Collision-Backpropagation based Obstacle Avoidance Method for a Legged Robot Expressed as a Simplified Dynamics Model

Jinwon Kim, Heechan Shin, and Sung-Eui Yoon
IEEE International Conference on Control, Automation and Systems (ICCAS), 2022


Simplified dynamics models have been widely adopted to reduce the computational complexity of motion
planning for legged robots. However, not much research has been conducted on the collision avoidance for a simplified
dynamics model. To contribute to this problem, we present the collision-backpropagation based obstacle avoidance
method (CBOA), in which we employ the gradient flow of the collision cost to optimize the trajectory, thus avoiding
collisions with obstacles. Our experiment shows that the CBOA reduces the collision rate of planned trajectories by up to
15.89 times compared to a previous implicit collision avoidance method.