HMR-ODTA: Online Diverse Task Allocation for a Team of Heterogeneous Mobile Robots
🤖 AI Summary
This paper addresses the challenge of online multi-pickup and delivery with time windows (MPDPTW) for heterogeneous mobile robot teams in time-sensitive environments such as hospitals. Unlike conventional centralized or static scheduling approaches limited to homogeneous tasks, we propose a decentralized dynamic scheduling framework enabling real-time, collaborative rescheduling across diverse robot capabilities and service request types. Our method integrates time-window-constrained modeling, cooperative task reallocation, and distributed decision-making. Experiments across problem scales of 40–280 tasks demonstrate a 50%–63% reduction in late-delivery penalties, alongside significant improvements in task completion rate and timeliness. The core contribution lies in jointly optimizing real-time responsiveness, delay minimization, and rejection-aware scheduling—while explicitly supporting robot heterogeneity and service diversity.
Application Category
📝 Abstract
Coordinating time-sensitive deliveries in environments like hospitals poses a complex challenge, particularly when managing multiple online pickup and delivery requests within strict time windows using a team of heterogeneous robots. Traditional approaches fail to address dynamic rescheduling or diverse service requirements, typically restricting robots to single-task types. This paper tackles the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), where autonomous mobile robots are capable of handling varied service requests. The objective is to minimize late delivery penalties while maximizing task completion rates. To achieve this, we propose a novel framework leveraging a heterogeneous robot team and an efficient dynamic scheduling algorithm that supports dynamic task rescheduling. Users submit requests with specific time constraints, and our decentralized algorithm, Heterogeneous Mobile Robots Online Diverse Task Allocation (HMR-ODTA), optimizes task assignments to ensure timely service while addressing delays or task rejections. Extensive simulations validate the algorithm's effectiveness. For smaller task sets (40-160 tasks), penalties were reduced by nearly 63%, while for larger sets (160-280 tasks), penalties decreased by approximately 50%. These results highlight the algorithm's effectiveness in improving task scheduling and coordination in multi-robot systems, offering a robust solution for enhancing delivery performance in structured, time-critical environments.
Problem
Research questions and friction points this paper is trying to address.
Coordinating time-sensitive deliveries with heterogeneous robots
Dynamic rescheduling for diverse service requests
Minimizing late deliveries while maximizing task completion
Innovation
Methods, ideas, or system contributions that make the work stand out.
Heterogeneous robot team for diverse tasks
Dynamic scheduling algorithm for rescheduling
Decentralized algorithm optimizes task assignments
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