2,162 research outputs found
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
We envision a warehouse in which dozens of mobile robots and human pickers
work together to collect and deliver items within the warehouse. The
fundamental problem we tackle, called the order-picking problem, is how these
worker agents must coordinate their movement and actions in the warehouse to
maximise performance (e.g. order throughput). Established industry methods
using heuristic approaches require large engineering efforts to optimise for
innately variable warehouse configurations. In contrast, multi-agent
reinforcement learning (MARL) can be flexibly applied to diverse warehouse
configurations (e.g. size, layout, number/types of workers, item replenishment
frequency), as the agents learn through experience how to optimally cooperate
with one another. We develop hierarchical MARL algorithms in which a manager
assigns goals to worker agents, and the policies of the manager and workers are
co-trained toward maximising a global objective (e.g. pick rate). Our
hierarchical algorithms achieve significant gains in sample efficiency and
overall pick rates over baseline MARL algorithms in diverse warehouse
configurations, and substantially outperform two established industry
heuristics for order-picking systems
Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.The authors also would like to thank their home Institute, CEFET/RJ, the federal Brazilian
research agencies CAPES (code 001) and CNPq, and the Rio de Janeiro research agency, FAPERJ, for
supporting this work.info:eu-repo/semantics/publishedVersio
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