Halo method improves real time robot delivery routing by up to 14 percent
HALO: Heterogeneous Allocation Via Localized Observations for the Vehicle Routing Problem
RoboticsMultiagent Systems
Summary
Routing many robots to deliver packages or manage warehouses is hard when each robot can only see or talk to nearby robots. The authors created HALO, a way for robots to make quick, smart decisions on where to go by learning from their local surroundings and their tasks. HALO works even when robots don’t have full information and handles different types of robots. Tests show HALO is faster and often better than older methods, even if those methods had complete information.
What this means in practice
- •For logistics operators: Optimize routing of delivery drones and robots in real time with limited communication across wide areas to improve efficiency and responsiveness.
- •For warehouse managers: Coordinate large teams of heterogeneous robots managing tasks dynamically in partially observable environments to speed up operations safely.
Authors
Andrew Meighan, Hyungsub Kim, Or Dantsker
Abstract
Scalable robotic fleets have become increasingly popular for various applications such as package delivery, warehouse management, and military operations. Prior fleet control algorithms solve centralized routing problems with up to $1{,}000$ tasks in controlled environments, yet they fail to consider realistic constraints such as limited observation and communication ranges typical of decentralized fleets. Thus, deploying existing fleet control algorithms into real-world settings is currently infeasible. To tackle this, we propose Heterogeneous Allocation via Localized Observations (HALO) to solve the Vehicle Routing Problem (VRP). HALO is a hybrid method that splits the VRP into allocation and routing portions to provide onboard, real-time solutions to robots in dynamic environments. During the allocation phase, HALO utilizes a heterogeneous graph neural network framework with unique message passing layers to explicitly separate the learning of spatial distributions and task-to-robot compatibility. Evaluation results on a partially observable, online variant of the VRP show HALO significantly outperforms the heuristic baseline while maintaining similar solution quality to an all-knowing offline variant of HALO. While HALO is explicitly designed for partially observable environments, it imposes no strict upper bound on the observation space allowing us to test HALO on the traditional static, single-depot VRP. Here, HALO outperforms state-of-the-art architectures strictly optimized for the static variant of the VRP by up to $14.06\%$. Throughout all testing, this framework maintains the quickest execution times which emphasizes its potential for large-scale, real-time deployment.