Multi-robot Task Allocation Research Articles

Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario

The hunter-and-gatherer approach copes with the problem of dynamic multirobot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multirobot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multirobot exploration algorithm for hunters based on innovative notions of “expected information gain” to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacle complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly distorts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics.

A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics

As marine plastic pollution threatens the marine ecosystem seriously, the government needs to find an effective way to clean marine plastics. Due to the advantages of easy operation and high efficiency, autonomous underwater vehicles (AUVs) have been applied to clean marine plastics. As for the large-scale marine environment, the marine plastic cleaning task needs to be accomplished through the collaborative work of multiple AUVs. Assigning the cleaning task to each AUV reasonably and effectively has an essential impact on improving cleaning efficiency. The coordination of AUVs is subjected to harsh communication conditions. Therefore, to release the dependence on the underwater communications among AUVs, proposing a reliable multi-robot task allocation (MRTA) model is necessary. Inspired by the evolutionary game theory, this paper proposes a novel multi-robot task allocation (MRTA) model based on replicator dynamics for marine plastic cleaning. This novel model not only satisfies the minimization of the cost function, but also reaches a relatively stable state of the task allocation. A novel optimization algorithm, equilibrium optimizer (EO), is adopted as the optimizer. The simulation results validate the correctness of the results achieved by EO and the applicability of the proposed model. At last, several valuable conclusions are obtained from the simulations on the three different assumed AUVs.

Performance analysis of clustering methods for balanced multi-robot task allocations

This paper models the Multi-Robot Task Allocation (MRTA) problem with a balance constraint to improve the utilisation (completion time) of the robots. Our balancing constraint attempts to minimise the travel distance difference among the robots as well as allocates an equal set of tasks to these robots. The clustering-based approach is employed to solve the Balanced Multi-Robot Task Allocation (BMRTA) problem for two principal reasons. That is, this approach clusters given tasks into groups using various clustering techniques for each robot and sequences the route for each robot using the travelling salesman problem (TSP) conhull algorithm. This work analyses the suitability and performance of the clustering techniques with respect to the balancing criteria using a benchmark dataset. Our findings suggest that K-means clustering is the most suitable for the solving BMRTA problem with complex topologies and it is scalable to deal with any number of tasks and robots compared with Gaussian Mixtures Models (GMM) and hierarchical clustering methods.

An arrovian analysis on the multi-robot task allocation problem: Analyzing a behavior-based architecture

Research in multi-robot systems is a rich field that has attracted much attention in recent decades. However, robot coordination and task allocation to a correct mission accomplishment are still challenging even with technological advances. Despite many proposals presented in the literature, the applications and theories about the task allocation problem are not yet exhausted. Thus, this work proposes an axiomatic framework based on Social Choice Theory to analyze the task allocation problem in intentional cooperation multi-robot systems. It uses Kenneth J. Arrow’s framework of his famous Impossibility Theorem. The conditions imposed by Arrow aim to create an ideal for preference aggregation mechanisms through axiomatic analysis. This paper aims to transport this analysis to the multi-robot domain. A behavior-based Multi-robot Task Allocation architecture is used to present simulation results and discuss two cases in the ordinal preference domain. The analysis results show that using the proposed framework to analyze, under the Arrovian perspective, implemented MRTA architectures is feasible.

Analysis and control of agreement and disagreement opinion cascades

We introduce and analyze a continuous time and state-space model of opinion cascades on networks of large numbers of agents that form opinions about two or more options. By leveraging our recent results on the emergence of agreement and disagreement states, we introduce novel tools to analyze and control agreement and disagreement opinion cascades. New notions of agreement and disagreement centrality, which depend only on network structure, are shown to be key to characterizing the nonlinear behavior of agreement and disagreement opinion formation and cascades. Our results are relevant for the analysis and control of opinion cascades in real-world networks, including biological, social, and artificial networks, and for the design of opinion-forming behaviors in robotic swarms. We illustrate an application of our model to a multi-robot task-allocation problem and discuss extensions and future directions opened by our modeling framework.

Dynamic Task Allocation in Multi-Robot System Based on a Team Competition Model.

In recent years, it is a trend to integrate the ideas in game theory into the research of multi-robot system. In this paper, a team-competition model is proposed to solve a dynamic multi-robot task allocation problem. The allocation problem asks how to assign tasks to robots such that the most suitable robot is selected to execute the most appropriate task, which arises in many real-life applications. To be specific, we study multi-round team competitions between two teams, where each team selects one of its players simultaneously in each round and each player can play at most once, which defines an extensive-form game with perfect recall. We also study a common variant where one team always selects its player before the other team in each round. Regarding the robots as the players in the first team and the tasks as the players in the second team, the sub-game perfect strategy of the first team computed via solving the team competition gives us a solution for allocating the tasks to the robots—it specifies how to select the robot (according to some probability distribution if the two teams move simultaneously) to execute the upcoming task in each round, based on the results of the matches in the previous rounds. Throughout this paper, many properties of the sub-game perfect equilibria of the team competition game are proved. We first show that uniformly random strategy is a sub-game perfect equilibrium strategy for both teams when there are no redundant players. Secondly, a team can safely abandon its weak players if it has redundant players and the strength of players is transitive. We then focus on the more interesting case where there are redundant players and the strength of players is not transitive. In this case, we obtain several counterintuitive results. For example, a player might help improve the payoff of its team, even if it is dominated by the entire other team. We also study the extent to which the dominated players can increase the payoff. Very similar results hold for the aforementioned variant where the two teams take actions in turn.

DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)

Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.

Towards a formal analysis of the multi-robot task allocation problem using set theory

Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.

A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking

In a multi-robot operation, multi-tasking resources are expected to simultaneously perform multiple tasks, thus, reducing the overall time/energy requirement of the operation. This paper presents a task allocation framework named Rostam that efficiently utilizes multi-tasking capable robots. Rostam uses a task clustering mechanism to form robot specific task maps. The customized maps identify tasks that can be multi-tasked by individual robots and mark them for simultaneous execution. The framework then uses an Evolutionary Algorithm along with the customized maps to make quality task allocations. The most prominent contribution of this work is Rostam's flexible design which enables it to handle a range of task allocation scenarios seamlessly. Rostam's performance is evaluated against an auction-based scheme; the results demonstrate its effective use of multi-tasking robots. The paper also demonstrates Rostam's flexibility towards a number of MRTA scenarios through a case study.

End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System

In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot’s steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index (TAI), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots.

A scheduling method for multi-robot assembly of aircraft structures with soft task precedence constraints

The use of multiple cooperating robotic manipulators to assemble large aircraft structures entails the scheduling of many discrete tasks such as drilling holes and installing fasteners . Since the tasks have different tool requirements, it is desirable to minimize tool changes that incur significant time costs. We approach this problem as a multi-robot task allocation problem with precedence constraints, where the constraints are loosely enforced in terms of prioritizing the tasks to avoid unnecessary tool changes. To avoid the computational burden of searching over all possible task prioritization options, our main contribution is to develop a two-step, data-driven approach to automatically select suitable precedence relations. The first step is to adapt an iterative auction-based method to encode the precedence relations using scheduling heuristics. The second step is to develop a robust machine learning method to generate policies for automatically selecting efficient scheduling heuristics based on the problem characteristics. Experimental results show that the top performing heuristics yield schedules that are more efficient than those of a baseline partition-based scheduler by almost 17%–19%, depending on the robot failure profiles. The learned policies are also able to select heuristics that perform better than greedy selection without incurring additional computational costs. • Multi-robot assembly requires scheduling a large number of tasks with tool changes. • We model the tool change requirements as soft task precedence constraints. • We propose a two-step approach for robust task scheduling. • Our approach consists of prioritized iterative auction followed by machine learning. • Results show improvements over a scheduler that does not model the tool changes.

Energy-aware task allocation strategy for multi robot system

ABSTRACT This paper proposes a distributed energy management task allocation strategy for a multi-robot system (MRS) involved in goods transportation. The main goal in such type of application is maximizing the productivity by transporting the maximum of goods. However, an important aspect of the optimal use of an MRS is an efficient energy management. The aim of this work is to develop a distributed cooperative strategy that considers energy management in task allocation. For this purpose, an Iterative Local Search-based (ILS) metaheuristic and a novel heuristic-based utility function are developed to solve the multi-robot task allocation problem. The proposed strategy uses the MRS as a population of individuals to solve the task allocation in a parallel distributed manner. Each robot runs the ILS-based algorithm and provides its solution for the task allocation, due to the fact that some of ILS parameters are randomly chosen. Thus, this strategy combines the exploration advantage of the population-based metaheuristics and the exploitation advantage and easy implementation on embedded systems (e.g., MRS) of the single-point-search metaheuristics. The proposed strategy is evaluated in a simulated environment and compared to three heuristics, in terms of the average consumed energy, the productivity of the system, and productivity per energy unit consumed.

A multi-robot task allocation algorithm based on universal gravity rules

A multi-robot task allocation algorithm based on universal gravity rules

Multi-robot Task Allocation System: Fuzzy Auction-Based and Adaptive Multi-threshold Approaches

Multi-robot Task Allocation System: Fuzzy Auction-Based and Adaptive Multi-threshold Approaches

Multi-robot Task Allocation Using Island Model Genetic Algorithm

Abstract Mobile robots have been used in several areas as agriculture, vigilance, and indoor transportation in factories and warehouses. The warehouses’ efficiency is directly related to transportation in the order picking process. Using mobile robots can improve transportation efficiency when associated with optimized task allocation. This paper shows a method, using the island model genetic algorithm, to allocate tasks with deadlines in multi-robot systems where mobile robots have different payloads and speed capacities. The approach’s performance was evaluated by varying parameters of the algorithm and the size of the task set, and the robot set involved in the allocation process. The performance criteria adopted were success rate in allocating all tasks, percentage of assigned tasks in non-complete solutions, and obtaining a complete solution. The results have shown that the method brings a more significant number of complete solutions in sets with a small and medium number of tasks. Besides that, the solutions obtained meets deadlines and show energy consumption reduction.

An effective method to use centralized Q-learning in multi-robot task allocation

An effective method to use centralized Q-learning in multi-robot task allocation

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.

A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem

Large-scale multi-robot task allocation (MRTA) problem is an important part of intelligent logistics scheduling. And the load capacity of robot and picking station are important factors affecting the MRTA problem. In this paper, the MRTA problem is built as a many-objective optimization model with four objectives, which takes the load capacity of single robot, single picking station, all robots and all picking stations into account. To solve the model, a hybrid many-objective competitive swarm optimization (HMaCSO) algorithm is designed. The novel selection method employing two different measurement mechanisms will form the mating selection operation. Then the population will be updated by employing the competitive swarm optimization strategy. Meanwhile, the environment selection will play a role in choosing the excellent solution. To prove the superiority of our approach, there are two series of experiments are carried out. On the one hand, our approach is compared with other five famous many-objective algorithms on benchmark problem. On the other hand, the involved algorithms are applied in solving large-scale MRTA problem. Simulation results prove that the performance of our approach is superior than other algorithms.

Task assignment strategy for multi-robot based on improved Grey Wolf Optimizer

Multi-robot task allocation (MRTA) is the basis of a multi-robot system to perform tasks automatically, which directly affects the execution efficiency of the whole system. A distributed cooperative task allocation strategy based on the algorithm of the improved Grey Wolf Optimizer (IGWO) was proposed to quickly and effectively plan the cooperative task path with a large number of working task points. The MRTA problem was transformed into multiple traveling salesman problems (MTSPs), and the task target points were clustered by the K-means clustering algorithm and divided into several traveling salesman problems (TSPs). The Grey Wolf Optimizer (GWO) was improved by the Kent chaotic algorithm to initialize the population and enhance the diversity of the population. Furthermore, an adaptive adjustment strategy of the control parameter $$\vec {a}$$ was proposed to balance exploration and exploitation. The individual speed and position updates in PSO were introduced to enable the gray wolf individual to preserve its optimal location information and accelerate the convergence speed. The IGWO was used to solve the optimal solutions to multiple TSP problems. Finally, the optimal solution space was integrated to get the optimal solution of MTSP, and 16 international classical test functions simulated the IGWO. The results showed that the IGWO algorithm has faster convergence speed and higher accuracy. The task allocation strategy is reasonable, with roughly equal path length, small planning cost, fast convergence speed, and excellent stability.

Development of a task-oriented, auction-based task allocation framework for a heterogeneous multirobot system

A multirobot system has cooperative team of robots designed to enhance efficiency of its operations. One of the critically investigated problems of multirobot system is the multirobot task allocation (MRTA) issue. The main objective of MRTA is to assign tasks to the most suitable robot based on its functions and capability as well as availability. In this paper, a task-oriented, auction-based task allocation framework is presented and tested through simulations and real-world experiments. The developed framework consists of a novel heuristic-based task allocation algorithm and communication module. It is implemented in a multirobot system, allowing tasks to be dynamically assigned to the robots as they achieve given tasks. The implemented framework shows robustness in its flexibility to the task and environment requirements such as resource and energy requirements and size of the environment. The framework involved a task allocation algorithm, which consists of bid generation and bid selection process, and a TCP/IP-based client-server communication module. The results from both simulations and real-world experiments matched, producing optimum results in task allocation.