arXiv:2607.05683v1 Announce Type: new Abstract: Battery charging of Autonomous Mobile Robots (AMRs) in warehouses is a critical operational challenge that heavily impacts both order processing times and throughput. In this study, we address the dynamic AMR charging problem under stochastic order arrivals, where robots must learn optimal charging decisions. Traditional fixed-rule heuristics often prove suboptimal in dynamic environments and fail to account for multi-AMR coordination, leading to severe resource inefficiencies. To overcome these limitations, we propose a Proximal Policy Optimizat
The increasing complexity of warehouse automation and the proliferation of AMRs necessitate more sophisticated battery management solutions to maximize operational efficiency and minimize downtime.
Efficient battery management for autonomous robots is a critical bottleneck for scaling automation in logistics and manufacturing, directly impacting throughput and operational costs.
This research proposes a more adaptive, AI-driven approach to battery management for AMRs, moving beyond fixed heuristics to optimize charging decisions dynamically in complex environments.
- · Logistics and e-commerce companies
- · Warehouse automation providers
- · AI/ML algorithm developers
- · Robotics manufacturers
- · Companies with suboptimal warehouse automation
- · Manual warehouse operations
- · Fixed-rule charging system providers
Improved efficiency and reduced operating costs for warehouses utilizing AMRs.
Accelerated adoption of AMRs due to enhanced reliability and return on investment.
Increased demand for advanced AI capabilities and compute at the edge to manage complex robotic fleets in real-time.
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Read at arXiv cs.LG