Simultaneous task allocation and planning for temporal logic goals in heterogeneous multi-robot systems

Abstract

This paper describes a framework for automatically generating optimal action-level behavior for a team of robots based on Temporal Logic mission specifications under resource constraints. The proposed approach optimally allocates separable tasks to available robots, without requiring a-priori an explicit representation of the tasks or the computation of all task execution costs. Instead, we propose an approach for identifying sub-tasks in an automaton representation of the mission specification and for simultaneously allocating the tasks and planning their execution. The proposed framework avoids the need of computing a combinatorial number of possible assignment costs, where each computation itself requires solving a complex planning problem. This can improve computational efficiency compared to classical assignment solutions, in particular for on-demand missions where task costs are unknown in advance. We demonstrate the applicability of the approach with multiple robots in an existing office environment and evaluate its performance in several case study scenarios.

Numerical Evaluation of Sample Gathering Solutions for Mobile Robots

“…Under the above explanations, Algorithm 1 includes the steps for obtaining an optimal solution for Problem 1. Sort set S r:collects in ascending order based on costs ω π(s) , s ∈ S r:collects 8 for s ∈ S r:collects do 9 Append path π(s) to plan of robot r, plan r 10 Insert command to collect sample s in plan r 11 Append path −1 π(s) to plan r 12 Insert command to deposit sample s in plan r 13 Return plans plan r , ∀r ∈ R 14 else 15 Problem 1 is infeasible 16 Return Example 2. We apply the optimal solution from this section on the example introduced in Section 2.…”

“…Some works provide strategies directly implementable on particular robots with complicated dynamics and multiple sensors [3]. Other research aims to increase the task expressiveness [4], e.g., starting from Boolean-inspired specifications [5,6] up to temporal logic ones [7][8][9][10], even if the obtained plans may be applied only to simple robots.…”

Simultaneous Task Allocation and Planning Under Uncertainty

“…A small number of approaches have looked into combining multi-agent TA and planning into a single problem [1,2,8,9]. Of particular relevance to this paper is the work described in [1] and [2] .…”

“…A small number of approaches have looked into combining multi-agent TA and planning into a single problem [1,2,8,9]. Of particular relevance to this paper is the work described in [1] and [2] . They use a logical model of robot operation plus a team task specification in LTL and propose an algorithm that allocates tasks to robots in order to minimise the maximum cost any robot will take to complete its tasks.…”

“…To address this limitation, recent work has considered the problem of simultaneous task allocation and planning (STAP) [1,2], which solves the complete problem in a single process, and can therefore take the plans of each robot (and their costs etc.) into account during the allocation process.…”