Deformation Recovery Control and Post-Impact Trajectory Replanning for Collision-Resilient Mobile Robots

TL;DR

This paper introduces a novel collision-inclusive motion planning approach for impact-resilient mobile robots, enabling real-time deformation recovery and trajectory replanning after collisions, reducing the need for conservative avoidance strategies.

Contribution

It presents a new deformation recovery controller and a post-impact trajectory replanner that directly handle collisions, improving robot resilience and efficiency during impact events.

Findings

Successfully tested on a custom impact-resilient robot

Achieved real-time collision recovery and trajectory adjustment

Demonstrated improved impact resilience and navigation efficiency

Abstract

The paper focuses on collision-inclusive motion planning for impact-resilient mobile robots. We propose a new deformation recovery and replanning strategy to handle collisions that may occur at run-time. Contrary to collision avoidance methods that generate trajectories only in conservative local space or require collision checking that has high computational cost, our method directly generates (local) trajectories with imposing only waypoint constraints. If a collision occurs, our method then estimates the post-impact state and computes from there an intermediate waypoint to recover from the collision. To achieve so, we develop two novel components: 1) a deformation recovery controller that optimizes the robot's states during post-impact recovery phase, and 2) a post-impact trajectory replanner that adjusts the next waypoint with the information from the collision for the robot to pass through and generates a polynomial-based minimum effort trajectory. The proposed strategy is evaluated experimentally with an omni-directional impact-resilient wheeled robot. The robot is designed in house, and it can perceive collisions with the aid of Hall effect sensors embodied between the robot's main chassis and a surrounding deflection ring-like structure.