Planning Trajectories that Bounce: Reflection Classes for Collision-Tolerant Robots

2026-09-22

AI Summary

该研究提出了一种利用受控墙面反射来规划机器人轨迹的方法，旨在减少执行时间和操纵努力，通过构造反射增强状态图系统地枚举和选择最优反射策略。

Abstract

Robot navigation methods tend to avoid contact, and consequently search for collision-free trajectories. For robots with high inertia and limited maneuverability, however, avoiding contact can require substantial steering effort and time, even when interactions with surrounding surfaces could be safely exploited. In this paper, we develop a planning method that deliberately uses controlled wall reflections to generate trajectories that can be easier and more efficient to execute than purely collision-free motion. We consider planar navigation in environments where a mobile robot is permitted to bounce off surrounding surfaces. To represent the resulting alternatives, we construct a reflection-augmented state graph in which paths are partitioned into distinct classes according to the sequence of walls used for reflection. This representation enables systematic enumeration of reflection strategies and identification of the lowest-cost path within each class. We show that, although a reflecting path cannot be shorter than the shortest collision-free path, it can reduce execution time and actuation effort by replacing costly changes in heading with controlled environmental interactions. The planned trajectories are executed using a contact-aware sampling-based controller with the robot's full dynamics. In our experiments, we demonstrate that in our simulated test scenario, the best reflecting class can reduce time and control effort. Our results show that controlled contact can provide dynamically advantageous navigation strategies that are excluded by conventional collision-avoidance formulations.

Problem

• controlled wall reflections
• collision-tolerant robots
• robot navigation
• trajectory planning
• environmental interactions

Innovation

• controlled wall reflections
• reflection-augmented state graph
• collision-tolerant robots
• dynamically advantageous navigation