Motion Planning for Collision-resilient Mobile Robots in Obstacle-cluttered Unknown Environments with Risk Reward Trade-offs
This work addresses the problem of enabling robots to operate more effectively in cluttered, unknown environments by shifting from collision avoidance to exploitation, which is incremental as it builds on existing sampling-based planning methods.
The paper tackles motion planning for mobile robots in unknown, obstacle-filled environments by developing an algorithm that can deliberately exploit collisions to progress toward goals, rather than strictly avoiding them, and demonstrates through experiments with a custom robot that it can trade off risk levels to improve trajectory statistics like time and path length.
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.