CIAO$^\star$: MPC-based Safe Motion Planning in Predictable Dynamic Environments
This addresses safe motion planning for robots in shared workspaces with moving agents, but it is incremental as it builds upon an existing method.
The paper tackles the problem of guaranteeing collision avoidance for robots in predictable dynamic environments by proposing CIAO*, an MPC-based motion planning algorithm that generalizes a free region concept to arbitrary norms and approximates time optimal planning. The experimental evaluation shows it achieves close to time optimal behavior.
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 single body robots using model predictive control (MPC). It optimizes the motion of one agent and accounts for the predicted movement of surrounding agents and obstacles. The experimental evaluation shows that CIAO$^\star$ reaches close to time optimal behavior.