HMR-ODTA: Online Diverse Task Allocation for a Team of Heterogeneous Mobile Robots

TL;DR

HMR-ODTA tackles online MPDPTW with a heterogeneous robot team using a decentralized auction-based framework that enables dynamic rescheduling via Simple Temporal Networks. The approach assigns tasks through round-robin auctioneers, STN-based scheduling, and energy-aware bid calculations, achieving substantial reductions in penalties and rejection rates compared to state-of-the-art baselines. Extensive ROS/Gazebo simulations in hospital-like environments demonstrate scalability from 40 to 280 tasks, with penalties decreasing by about 63% at small scales and 50% at larger scales, underscoring practical impact for time-critical, multi-robot coordination. The work advances online MAPDP with heterogeneity by integrating multi-criteria decision-making and adaptive scheduling to maintain high service reliability in dynamic settings.

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.

Figures (4)

• Figure 1: Top view of the hospital building Hospiotal. Labels: OT-Operation Theatre, GW-General Ward, D.O.- Doctor Office, S-Store, W-Washroom, WR-Waiting Room.
• Figure 2: The simulation of robot task allocation and navigation within a hospital building involves robots (denoted by $R_0^0$, $R_1^0$, $R_1^2$, etc.) being assigned to complete service requests labeled $j_1$, $j_2$, $j_3$, etc.), at specified locations. Pickup points are marked with red squares, while drop-off points are indicated by green squares. The green paths trace the routes the robots follow as they move between the pickup and drop-off locations. The layout illustrates the hospital floor plan, with robots navigating to complete their designated tasks.
• Figure 3: Number of Service Requests vs. Average Number of Rejected Requests. Robot Team Size = 80. Service requests are varying from 40 to 280 in increments of 40.
• Figure 4: Number of Service Requests vs. Average Penalty (in seconds). Robot Team Size = 80. Service requests are varying from 40 to 280 in increments of 40.