Location: University of California, Riverside (UCR); Autonomous Robots and Control Systems (ARCS) Lab
Project Members: Zhouyu Lu, Zhichao Liu, Gustavo J Correa, Dr. Konstantinos Karydis (PI)
Citation: Z. Lu, Z. Liu, G. J. Correa, and K. Karydis. Risk-aware Motion Planning for Collision-resilient Mobile Robots in Unknown Obstacle-cluttered Environments. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020. (https://arxiv.org/pdf/2009.01973.pdf)
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.
The image on the left is the collision-resilient Omnipuck platform built in the laboratory. It's body is surrounded by a reflection ring that enables the robot to collide with environment and rebounce from it. The image on the right depicts a case where the switching controller present within the motion planning algorithm detects potential collisions that may occur in future time and determines that the best action is to collide and follow the wall of the obstacle's boundary.
Images are from [@lu2020motion].
[@lu2020motion] Z. Lu, Z. Liu, G. J. Correa, and K. Karydis. Risk-aware Motion Planning for Collision-resilient Mobile Robots in Unknown Obstacle-cluttered Environments. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2020. (https://arxiv.org/pdf/2009.01973.pdf)