Dynamic Risk Density for Autonomous Navigation in Cluttered Environments without Object Detection

In this paper, we examine the problem of navigating cluttered environments without explicit object detection and tracking. We introduce the dynamic risk density to map the congestion density and spatial flow of the environment to a cost function for the agent to determine risk when navigating that environment. We build upon our prior work, wherein the agent maps the density and motion of objects to an occupancy risk, then navigate the environment over a specified risk level set. Here, the agent does not need to identify objects to compute the occupancy risk, and instead computes this cost function using the occupancy density and velocity fields around them. Simulations show how this dynamic risk density encodes movement information for the ego agent and closely models the object-based congestion cost. We implement our dynamic risk density on an autonomous wheelchair and show how it can be used for navigating unstructured, crowded and cluttered environments.