Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs

TL;DR

This paper introduces a novel sampling-based online planning algorithm for collision-resilient robots that balances collision avoidance and exploitation, enabling robots to intentionally collide when beneficial for reaching goals.

Contribution

It presents a new joint optimization function and pruning technique for online planning that explicitly manages collision risks and exploits collisions to improve navigation in unknown environments.

Findings

Algorithm effectively balances risk and goal progress.

Deliberate collision decisions can reduce travel time.

The approach improves navigation in cluttered environments.

Abstract

Collision avoidance in unknown obstacle-cluttered environments may not always be feasible. This paper focuses on an emerging paradigm shift in which potential collisions with the environment can be harnessed instead of being avoided altogether. To this end, we introduce a new sampling-based online planning algorithm that can explicitly handle the risk of colliding with the environment and can switch between collision avoidance and collision exploitation. Central to the planner's capabilities is a novel joint optimization function that evaluates the effect of possible collisions using a reflection model. This way, the planner can make deliberate decisions to collide with the environment if such collision is expected to help the robot make progress toward its goal. To make the algorithm online, we present a state expansion pruning technique that significantly reduces the search space while ensuring completeness. The proposed algorithm is evaluated experimentally with a built-in-house holonomic wheeled robot that can withstand collisions. We perform an extensive parametric study to investigate trade-offs between (user-tuned) levels of risk, deliberate collision decision making, and trajectory statistics such as time to reach the goal and path length.