Collision-Free Velocity Scheduling for Multi-Agent Systems on Predefined Routes via Inexact-Projection ADMM

2026-03-23

AI Summary

This work addresses coordination in structured multi-agent transportation systems where agents follow fixed, non-reroutable paths and must schedule their passage through waypoints to avoid collisions while maximizing efficiency. The authors propose a nonlinear speed scheduling method that eliminates the need for integer ordering variables by employing a differentiable trajectory model to map time into smooth position curves. Safety constraints based on inter-agent distances are enforced over a dense temporal grid. An inexact projected ADMM algorithm efficiently solves the resulting optimization problem by integrating structured temporal updates with gradient-based collision-avoidance corrections. Experiments demonstrate that the approach consistently generates feasible and efficient schedules across diverse scenarios—including random intersections, bottlenecks, and graph-structured networks—with notably improved makespan performance in bottleneck settings compared to representative hierarchical baselines.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In structured multi-agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. This paper addresses route-constrained multi-agent coordination by optimizing waypoint passage times while preserving each agent's assigned waypoint order and nominal route assignment. A differentiable surrogate trajectory model maps waypoint timings to smooth position profiles and captures first-order tracking lag, enabling pairwise safety to be encoded through distance-based penalties evaluated on a dense temporal grid spanning the mission horizon. The resulting nonlinear and nonconvex velocity-scheduling problem is solved using an inexact-projection Alternating Direction Method of Multipliers (ADMM) algorithm that combines structured timing updates with gradient-based collision-correction steps and avoids explicit integer sequencing variables. Numerical experiments on random-crossing, bottleneck, and graph-based network scenarios show that the proposed method computes feasible and time-efficient schedules across a range of congestion levels and yields shorter mission completion times than a representative hierarchical baseline in the tested bottleneck cases.

Problem

• multi-agent systems
• predefined routes
• collision avoidance
• velocity scheduling
• route-constrained coordination

Innovation

• inexact-projection ADMM
• differentiable surrogate trajectory
• velocity scheduling
• multi-agent coordination
• collision avoidance