analiza aiqbench
This research advances multi-robot systems by embedding macroscopic social norms directly into global path planning, ensuring long-term behavioral consistency in human-centric environments. By decoupling high-level social-aware routing from low-level trajectory coordination via mixed-integer convex programming, the framework enables scalable conflict resolution for large fleets without overburdening local planners. The approach bridges a critical gap between safety/efficiency and predictability, which is essential for widespread public acceptance of autonomous robot deployments.
Socially Consistent Multi-Robot Navigation Using Decoupled Planning and Trajectory Coordination
arXiv:2607.20772v1 Announce Type: new Abstract: The successful integration of mobile robots in human-centric environments requires navigation that is not only safe and efficient, but also predictable and aligned with social conventions, key precursors for human comfort and acceptance. While significant research addresses short-term human-aware planning, these methods often lack mechanisms for ensuring consistent and predictable behaviors across long horizons. Without socially aware long-term planners, local planners are overburdened, resulting in inefficient and locally reactive movements that undermine predictability. This paper introduces a partially decentralized framework that generates predictable and socially consistent multi-robot motion by decoupling global path planning from trajectory coordination. First, we propose a modified A* planner that embeds macroscopic social norms into the planner cost function. Planned paths are shared across mobile robots to collaboratively build a social graph of established routes, which enforces path consistency and reduces future planning effort. Second, we leverage the emergent structure of the socially constrained paths to formulate the multi-robot trajectory coordination problem as a mixed-integer convex program. The convex program enables efficient computation of conflict- free trajectories, scaling effectively to large fleets and supporting dynamic task assignment. Our results demonstrate that enforcing social consistency at the path planning stage produces predictable, socially compliant mobile robot paths and simplifies the otherwise complex problem of multi-robot coordination.
Abstract:The successful integration of mobile robots in human-centric environments requires navigation that is not only safe and efficient, but also predictable and aligned with social conventions, key precursors for human comfort and acceptance. While significant research addresses short-term human-aware planning, these methods often lack mechanisms for ensuring consistent and predictable behaviors across long horizons. Without socially aware long-term planners, local planners are overburdened, resulting in inefficient and locally reactive movements that undermine predictability. This paper introduces a partially decentralized framework that generates predictable and socially consistent multi-robot motion by decoupling global path planning from trajectory coordination. First, we propose a modified A* planner that embeds macroscopic social norms into the planner cost function. Planned paths are shared across mobile robots to collaboratively build a social graph of established routes, which enforces path consistency and reduces future planning effort. Second, we leverage the emergent structure of the socially constrained paths to formulate the multi-robot trajectory coordination problem as a mixed-integer convex program. The convex program enables efficient computation of conflict- free trajectories, scaling effectively to large fleets and supporting dynamic task assignment. Our results demonstrate that enforcing social consistency at the path planning stage produces predictable, socially compliant mobile robot paths and simplifies the otherwise complex problem of multi-robot coordination.
Robotics (cs.RO)
arXiv:2607.20772 [cs.RO]
(or arXiv:2607.20772v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20772 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 302 cuvinte
Subiecte conexe
Links #5: 2026/07
LessWrong · 31 iul. 2026
SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation
arXiv · 21 iul. 2026
TRACER: Adaptive Multi-Robot Social Navigation via Joint Human-Response Prediction and Interaction-Aware Replanning
arXiv · 17 sept. 2026
Links #3: 2026/06 Part 1
LessWrong · 15 iun. 2026