Optimized and kinematically feasible multi-agent motion planning

2026-05-03

AI Summary

This work addresses the challenge of simultaneously achieving path optimality and kinematic feasibility in multi-agent motion planning by proposing a two-stage framework. It first generates initial collision-free paths using Conflict-Based Search (CBS) or Priority-Based Search (PBS), then refines them through a multi-phase optimal control problem (OCP) formulation. A motion primitive generation mechanism is introduced to enforce uniform sampling time constraints. Additionally, the SIPP-IP algorithm is extended to accommodate general cost functions and large-footprint agents. Experimental results demonstrate that, in trailer-truck systems, lattice-based planners outperform the extended SIPP-IP due to less conservative collision checking. In cluttered environments, CBS yields higher success rates and lower computation times than PBS, though both approaches produce solutions of comparable quality after optimization.

Abstract

Multi-agent motion planning (MAMP) is an important problem for autonomous systems with multiple agents. In this work we propose a two-step method for finding optimized and kinematically feasible solutions to MAMP problems. The first step finds an initial feasible solution using state-of-the-art methods such as conflict-based search (CBS) or priority-based search (PBS), and the second step is an improvement step which improves the solution by solving a multi-phase optimal control problem (OCP) where the initial solution is used to warm-start the solver. We also propose a method for generating motion primitives in an optimized way under the constraint that the primitive durations are all multiples of the same sample time. We evaluate our proposed framework on a MAMP problem for tractor-trailer systems. We extend the safe interval path planning with interval projections (SIPP-IP) algorithm so it can handle more general cost functions and larger agents, but our results show that for the tractor-trailer system a simple lattice-based planner performs better due to less conservative collision checks. Our experiments also indicate that CBS performs better than PBS for this system as it achieves a higher success rate in environments with obstacles and had a lower average runtime, although both planners achieve solutions of similar quality after the improvement step.

Problem

• multi-agent motion planning
• kinematic feasibility
• tractor-trailer systems
• motion primitives
• collision avoidance

Innovation

• multi-agent motion planning
• optimal control problem
• motion primitives
• kinematic feasibility
• conflict-based search