Socially Consistent Multi-Robot Navigation Using Decoupled Planning and Trajectory Coordination

Abstract

The successful integration of mobile robots in human-centric environments requires navigation that is not only safe and efficient, but also consistent and compliant with social conventions: key precursors for human comfort and acceptance. Most human-aware navigation research targets the local planner, generating short-horizon, sensing-dependent reactive behavior around nearby humans. However, some social conventions are long-horizon routing choices that are better established at the global planning level, where the positions of individual humans are either unobservable or likely to be stale by the time the robot arrives. We develop a partially decentralized global planning system that encodes social conventions and coordinates multiple robots, complementing local planners that react to dynamically sensed humans. First, we introduce a general mechanism for embedding sensing-independent social conventions into a modified A* cost function. Planned paths are shared across the fleet to collaboratively build a shared social graph of established routes, enforcing path consistency and reducing future planning effort. Second, we leverage the resulting structure of these socially consistent paths to formulate multi-robot trajectory coordination as a mixed-integer convex program. While the underlying optimization is solved centrally, the constraint generation is distributed across the fleet, enabling efficient computation of conflict-free trajectories. Through simulation and hardware experiments, we demonstrate that viewing global social path planning and multi-robot trajectory coordination as a single system, rather than independent problems, produces socially consistent, repeatable paths and simplifies multi-robot coordination.

Accepted to Civil Engineering Sciences