Task Allocation and Motion Planning in Dynamic, Cluttered Environments via CBBA and Graphs of Convex Sets

2026-06-16

AI Summary

This work addresses the challenge of coordinating multiple agents to jointly allocate dynamic tasks—such as rendezvous—and generate safe, efficient trajectories in cluttered, dynamic environments. The paper presents the first deep integration of the Consensus-Based Bundle Algorithm (CBBA) with Graphs of Convex Sets (GCS), constructing a time-expanded 3D configuration space model that enables bidirectional coupling between task assignment and spatiotemporal trajectory planning. The proposed framework supports distributed coordination, provides accurate estimates of task completion times, and embeds collision-avoidance constraints directly within the optimization formulation, thereby enabling real-time replanning. Simulation results demonstrate that the approach efficiently produces collision-free, time-optimal cooperative strategies in complex scenarios involving both static and dynamic tasks.

Abstract

Multi-agent task planning in cluttered, dynamic environments requires assigning tasks to agents while simultaneously determining safe, time-efficient trajectories through the environment. When tasks are dynamic, such as rendezvous objectives, allocation decisions depend not only on which agent is best suited for a task, but also on when and where that task can be reached. This paper presents a solution to this problem, which combines Graphs of Convex Sets (GCS) for trajectory optimization with the Consensus-Based Bundle Algorithm (CBBA) for distributed task allocation. In our approach, GCS finds optimal trajectories through dynamic environments using a time-extended (3D+time) configuration space. At the same time, CBBA coordinates task assignments across agents, enabling informed decision-making in a moving environment. We then connect allocation and planning to allow the agents to avoid collisions in the 3D+time configuration space and provide accurate time estimates for task completion. We demonstrate the effectiveness of our approach in simulated cluttered environments with static and dynamic tasks.

Problem

• Task Allocation
• Motion Planning
• Dynamic Environments
• Multi-agent Systems
• Trajectory Optimization

Innovation

• Graphs of Convex Sets
• Consensus-Based Bundle Algorithm
• multi-agent task allocation
• trajectory optimization
• dynamic cluttered environments