Motion Generation With Environmental Constraints

2026-07-27

AI Summary

In high-dimensional and uncertain environments, traditional robotic motion planning struggles to simultaneously achieve obstacle avoidance, computational efficiency, and robust execution. This work proposes an Environment-Constrained Exploitation (ECE) approach that transcends the conventional “avoidance-as-non-contact” paradigm by treating environmental contacts as intentional constraints. These active constraints reduce the effective planning dimensionality and computational complexity while directing exploration toward task-relevant regions. By integrating contact-aware modeling and uncertainty handling within a rapidly-exploring random tree (RRT) framework, ECE substantially enhances both planning efficiency and execution robustness. Real-world experiments demonstrate that the method effectively simplifies motion planning in complex settings and improves system adaptability and overall performance.

Abstract

Robot motion planning faces challenges in high-dimensional spaces and uncertain environments, often constrained by the need for collision-free motions. We advocate an alternative approach, Environmental Constraint Exploitation (ECE), where deliberate contact with the environment simplifies planning by reducing dimensionality and computational complexity. By integrating ECE into motion planning algorithms, we bias exploration to task-relevant regions and leverage contact for uncertainty reduction to improve robustness during execution. We evaluate ECE benefits with RRT-based planners and demonstrate their practical benefits in a real-world application. This work consolidates and extends prior research, showcasing how ECE simplifies motion planning while enhancing adaptability and performance in complex environments.

Problem

• motion planning
• high-dimensional spaces
• uncertain environments
• collision-free motions
• environmental constraints

Innovation

• Environmental Constraint Exploitation
• motion planning
• contact-based planning
• RRT-based planners
• uncertainty reduction