SAFE--MA--RRT: Multi-Agent Motion Planning with Data-Driven Safety Certificates

2025-09-04

AI Summary

This paper addresses collaborative motion planning for homogeneous linear multi-agent systems operating in unknown obstacle-rich environments without explicit system models. Method: We propose a fully data-driven framework that is dynamically feasible and provably safe. It learns feedback gains and local invariant ellipsoids—serving as safety certificates—by solving a semidefinite program on experimental data. Distributed, optimization-free trajectory generation is achieved by integrating grid-based RRT sampling with a spatiotemporal resource reservation mechanism. Contribution/Results: To the best of our knowledge, this is the first work to unify data-driven invariant set learning with spatiotemporal reservation. Relying solely on limited experimental data and convex optimization tools, it simultaneously guarantees collision avoidance with static/dynamic obstacles and inter-agent collisions. The framework significantly reduces computational overhead while providing formal safety guarantees. Extensive simulations validate its effectiveness under tight dynamical constraints and complex obstacle configurations.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper proposes a fully data-driven motion-planning framework for homogeneous linear multi-agent systems that operate in shared, obstacle-filled workspaces without access to explicit system models. Each agent independently learns its closed-loop behavior from experimental data by solving convex semidefinite programs that generate locally invariant ellipsoids and corresponding state-feedback gains. These ellipsoids, centered along grid-based waypoints, certify the dynamic feasibility of short-range transitions and define safe regions of operation. A sampling-based planner constructs a tree of such waypoints, where transitions are allowed only when adjacent ellipsoids overlap, ensuring invariant-to-invariant transitions and continuous safety. All agents expand their trees simultaneously and are coordinated through a space-time reservation table that guarantees inter-agent safety by preventing simultaneous occupancy and head-on collisions. Each successful edge in the tree is equipped with its own local controller, enabling execution without re-solving optimization problems at runtime. The resulting trajectories are not only dynamically feasible but also provably safe with respect to both environmental constraints and inter-agent collisions. Simulation results demonstrate the effectiveness of the approach in synthesizing synchronized, safe trajectories for multiple agents under shared dynamics and constraints, using only data and convex optimization tools.

Problem

• Data-driven motion planning for multi-agent systems without explicit models
• Ensuring dynamic feasibility and safety using invariant ellipsoids
• Preventing inter-agent collisions through space-time coordination

Innovation

• Data-driven motion planning with convex optimization
• Safety certificates via invariant ellipsoids and feedback
• Space-time reservation table for inter-agent coordination