arXiv (All)
open access
ART-TEB: Adaptive Trajectory Planning for Mobile Robots in Cluttered Environments
Trajectory planning for mobile robots remains a major challenge, particularly in cluttered environments, where existing planning algorithms often fail or produce suboptimal paths. To address this issue, we propose an adaptive trajectory refinement algorithm, ART-TEB, comprising two main stages. Firs
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hybrid Algorithms for Two-Level Learning and Optimization: Theory and Applications#445366
- Reassessing adult surfactant replacement therapy with mechanics-informed reinforcement learning#796312
- FrogNano: Training a 4B Coding Agent via Online Task Synthesis#1013208
- PyAPX: Python toolkit for atomic configuration pattern exploration#821744
- InstructMesh: Selective Refinement of Generative 3D Models for Fabrication#618593
Record · ID 1143636
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive