Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

Abstract

This paper focuses on decentralized task allocation and sequencing for multiple heterogeneous robots. Each task is defined as visiting a point in a subset of the robot configuration space -this definition captures a variety of tasks including inspection and servicing. The robots are heterogeneous in that they may be subject to different differential motion constraints. Our approach is to transform the problem into a multi-vehicle generalized traveling salesman problem (GTSP). To solve the GTSP, we propose a novel decentralized implementation of large-neighborhood search (LNS). Our solution approach leverages the GTSP insertion methods proposed in Fischetti et al. [A branchand-cut algorithm for the symmetric generalized traveling salesman problem, Oper. Res. 45(3) (1997) 378-394]. to repeatedly remove and reinsert tasks from each robot path. Decentralization is achieved using combinatorial-auctions between the robots on tasks removed from robot's path. We provide bounds on the length of the dynamically feasible robot paths produced by the insertion methods. We also show that the number of bids in each combinatorial auction, a crucial factor in the runtime, scales linearly with the number of tasks. Finally, we present extensive benchmarking results to characterize both solution quality and runtime, which show improvements over existing decentralized task allocation methods.

Dec-MCTS: Decentralized planning for multi-robot active perception

“…Market-based methods (Dias et al, 2006) involve each robot negotiating over which tasks it will perform, and are more appropriate for coverage and exploration problems (Zlot et al, 2002). Sadeghi and Smith (2017) applied an auction method with traveling salesman problem (TSP) heuristics to a problem formulated as a generalization of the TSP. Stranders et al (2009) combined max-sum message passing with branch and bound pruning to find sequences of viewpoints that minimize the entropy of a Gaussian process.…”

Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

“…Solutions are proposed for heterogeneous robotic agents to be allocated to corresponding tasks that impose specific execution requirements [2]. An optimal sequence of task-agent allocation to minimize the task allocation time or the consumption of the robots' energy for a team of heterogeneous mobile robotic agents is introduced in [3]. The formation containment control designs of multi-leader follower systems have been proposed and reported in [4,5]; however, the influence of the information transmission between the agents was not considered in the design.…”

A Bid-Based Grouping Method for Communication-Efficient Decentralized Multi-UAV Task Allocation

“…The task planner typically poses an optimization problem whose objective function represents the overall mission performance, and solves it. The most popular and typical mathematical formulation for task allocation is mixed-integer programs [12][13][14][15][16], which is NP hard, and thus a variety of approximate and/or heuristic algorithms [2,[17][18][19][20][21][22][23][24][25][26][27][28][29][30] have been developed to efficiently produce solutions to the allocation problem.…”

“…Decentralized task allocation that allows each agent to make decisions based on the local information, including information available to the particular agent and information obtained via communications, can be a better framework for task allocation in a dynamic environment [2,17,18,20,21,[25][26][27]. One key requirement for decentralized task allocation is to produce conflict-free assignment.…”