Auction-Based Task Allocation with Energy-Conscientious Trajectory Optimization for AMR Fleets

2026-03-22

AI Summary

This work addresses energy consumption optimization for fleets of autonomous mobile robots (AMRs) operating in asymmetric task spaces by proposing a hierarchical two-stage framework. In the first stage, tasks are allocated via a sequential auction mechanism incorporating a closed-form bidding function and a region-aware energy-based bidding strategy, which elucidates how task-space uniformity influences bidding performance. In the second stage, each robot computes an energy-optimal trajectory using a physics-based battery model while avoiding collisions through pairwise proximity penalties. The system further integrates an event-triggered warm-start rescheduling mechanism to handle dynamic disturbances and failures. Experimental results across 505 scenarios demonstrate an average energy saving of 11.8%, with rescheduling latency under 10 milliseconds; in environments with heterogeneous friction, the region-aware bidding strategy improves energy efficiency by 2–2.4% compared to distance-based bidding.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

This paper presents a hierarchical two-stage framework for multi-robot task allocation and trajectory optimization in asymmetric task spaces: (1) a sequential auction allocates tasks using closed-form bid functions, and (2) each robot independently solves an optimal control problem for energy-minimal trajectories with a physics-based battery model, followed by a collision avoidance refinement step using pairwise proximity penalties. Event-triggered warm-start rescheduling with bounded trigger frequency handles robot faults, priority arrivals, and energy deviations. Across 505 scenarios with 2-20 robots and up to 100 tasks on three factory layouts, both energy- and distance-based auction variants achieve 11.8% average energy savings over nearest-task allocation, with rescheduling latency under 10 ms. The central finding is that bid-metric performance is regime-dependent: in uniform workspaces, distance bids outperform energy bids by 3.5% (p < 0.05, Wilcoxon) because a 15.7% closed-form approximation error degrades bid ranking accuracy to 87%; however, when workspace friction heterogeneity is sufficient (r < 0.85 energy-distance correlation), a zone-aware energy bid outperforms distance bids by 2-2.4%. These results provide practitioner guidance: use distance bids in near-uniform terrain and energy-aware bids when friction variation is significant.

Problem

• task allocation
• trajectory optimization
• energy efficiency
• multi-robot systems
• asymmetric task spaces

Innovation

• auction-based task allocation
• energy-conscientious trajectory optimization
• physics-based battery model
• event-triggered rescheduling
• hierarchical multi-robot coordination