Multi-robot Routing Research Articles

Robust Multiple-Path Orienteering Problem: Securing Against Adversarial Attacks

The multiple-path orienteering problem asks for paths for a team of robots that maximize the total reward collected while satisfying budget constraints on the path length. This problem models many multirobot routing tasks, such as exploring unknown environments and information gathering for environmental monitoring. In this article, we focus on how to make the robot team robust to failures when operating in adversarial environments. We introduce the robust multiple-path orienteering problem (RMOP), where we seek worst case guarantees against an adversary that is capable of attacking at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> robots. We consider two versions of this problem: RMOP offline and RMOP online. In the offline version, there is no communication or replanning when robots execute their plans, and our main contribution is a general approximation scheme with a bounded approximation guarantee that depends on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> and the approximation factor for single-robot orienteering. In particular, we show that the algorithm yields a: 1) constant-factor approximation when the cost function is modular; 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> factor approximation when the cost function is submodular; and 3) constant-factor approximation when the cost function is submodular, but the robots are allowed to exceed their path budgets by a bounded amount. In the online version, the RMOP is modeled as a two-player sequential game and solved adaptively in a receding horizon fashion based on Monte Carlo tree search. In addition to theoretical analysis, we perform simulation studies for ocean monitoring and tunnel information-gathering applications to demonstrate the efficacy of our approach.

Deterministic annealing with Potts neurons for multi-robot routing

A deterministic annealing (DA) method is presented for solving the multi-robot routing problem with min–max objective. This is an NP-hard problem belonging to the multi-robot task allocation set of problems where robots are assigned to a group of sequentially ordered tasks such that the cost of the slowest robot is minimized. The problem is first formulated in a matrix form where the optimal solution of the problem is the minimum-cost permutation matrix without any loops. The solution matrix is then found using the DA method is based on mean field theory applied to a Potts spin model which has been proven to yield near-optimal results for NP-hard problems. Our method is bench-marked against simulated annealing and a heuristic search method. The results show that the proposed method is promising for small-medium sized problems in terms of computation time and solution quality compared to the other two methods.

Hierarchical Planning for Heterogeneous Multi-Robot Routing Problems via Learned Subteam Performance

This letter considersa particular class of multi-robot task allocation problems, where tasks correspond to heterogeneous multi-robot routing problems defined on different areas of a given environment. We present a hierarchical planner that breaks down the complexity of this problem into two subproblems: the high-level problem of allocating robots to routing tasks, and the low-level problem of computing the actual routing paths for each subteam. The planner uses a Graph Neural Network (GNN) as a heuristic to estimate subteam performance for specific coalitions on specific routing tasks. It then iteratively refines the estimates to the real subteam performances as solutions of the low-level problems become availableon a testbed problem having a heterogeneous multi-robot area inspection problem as the base routing task, we empirically show that our hierarchical planner is able to compute optimal or near-optimal (within 7%) solutions approximately 16 times faster (on average) than an optimal baseline that computes plans for all the possible allocations in advance to obtain precise routing times. Furthermore, we show that a GNN-based estimator can provide an excellent trade-off between solution quality and computation time compared to other baseline (non-learned) estimators.

Prognostics-aware multi-robot route planning to extend the lifetime

Transformation to Industry 4.0, manufacturing systems need more intelligent devices with capable of self-awareness. Prognostic-aware robotic systems are one of key components for the self-awareness in manufacturing. The prognostics-aware route planning is one of the key components for the success of the multi-robot team during the long-term and uninterrupted operations with also extending lifetime and reducing maintenances costs. In this study, a Prognostics-aware Multi-Robot Route Planning (P-MRRP) algorithm is proposed for extending lifetime of the robot team. In the P-MRRP algorithm, firstly routes are obtained from route set construction algorithm and most reliable route set is selected by calculating Probability of Route Completion (PoRC) according to reliability of the robot team. The proposed algorithm also considers effect of load during the route of robots. In this study, the reliability of the robot is updated considering both the travelled distances with route of robot and the load of robot between pickup and/or delivery nodes. The results of P-MRRP algorithm are compared with the results of classical MRRP. The performance of the algorithm shows that the lifetime of mobile robot team can be extended by using the P-MRRP algorithm.

Multi-Robot Routing Problem with Min–Max Objective

In this paper, we study the “Multi-Robot Routing problem” with min–max objective (MRR-MM) in detail. It involves the assignment of sequentially ordered tasks to robots such that the maximum cost of the slowest robot is minimized. The problem description, the different types of formulations, and the methods used across various research communities are discussed in this paper. We propose a new problem formulation by treating this problem as a permutation matrix. A comparative study is done between three methods: Stochastic simulated annealing, deterministic mean-field annealing, and a heuristic-based graph search method. Each method is investigated in detail with several data sets (simulation and real-world), and the results are analysed and compared with respect to scalability, computational complexity, optimality, and its application to real-world scenarios. The paper shows that the heuristic method produces results very quickly with good scalability. However, the solution quality is sub-optimal. On the other hand, when optimal or near-optimal results are required with considerable computational resources, the simulated annealing method proves to be more efficient. However, the results show that the optimal choice of algorithm depends on the dataset size and the available computational budget. The contribution of the paper is three-fold: We study the MRR-MM problem in detail across various research communities. This study also shows the lack of inter-research terminology that has led to different names for the same problem. Secondly, formulating the task allocation problem as a permutation matrix formulation has opened up new approaches to solve this problem. Thirdly, we applied our problem formulation to three different methods and conducted a detailed comparative study using real-world and simulation data.

Scheduling for multi-robot routing with blocking and enabling constraints

This paper considers the problem of servicing a set of locations by a fleet of robots so as to minimize overall makespan. Although motivated by a specific real-world, multi-robot drilling and fastening application, the problem also arises in a range of other multi-robot domains where service start times are subject to precedence constraints and robots must be routed in space and time to avoid collisions. We formalize this general problem and analyze its complexity. We develop a heuristic local search procedure for solving it and analyze its performance on a set of synthetically generated problem instances, some of which capture the specific structure of the motivating drilling and fastening application, and others that generalize to other application settings. We provide a differential analysis of our local search procedure and a comparison to other approaches to demonstrate the efficacy of the proposed heuristic.

Multirobot Routing Algorithms for Robots Operating in Vineyards

We consider the problem of multirobot routing in vineyards, a task motivated by our ongoing project aiming at creating a corobotic system to implement precision irrigation on large-scale commercial vineyards. The problem is related to a combinatorial optimization problem on graphs called “team orienteering.” Team orienteering is known to be NP-hard, thus motivating the development of heuristic solutions that can scale to large problem instances. We propose three different parameter-free approaches informed by the domain we consider and compare them against a general purpose heuristic developed previously. In numerous benchmarks derived from data gathered in a commercial vineyard, we demonstrate that our solutions outperform the general purpose heuristic and are scalable, thus allowing us to solve instances with tens of thousands of vertices in the graphs. Note to Practitioners —Routing problems with budget and motion constraints are pervasive to many applications. In particular, the structural constraints considered in this problem are found not only in agricultural environments but also in warehouse logistics and other domains where goods are arranged along regular linear structures. This article proposes and analyzes algorithms that can be applied when multiple agents must be coordinated in these environments. In particular, by utilizing domain-specific knowledge, the solutions proposed in this article outperform general purpose approaches that poorly scale with the size of the environment. The algorithms we present also ensure that no collisions occur between robots—an aspect normally neglected in algorithms previously proposed to solve the team orienteering problem.

Sequential single-item auction improvements for heterogeneous multi-robot routing

We introduce new auction bidding and resolution algorithms to improve multi-robot sequential single-item auctions for heterogeneous systems. We consider two objectives, minimising the energy usage and time required to complete all tasks. Sequential single-item auctions are computationally inexpensive while producing efficient task allocations for homogeneous robots, but produce less efficient allocations for heterogeneous robots. Our algorithms provide consistent and significant (up to 20%) improvements for both objectives for a number of scenarios relative to the standard auction process, as tested in MATLAB simulations. Interestingly, our algorithms produce faster task completion even in homogeneous systems. We also introduce a new algorithm for sequential single-item auctions when robots have partial knowledge of their environment. We illustrate its improved performance and analyse its sensitivity, showing that precise tuning is not essential for faster and more efficient task completion. These improvements can reduce energy usage and task completion times for both indoor and outdoor robots in a variety of fields.

A Distributed Version of the Hungarian Method for Multirobot Assignment

In this paper, we propose a distributed version of the Hungarian method to solve the well-known assignment problem. In the context of multirobot applications, all robots cooperatively compute a common assignment that optimizes a given global criterion (e.g., the total distance traveled) within a finite set of local computations and communications over a peer-to-peer network. As a motivating application, we consider a class of multirobot routing problems with “spatiotemporal” constraints, i.e., spatial targets that require servicing at particular time instants. As a means of demonstrating the theory developed in this paper, the robots cooperatively find online suboptimal routes by applying an iterative version of the proposed algorithm in a distributed and dynamic setting. As a concrete experimental test bed, we provide an interactive “multirobot orchestral” framework, in which a team of robots cooperatively plays a piece of music on a so-called orchestral floor.

Evaluation of Auction-Based Multi-Robot Routing by Parallel Simulation

Auction methods are a promising approximation approach for distributed routing including multi-robot routing, where targets on a map need to be allocated to agents while a team objective is satisfied. While many algorithms based on sequential single-item (SSI) auctions have been presented, they are currently evaluated by serial simulation where agents serially calculate their bids on a single machine. We consider a scenario where a bidding algorithm incurs significant computational overhead due to on-demand calculations of the shortest distances on a road map. We evaluate the bidding algorithm under parallel simulations where agents perform bid calculations simultaneously on a parallel machine, and reveal that the algorithm suffers from severe synchronization overhead ignored by serial simulation. We also present the broadcasting and speculation techniques to alleviate such synchronization overhead. Our empirical results on multi-robot routing variants show that both techniques improve the efficiency of parallelization, and speculation achieves more significant improvement.

Multi-robot routing under connectivity constraints

This paper addresses the problem of routing multiple range constrained robots to service spatially distributed requests at specified time instants, while ensuring a connected information exchange network at all times. We discuss the feasibility aspects of such a connectivity constrained routing problem. In particular, we derive the minimum number of robots required to service such requests, and we present an algorithm for the explicit construction of the corresponding routes for every robot, with total length traveled as the optimization criterion.

Multi-robot routing for servicing spatio-temporal requests: A musically inspired problem

In this paper, we consider the problem of routing multiple robots to service spatially distributed requests at specified time instants. As a motivating example, we present the Robot Music Wall, a musically instrumented surface where planar positions correspond to distinct notes of an instrument. Multiple robots with the ability to traverse the wall can effectively “play” a piece of music by reaching positions on the wall that correspond to the musical notes in the piece, at specified time instants. We show that the multi-robot routing problem for servicing such spatio-temporal requests can be formulated as a pure assignment problem with the resulting reduction in complexity. Moreover, we derive the minimum number of robots required to service such requests.

A FAST AND ROBUST ALGORITHM FOR COLOR-BLOB TRACKING IN MULTI-ROBOT COORDINATED TASKS

In this paper, a fast and robust algorithm is presented for color-blob tracking, which is applicable in a multi-robot cooperative control system. The algorithm, which is immune to uneven illumination, identifies the current poses (positions and orientations) of a robot and the manipulated object (a rectangle box) from a color image, in real time. Two main challenges are faced in the multi-robot task considered in the paper. The first one concerns the response speed of the vision subsystem. The second challenge comes from uneven lighting, which makes it very difficult for the vision subsystem to trace a specific color blob in different positions. A fast computer vision algorithm is presented to cope with these challenges. First, an image in the RGB (Red-Green-Blue) color space is converted into the HSI (Hue-Saturation-Intensity) color space. Then the Saturation and the Intensity components of the image are removed and only the Hue component is retained. Second, filtering and template matching technologies are employed to remove the disturbances from the background and other objects in the image. Finally, coordinate transformations are used to reconstruct the poses of the robot and the object when they are moving. A multi-robot route planning approach is presented, which uses the information acquired by the color-blob tracking algorithm. The experimental results are presented to show the feasibility and the effectiveness of the algorithm.