使用解耦规划和轨迹协调的社会一致多机器人导航
Socially Consistent Multi-Robot Navigation Using Decoupled Planning and Trajectory Coordination
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中文总结 AI 辅助
研究多机器人在人类环境中的导航，通过解耦规划与轨迹协调，改进A*规划器嵌入社会规范构建社会图，转化轨迹协调为凸规划，实现可预测、符合社会规范的路径规划，简化多机器人协调。
中文摘要 AI 辅助
移动机器人在以人类为中心的环境中的成功集成需要安全高效、可预测且符合社会规范的导航。现有研究多关注短期人类感知规划，缺乏长期一致性机制。本文提出部分去中心化框架，将全局路径规划与轨迹协调解耦。先改进A*规划器嵌入社会规范，共享路径构建社会图增强一致性。再将轨迹协调问题转化为混合整数凸规划，有效计算无冲突轨迹。结果表明该方法能产生可预测、符合社会规范的路径，简化多机器人协调问题。
英文摘要
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.
