A Model Predictive-based Motion Planning Method for Safe and Agile Traversal of Unknown and Occluding Environments

Agile navigation through uncertain and obstacle-rich environments remains a challenging task for autonomous mobile robots (AMR). For most AMR, obstacles are identified using onboard sensors, e.g., lidar or cameras. The effectiveness of these sensors may be severely limited, however, by occlusions introduced from the presence of other obstacles. The occluded area may contain obstacles, static or dynamic, not included into the motion planning of the robot and could cause potential collisions if they suddenly appear in the field of view of the robot. This paper proposes a general Model Predictive Control (MPC)-based framework for handling occlusions in structured or unstructured environments, that contain known or unknown static or dynamic obstacles. Safety is promoted by commanding velocities that consider surrounding obstacle uncertainty, while perception is promoted through a specially designed objective that can reduce the occluded area created by obstacles. The effectiveness of this framework is validated through simulations that show swift and safe motion in a variety of different environments. Similarly, experimental validation is achieved with a Boston Dynamics' Spot quadruped robot operating in an occluding environment.