Abstract
This paper describes a sampling strategy for the dynamic window approach (DWA), a local path planner, for omni-directional robot motion planning. An efficient local planner allows the robot to quickly respond to dynamic obstacles and ensures that commanded velocities meet the dynamic constraints of the robot. While typical DWA implementations sample the velocity space evenly, we propose that targeted sampling (TS) will result in a more fine-grained search of the relevant velocity space, leading to better control and performance in space-constrained environments. Our TS-DWA strategy is informed by the global planned path, allowing us to sample more velocities in the general path direction. We employ a polar velocity generator to selectively sample velocities and couple angular velocity samples to the path curvature. A bias for angular velocity is added for robots with a preferred heading, such as robots with forward-mounted sensors, to quickly turn toward the desired direction for better sensing. The strategy is implemented as a robot operating system (ROS) navigation stack local_planner plugin and tested in simulation with Gazebo using an omni-directional robot platform. Experiments show that as the space around the simulated robot gets smaller, our proposed sampling strategy results in more successful navigation trials to the goal in space-constrained environments compared to other commonly used methods like DWA and timed-elastic-band, where planning fails or oscillates. TS-DWA was also deployed on the Vision60 quadrupedal robot to demonstrate navigation through narrow corridors in the real world.
