Title
Statistically Distinct Plans for Multi-Objective Task Assignment
Author
Wilde, N. (TU Delft Learning & Autonomous Control)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control) 
Date
2024
Abstract
We study the problem of finding statistically distinct plans for stochastic task assignment problems such as online multi-robot pickup and delivery (MRPD) when facing multiple competing objectives. In many real-world settings robot fleets do not only need to fulfil delivery requests, but also have to consider auxiliary objectives such as energy efficiency or avoiding human-centered work spaces. We pose MRPD as a multi-objective optimization problem where the goal is to find MRPD policies that yield different trade-offs between given objectives. There are two main challenges: 1) MRPD is computationally hard, which limits the number of trade-offs that can reasonably be computed, and 2) due to the random task arrivals, one needs to consider statistical variance of the objective values in addition to the average. We present an adaptive sampling algorithm that finds a set of policies which i) are approximately optimal, ii) approximate the set of all optimal solutions, and iii) are statistically distinguishable. We prove completeness and adapt a state-of-the-art MRPD solver to the multi-objective setting for three example objectives. In a series of simulation experiments we demonstrate the advantages of the proposed method compared to baseline approaches and show its robustness in a sensitivity analysis. The approach is general and could be adapted to other multi-objective task assignment and planning problems under uncertainty.
Subject
Costs
Linear programming
Multi-Objective Optimization
Multi-Robot Task Assignment
Optimization
Path Planning for Multiple Mobile Robots
Pickup and Delivery
Planning
Quality of service
Robots
Task analysis
To reference this document use:
http://resolver.tudelft.nl/uuid:26134021-9159-423c-a8b1-5bcf5621938b
DOI
https://doi.org/10.1109/TRO.2024.3359530
Embargo date
2024-07-29
ISSN
1552-3098
Source
IEEE Transactions on Robotics, 40, 2217-2232
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 N. Wilde, J. Alonso-Mora