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

• URL: http://arxiv.org/abs/2509.04413v1
• Date: Thu, 04 Sep 2025 17:34:59 GMT
• Title: SAFE--MA--RRT: Multi-Agent Motion Planning with Data-Driven Safety Certificates
• Authors: Babak Esmaeili, Hamidreza Modares,
• Abstract summary: This paper proposes a fully data-driven motion-planning framework for homogeneous linear multi-agent systems.<n>Each agent independently learns its closed-loop behavior from experimental data.<n>A sampling-based planner constructs a tree of such waypoints, where transitions are allowed only when adjacent ellipsoids overlap.
• Score: 6.77934423529734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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