A blind-spot-aware optimization-based planner for safe robot navigation

Safe mobile robot navigation should consider not only collision avoidance with current obstacles but also include non-visible areas (to which we refer as blind spots) and the resulting risk of collision with hidden moving objects (e.g. people). Such capability is important for mobile robots operating in environments shared with humans - for instance a shopping assistant robot in a supermarket. This work aims to extend an existing motion planner for mobile robots (the Time Elastic Band planner) by including blind spots. As a result, the final planner does not only consider static and visible dynamic obstacles, but handles blind spots, too. To identify such blind spots, we define and use critical corners that imply them. Hence, our contributions in this paper are creating a critical corner detector, which operates on laser scan data, and the extension of a factor-graph-based path planner. We evaluate the proposed method standalone and in our simulation environment of a supermarket. It can be seen that the implementation is capable of detecting and dealing with blind spots. Finally, we provide source code for both the detector and the planner extensions.