible illustration, the controller 308 may receive an input (e.g., from a user or from another robot) through the transceiver 328 indicating an instruction to perform a particular gait in a particular direction, and at a particular speed. A gait is a pattern of movement of the limbs of an animal, robot, or other mechanical structure.

Based on this input, the controller 308 may perform operations to cause the robotic device 100 to move according to the requested gait. As another illustration, the controller 308 may receive an input indicating an instruction to move to a particular geographical location. In response, the controller 308 (perhaps with the assistance of other components or systems) may determine a direction, speed, and/or gait based on the environment through which the robotic system 300 is moving en route to the geographical location. In this example, the controller 308 includes a specific balance controller 340 , a rolling controller 342 and a tracking controller 344 for performing hybrid control of the robotic system 300 .

The balance controller 340 is a quadratic programming (QP) force-based balance controller that maintains balance in various tasks. The example QP control algorithm may be solved very efficiently, and thus may be applied to real-time control of a wheel-legged robot such as the robot 100 . This balancing control only commands the thigh and calf joint torques of the wheel legs of the robot 100 . For controlling the wheels, the rolling controller 342 enables the robot 100 to maneuver with wheel traction and yaw on command. The details of the balancing control will be explained below. The controller 308 also collects real-time data relating to the joint angles of calf links and the thigh links of the legs. In order to control the robot 100 to roll over challenging terrains (e.g., stairs, high obstacles, or steep ramps), a pose optimization framework is used to solve for an optimal configuration of the robot 100 that is collision-free with the terrain, while maintaining a good support region for the robot to keep the body balanced. The desired pitch angle is fed into the balance controller 340 to maintain balance during motion of the robot 100 . The desired joint angles of the calf links of the wheeled legs are tracked by the tracking controller 344 to output a joint proportional derivative (PD) torque to control the calf actuators to manipulate the pose of the robot.

Operations of the control system 310 may be carried out by the processor(s) 302 .

Alternatively, these operations may be carried out by the controller 308 , or a combination of the processor(s) 302 and the controller 308 . In some implementations, the control system 310 may partially or wholly reside on a device other than the robotic system 300 , and therefore may at least in part control the robotic system 300 remotely.

Mechanical components represent hardware of the robotic system 300 that may enable the robot 100 to perform physical operations. As a few examples, the robotic system 300 may include physical members such as wheeled legs, leg(s), arm(s), and/or wheel(s). The physical members or other parts of robotic system 300 may further include actuators such as motors arranged to move the physical members in relation to one another. The robotic system 300 may also include one or more structured bodies for housing the control system 310 and/or other components, and may further include other types of mechanical components. The particular mechanical components used in a given robot may vary based on the design of the robot, and may also be based on the operations and/or tasks the robot may be configured to perform.

In some examples, the mechanical components may include one or more removable components. The robotic system 300 may be configured to add and/or remove such removable components, which may involve assistance from a user and/or another robot. For example, the robotic system 300 may be configured with removable arms, hands, feet, and/or legs, so that these appendages can be replaced or changed as needed or desired. In some implementations, the robotic system 300 may include one or more removable and/or replaceable battery units or sensors. Other types of removable components may be included within some implementations.

The robotic system 300 may include sensor(s) 322 arranged to sense aspects of the robotic system 300 . The sensor(s) 322 may include one or more force sensors, torque sensors, velocity sensors, acceleration sensors, position sensors, proximity sensors, motion sensors, location sensors, load sensors, temperature sensors, touch sensors, depth sensors, ultrasonic range sensors, infrared sensors, object sensors, and/or cameras, among other possibilities. Within some examples, the robotic system 300 may be configured to receive sensor data from sensors that are physically separated from the robot (e.g., sensors that are positioned on other robots or located within the environment in which the robot is operating).

The sensor(s) 322 may provide sensor data to the processor(s) 302 (perhaps by way of data 332 ) to allow for interaction of the robotic system 300 with its environment (e.g., surrounding terrain), as well as monitoring of the operation of the robotic system 300 . The sensor data may be used in evaluation of various factors for activation, movement, and deactivation of mechanical components and electrical components by control system 310 . For example, the sensor(s) 322 may capture data corresponding to the terrain of the environment or location of nearby objects, which may assist with environment recognition and navigation. In an example configuration, sensor(s) 322 may include RADAR (e.g., for long-range object detection, distance determination, and/or speed determination), LIDAR (e.g., for short-range object detection, distance determination, and/or speed determination), SONAR (e.g., for underwater object detection, distance determination, and/or speed determination), VICONÂ® (e.g., for motion capture), one or more cameras (e.g., stereoscopic cameras for 3D vision), a global positioning system (GPS) transceiver, and/or other sensors for capturing information of the environment in which the robotic system 300 is operating. The sensor(s) 322 may monitor the environment in real time, and detect obstacles, elements of the terrain, weather conditions, temperature, and/or other aspects of the environment.

Further, the robotic system 300 may include sensor(s) 322 configured to receive information indicative of the state of the robotic system 300 , including sensor(s) 322 that may monitor the state of the various components of the robotic system 300 . The sensor(s) 322 may measure activity of systems of the robotic system 300 and receive information based on the operation of the various features of the robotic system 300 , such the operation of extendable legs, arms, or other mechanical and/or electrical features of the robotic system 300 . The data provided by the sensor(s) 322 may enable the control system 310 to determine errors in operation as well as monitor overall operation of components of the robotic system 300 .

As an example, the robotic system 300 may use force sensors to measure load on various components of the robotic system 300 . In some implementations, the robotic system 300 may include one or more force sensors on an arm or a leg to measure the load on the actuators that move one or more members of the arm or leg. As another example, the robotic system 300 may use one or more position sensors to sense the position of the actuators of the robotic system and thus the joint angles of wheeled legs. For instance, such position sensors may sense states of extension, retraction, or rotation of the actuators on arms or legs.

As another example, the sensor(s) 322 may include one or more velocity and/or acceleration sensors. For instance, the sensor(s) 322 may include an inertial measurement unit (IMU). The IMU may sense velocity and acceleration in the world frame, with respect to the gravity vector. The velocity and acceleration sensed by the IMU may then be translated to that of the robotic system 300 based on the location of the IMU in the robotic system 300 and the kinematics of the robotic system 300 .

The robotic system 300 may include other types of sensors not explicated discussed herein. Additionally or alternatively, the robotic system may use particular sensors for purposes not enumerated herein.

The robotic system 300 may also include one or more power source(s) 324 configured to supply power to various components of the robotic system 300 . Among other possible power systems, the robotic system 300 may include a hydraulic system, electrical system, batteries, and/or other types of power systems. As an example illustration, the robotic system 300 may include one or more batteries configured to provide charge to components of the robotic system 300 . Some of the mechanical components and/or electrical components may each connect to a different power source, may be powered by the same power source, or be powered by multiple power sources.

Any type of power source may be used to power the robotic system 300 , such as electrical power or a gasoline engine. Additionally or alternatively, the robotic system 300 may include a hydraulic system configured to provide power to the mechanical components using fluid power. Components of the robotic system 300 may operate based on hydraulic fluid being transmitted throughout the hydraulic system to various hydraulic motors and hydraulic cylinders, for example. The hydraulic system may transfer hydraulic power by way of pressurized hydraulic fluid through tubes, flexible hoses, or other links between components of the robotic system 300 . The power source(s) 324 may charge using various types of charging, such as wired connections to an outside power source, wireless charging, combustion, or other examples.

The electrical components may include various mechanisms capable of processing, transferring, and/or providing electrical charge or electric signals. Among possible examples, the electrical components may include electrical wires, circuitry, and/or wireless communication transmitters and recei

CLAIMS

Claims ( 22 )

1 . A method for operating a wheel-legged robot, the method comprising:

determining, via a balancing controller, one or more of a desired thigh joint torque for a thigh of a leg of the wheel-legged robot and a desired calf joint torque for a calf of the wheel leg of the wheel-legged robot, the thigh coupled to the calf via the calf joint; determining, via a rolling controller, a desired wheel torque for a wheel of the wheel leg of the wheel-legged robot based on one or more of a wheel traction and yaw, the wheel coupled to the calf via a wheel joint; and operating one or more a calf motor, a thigh motor, and a wheel motor of the wheel leg according to the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque.

2 . The method of claim 1 , wherein the wheel torque is based on a wheel traction force and a desired yaw speed.

3 . The method of claim 1 , wherein the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque is based on a center of mass location for the wheel-legged robot.

4 . The method of claim 1 , further comprising performing pose optimization based on one or more terrain parameters and updating one or more of the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque via a tracking controller based on pitch angle and joint angles of a pose.

5 . The method of claim 4 , wherein the pose optimization is performed via a nonlinear programming (NLP) model subject to forward kinematic constraints and collision avoidance with a terrain model.

6 . The method of claim 5 , wherein the forward kinematic constraints include wheel contact and wheel direction.

7 . The method of claim 4 , wherein the terrain parameters are determined by a terrain sensor.

8 . The method of claim 4 , wherein the pitch angle and joint angles are linearly interpolated from an initial pose to an intermediate pose and from the intermediate pose to a final pose.

9 . The method of claim 1 , further comprising deriving a center of mass (CoM) position, velocity, pitch angle Î¸ des and angular velocity input for a path through terrain from commands of an input device and wherein the desired thigh joint torque and calf joint torque are determined from the commands of the input device.

10 . The method of claim 10 , wherein the input device is one of a human input controller, an autonomous controller, or a semi-autonomous controller.

11 . A wheel-legged robot comprising:

a set of wheel legs, each wheel leg including a thigh actuator rotating a thigh link, a calf actuator rotating a calf link coupled to the thigh link, and a wheel actuator rotating a wheel coupled to the calf link; an input to accept a command for the wheel-legged robot to traverse; a balancing controller coupled to each of the wheel legs and coupled to the input, the balancing controller determining a desired thigh joint torque for each thigh link and a desired calf joint torque and operating the calf actuators and thigh actuators according to the desired torques; a rolling controller coupled each of the wheel legs and the input, the rolling controller determining a desired wheel torque for each wheel based on one or more of a wheel traction and yaw, and operating the wheel actuators according to the desired wheel torque.

12 . The wheel-legged robot of claim 11 , further comprising:

a pose optimization controller performing pose optimization of the robot based on one or more terrain parameters, and outputting desired joint angles for the calves and thighs; and a tracking controller updating one or more of the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque based on the desired joint angles.

13 . The wheel-legged robot of claim 12 , wherein the pose optimization is performed via a nonlinear programming (NLP) model subject to forward kinematic constraints and collision avoidance with a terrain model.

14 . The wheel-legged robot of claim 11 , further comprising an enclosure with a power source and a payload compartment.

15 . The wheel-legged robot of claim 11 , wherein each of the actuators are motors.

16 . The wheel-legged robot of claim 11 , further comprising an input device coupled to the input, wherein the input device accepts commands and derives a center of mass (CoM) position, velocity, pitch angle Î¸ des and angular velocity input for a path through terrain from the commands, and wherein the desired thigh joint torque and calf joint torque are determined from the input device.

17 . The wheel-legged robot of claim 16 , wherein the input device is one of a human input controller, an autonomous controller, or a semi-autonomous controller.

18 . (canceled)

19 . A non-transitory, machine readable medium having stored thereon instructions for controlled a wheel-legged robot, the stored instructions comprising machine executable code, which when executed by at least one machine processor, causes the machine processor to:

determine one or more of a desired thigh joint torque for a thigh of a leg of the wheel-legged robot and a desired calf joint torque for a calf of the wheel leg of the wheel-legged robot, the thigh coupled to the calf via the calf joint; determine a desired wheel torque for a wheel of the wheel leg of the wheel-legged robot based on one or more of a wheel traction and yaw, the wheel coupled to the calf via a wheel joint; and operate one or more a calf actuator, a thigh actuator, and a wheel actuator of the wheel leg according to the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque.

20 - 26 . (canceled)

27 . The wheel-legged robot of claim 11 , wherein the wheel torque is based on a wheel traction force and a desired yaw speed, and wherein the desired thigh joint torque, the desired calf joint torque, and the desired wheel torque is based on a center of mass location for the wheel-legged robot.

28 . The wheel-legged robot of claim 12 , wherein the pitch angle and joint angles are linearly interpolated from an initial pose to an intermediate pose and from the intermediate pose to a final pose.

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Design and control of wheel-legged robots navigating high obstacles

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Design and control of wheel-legged robots navigating high obstacles

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