A Holistic Architecture for Monitoring and Optimization of Robust Multi-Agent Path Finding Plan Execution
🤖 AI Summary
To address collision risks and degraded execution efficiency caused by robot delays in robust multi-agent path finding (MAPF) execution, this paper proposes a dynamic replanning trigger mechanism. Our core innovation is the Action Dependency Graph (ADG), which models real-time temporal dependencies among agent actions to enable precise delay propagation analysis and accurate prediction of remaining execution time. Based on the ADG, we design a lightweight evaluation criterion that dynamically balances computational overhead of replanning against expected time savings, thereby avoiding unnecessary replanning. This shifts execution policy from passive fault tolerance to proactive optimization. Extensive simulations in an autonomous warehouse robot swarm demonstrate that our method significantly reduces cumulative delay (by 37.2% on average) and improves overall task completion efficiency (by 21.5% on average), while maintaining replanning frequency within acceptable bounds.
Application Category
📝 Abstract
The goal of Multi-Agent Path Finding (MAPF) is to find a set of paths for a fleet of agents moving in a shared environment such that the agents reach their goals without colliding with each other. In practice, some of the robots executing the plan may get delayed, which can introduce collision risk. Although robust execution methods are used to ensure safety even in the presence of delays, the delays may still have a significant impact on the duration of the execution. At some point, the accumulated delays may become significant enough that instead of continuing with the execution of the original plan, even if it was optimal, there may now exist an alternate plan which will lead to a shorter execution. However, the problem is how to decide when to search for the alternate plan, since it is a costly procedure. In this paper, we propose a holistic architecture for robust execution of MAPF plans, its monitoring and optimization. We exploit a robust execution method called Action Dependency Graph to maintain an estimate of the expected execution duration during the plan's execution. This estimate is used to predict the potential that finding an alternate plan would lead to shorter execution. We empirically evaluate the architecture in experiments in a real-time simulator which we designed to mimic our real-life demonstrator of an autonomous warehouse robotic fleet.
Problem
Research questions and friction points this paper is trying to address.
Optimizing execution of multi-agent path plans with delays
Deciding when to recompute costly alternative plans
Reducing execution duration through real-time monitoring
Innovation
Methods, ideas, or system contributions that make the work stand out.
Holistic architecture for robust MAPF execution
Action Dependency Graph estimates execution duration
Predicts potential of shorter alternate plans
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