Online Multi-Robot Task Assignment with Stochastic Blockages

Abstract

We study the problem of finding statistically distinct plans for stochastic planning and 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 multiobjective 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.

Optimizing Task Waiting Times in Dynamic Vehicle Routing

“…T HE Dynamic Vehicle Routing Problem is an important and long-studied challenge in assigning autonomous vehicles (or robots) to tasks as they appear in the environment. Applications include pickup-and-delivery [1], [2], mobility-on-demand transportation systems [3], [4], [5], sensor networks [6], [7], surveillance [8], [9], personal care [10], [11], and environmental monitoring [12], [13], [14].…”

“…The DVRP finds widespread applications in various robotics and transportation domains. Fleets of autonomous vehicles are deployed for pickup-and-delivery throughout cities [1], [2] or for providing on-site service in indoor environments such as hospitals [10], [11]. In many cases, vehicles need to react to new requests appearing over time.…”

Enhancing motion planning efficiency in dynamic environments through advanced algorithms for mobile robots

“…probabilistic policy search methods [170], Bayesian Optimization [171,172], and adaptive sampling [173]. Although these approaches are useful for dynamically adjusting controllers in multi-objective settings, they assume that objectives are explicitly defined, which limits the generalizability of the obtained policies to other tasks.…”