CIAO$^\star$: MPC-based Safe Motion Planning in Predictable Dynamic Environments

Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore it generalizes CIAO's free region concept to arbitrary norms and proposes a cost function to approximate time-optimal motion planning. The proposed method, CIAO$^\star$, finds kinodynamically feasible and collision free trajectories for constrained robots using a \ac*{mpc} framework and accounts for the predicted movement of other agents. The experimental evaluation shows that CIAO$^\star$ reaches close to time optimal behavior.