Multi-robot Task Allocation Problem Research Articles

Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform

Research investigations in the realm of micro-robotics often center around strategies addressing the multi-robot task allocation (MRTA) problem. Our contribution delves into the collaborative dynamics of micro-robots deployed in targeted hostile environments. Employing advanced algorithms, these robots play a crucial role in enhancing and streamlining operations within sensitive areas. We adopt a tailored GREEDY approach, strategically adjusting weight parameters in a multi-objective function that serves as a cost metric. The objective function, designed for optimization purposes, aggregates the cost functions of all agents involved. Our evaluation meticulously examines the MRTA efficiency for each micro-robot, considering dependencies on factors such as radio connectivity, available energy, and the absolute and relative availability of agents. The central focus is on validating the positive trend associated with an increasing number of agents constituting the cluster. Our methodology introduces a trio of micro-robots, unveiling a flexible strategy aimed at detecting individuals at risk in demanding environments. Each micro-robot within the cluster is equipped with logic that ensures compatibility and cooperation, enabling them to effectively execute assigned missions. The implementation of MRTA-based collaboration algorithms serves as an adaptive strategy, optimizing agents' mobility based on specific criteria related to the characteristics of the target site.<p class="JAMRISAbstract"> </p>

An indicator-based evolutionary algorithm with adaptive archive update cycle for multi-objective multi-robot task allocation

The multi-robot task allocation problem has garnered significant attention in research and development. This paper addresses the multi-robot task allocation problem by introducing a unified model that seamlessly transforms into four popular mathematical models through simple parameter adjustments. We propose an efficient indicator-based multi-objective evolutionary algorithm with a hybrid encoding scheme for task execution order and robot starting point information. The algorithm employs the hypervolume indicator for environmental selection to enhance convergence and the modified crowding distance for archive updates to promote diversity. Additionally, an adaptive archive update mechanism is designed for time efficiency. In experiments comparing our proposed algorithm with 8 state-of-the-art algorithms on 18 randomly generated instances of various sizes, along with 4 real-world problems from the TSPLIB benchmark, our algorithm consistently outperformed the comparison algorithms in five key indicators. These results underscore the effectiveness of our approach in addressing real-world multi-robot task allocation problems.

Heuristic reoptimization of time‐extended multi‐robot task allocation problems

AbstractProviding high quality solutions is crucial when solving NP‐hard time‐extended multi‐robot task allocation (MRTA) problems. Reoptimization, that is, the concept of making use of a known solution to an optimization problem instance when the solution to a similar problem instance is sought, is a promising and rather new research field in this application domain. However, so far no approximative time‐extended MRTA solution approaches exist for which guarantees on the resulting solution's quality can be given. We investigate the reoptimization problems of inserting as well as deleting a task to/from a time‐extended MRTA problem instance. For both problems, we can give performance guarantees in the form of an upper bound of 2 on the resulting approximation ratio for all heuristics fulfilling a mild assumption. We furthermore introduce specific solution heuristics and prove that smaller and tight upper bounds on the approximation ratio can be given for these heuristics if only temporal unconstrained tasks and homogeneous groups of robots are considered. A conclusory evaluation of the reoptimization heuristic demonstrates a near‐to‐optimal performance in application.

Probability-Tuned Market-Based Allocations for UAV Swarms Under Unreliable Observations.

Unmanned aerial vehicle (UAV) swarms are becoming increasingly attractive as highly integrated miniature sensors and processors deliver extraordinary performance. The employment of UAV swarms on complex real-life tasks has motivated exploration on allocation problems involving multiple UAVs, complex constraints, and multiple tasks with coupling relationships. Such problems have been summarized domain independently as multirobot task allocation problems with temporal and ordering constraints (MRTA/TOC). The majority of MRTA/TOC works have hitherto focused on deterministic settings, while their stochastic counterparts are sparsely explored. In this article, allocation problems incorporating classification uncertainty of targets and soft ordering constraints of tasks are considered. To address such problems, a novel market-based allocation algorithm, the probability-tuned market-based allocation (PTMA), is proposed. PTMA consists of iterations between two phases: 1) the first phase updates local perception of global situational awareness and 2) the second phase is a market-based allocation scheme with embedded artificial randomness to locally generate allocation results. Under reasonable assumptions on classification uncertainty, the proposed PTMA algorithm is proven to guarantee a superior performance compared with conventional auctions, verifying the feasibility of using randomness to counter randomness theoretically. Three groups of numerical experiments have been conducted to assess the performance of the proposed PTMA. The first group of experiments compares PTMA with the consensus-based auction algorithm (CBAA), the chance-constrained CBAA, and the PTMA-I (specialization of PTMA with the Identity matrix), on simulated task scenarios with varying degrees of classification uncertainty. The second group evaluates the stability of PTMA. The third group tests the scalability of PTMA on expanded task scenarios. Simulation results demonstrate a satisfactory performance in effectiveness, robustness, stability, and scalability of the proposed PTMA.

A coarse- and fine-grained niching-based differential evolution for multimodal optimization problems and its application in multirobot task allocation

Multimodal optimization problems (MOPs) need to locate the global optima as many as possible and refine the accuracy of the identified optima as high as possible. How to divide niches reasonably and search solutions effectively are two difficult problems facing MOPs. In this paper, a coarse- and fine-grained niching-based differential evolution (CFNDE) algorithm with micro-search strategy is proposed to divide population reasonably and search the optima effectively, which mainly includes four contributions. Firstly, a framework of coarse- and fine-grained niching (CFN) strategy is proposed, where the coarse-grained clustering method provides the information of clusters to the fine-grained clustering method to accurately divide the population into niches. Secondly, a micro-search-based mutation (MSM) strategy is proposed to accelerate the convergence of population, which searches for the optimal solution in the range between the current individual and the nearest individual. Thirdly, a multi-level local search (MLLS) strategy that incorporates the cluster-level and population-level search mechanisms is proposed to refine the accuracy of solutions. Finally, a stagnation detection and re-search (SDR) strategy is proposed to re-search the local optima in evolution. We compare CFNDE with the-state-of-art multimodal optimization algorithms on the widely used benchmark CEC’2013. The experimental results show that CFNDE performs better than or similar to some recently proposed algorithms in 17 out of 20 tested functions. Besides, the proposed CFNDE is also applied to the multirobot task allocation problems and CFNDE provides multiple completely different optimal scheduling schemes to deal with emergencies.

A Reconfiguration Method for Muti-Robot Monitoring Patrols

This paper addresses the problem of multi-robot task allocation and trajectory planning in industrial environments. The objective is to optimize the overall cost of robot surveillance patrols in a dynamic high-risk environment. In this context, a hybrid beam search based approach is proposed to plan the patrol trajectories iteratively to accommodate environmental changes under some functional and operational constraints. Moreover, a real-time based system is introduced for remotely monitoring dynamic surveillance missions with automated mobile agents. Finally, a case study is detailed to show the efficiency of our approach in the case of the industrial port area of Fos-sur-Mer city in France.

Optimization techniques for Multi-Robot Task Allocation problems: Review on the state-of-the-art

In the last years, Multi-Robot Systems (MRS) have experienced considerable recognition due to various possible real-world applications. Multi-Robot Task Allocation (MRTA) is among the most interesting MRS problems. This problem concerns the situation when a set of given tasks must be performed by a team of mobile robots with the intention of optimizing an objective function (e.g., minimizing the mission time). This paper aims to present MRTA applications and categorizes methods into market-based, behavior-based, and optimization-based approaches. The paper focus on the latter and review several works in order to point out their advantages and limitations and to identify possible future research opportunities. Furthermore, a statistical analysis is provided to identify the most used methods and the evolution of the topic over the years.

Solving the Multi-robot task allocation with functional tasks based on a hyper-heuristic algorithm

Multi-robot task allocation (MRTA) problem is a classical problem in multi-robot systems. The most common assumption in MRTA is that all the tasks need to be completed with the time cost as small as possible. It is worth noting that some tasks may be optional in some real-world situations, i.e., the robots do not necessarily need to complete all tasks. These optional tasks do not limit the achievement of the goal, but the completion of these tasks will lead to some functional effects (e.g., making other tasks easier to be completed). Therefore, these optional tasks can be called “functional task”. Different from functional tasks, if a task must be completed, this task can be called “compulsory task”. In this paper, we study the problem where the robots need to minimize the time cost of completing all compulsory tasks and can selectively complete some functional tasks. The existence of the functional tasks greatly increases the solution space of MRTA, because the functional tasks should be firstly suitably selected and then suitably allocated. Existing related algorithms are usually based on the assumption that all tasks must be allocated. Thus, these algorithms cannot suitably deal with functional tasks. We analyze the characteristics of this problem and present a new hyper-heuristic algorithm. The low-level heuristic (LLH) in hyper-heuristic is designed to score the functional tasks by using influence diffusion model. The high-level strategy (HLS) seeks the optimal values of the key parameters in the influence diffusion model based on particle swarm optimization (PSO). Extensive simulated experiments are presented to comprehensively analyze the proposed algorithm. The proposed hyper-heuristic is compared with greedy algorithm and two meta-heuristic algorithms. Based on the simulated data, it is known that the hyper-heuristic algorithm can outperform the benchmark algorithms especially when the number of functional tasks is large.

A novel multimodal multi-objective optimization algorithm for multi-robot task allocation

Multi-robot task allocation (MRTA) is widely used in various fields and plays an important role in some complex task environments due to its ability to distribute parallel processing tasks. However, the multi-robot cooperative system is susceptible to actual environments or preferences of decision-makers. Therefore, providing enough solutions/schemes in the MRTA is important. To improve the reliability and feasibility of obtained solution set, an improved multimodal multi-objective differential evolution algorithm hybrid with a simulated annealing algorithm (IMMODE-SA) is proposed to solve MRTA problems in this study. In the proposed IMMODE-SA, a novel population initialization method is used to improve the population quality, and a redundant solution deletion method is employed to delete redundant solutions during the search process. Moreover, a simulated annealing algorithm is utilized to improve the exploitation capability in the last generation of evolutionary process. To verify the performance of the proposed algorithm, extensive simulation experiments are conducted on three MRTA instances. Experimental results show that the proposed algorithm performs better than other competitors on MRTA instances in terms of Hypervolume (HV). Also, the validity of the proposed algorithm is demonstrated via three experiments and experimental analysis results indicate that the IMMODE-SA can provide more equivalent optimal schemes to decision makers. Finally, it is crucial to solve MRTA problems with time window constraints.

Strengthening evolution-based differential evolution with prediction strategy for multimodal optimization and its application in multi-robot task allocation

Many real-world problems can be considered multimodal optimization problems (MMOPs), which require locating as many global optima as possible and refining the accuracy of the found optima as high as possible. However, there are some issues with existing algorithms for solving MMOPs. For instance, most of the existing methods adopt the greedy selection strategy to select offspring, which may lead some individuals to fall into local optima and the repetitive evaluations for these local optima will exhaust many fitness evaluations (FEs). Moreover, many MMOPs tend to be expensive to evaluate, and the rational allocation of evaluation resources to better deal with MMOPs is a critical challenge within a limited number of FEs. How to allocate FEs reasonably in a whole evolution and how to avoid individuals becoming trapped in local optima are two key problems in solving MMOPs. Therefore, this paper proposes a strengthening evolution-based differential evolution with prediction strategy (SEDE-PS) for solving MMOPs and verifies its performance in a multirobot task allocation (MRTA) problem, which has the following three contributions. First, a neighbour-based evolution prediction (NEP) strategy is proposed to predict the position of individuals in the next generation by using the historical information of individuals as much as possible. Second, a prediction-based mutation (PM) strategy is introduced to accelerate convergence by combining it with the NEP strategy. Third, a strengthening evolution (SE) strategy is proposed to select inferior individuals to evolve them unconditionally several times and make them approach global optima or jump out of local optima. We compare the SEDE-PS with state-of-the-art multimodal optimization algorithms on the widely used CEC’2013 benchmark. The experimental results show that SEDE-PS performs better than, or is competitive with these compared algorithms. Moreover, SEDE-PS is applied to a real-world MRTA problem to further verify the effectiveness of SEDE-PS.

Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks

In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach.

Market Approaches to the Multi-Robot Task Allocation Problem: a Survey

Market Approaches to the Multi-Robot Task Allocation Problem: a Survey

Optimizing Parallel Task Execution for Multi-Agent Mission Planning

Multi-agent systems have received a tremendous amount of attention in many areas of research and industry, especially in robotics and computer science. With the increased number of agents in missions, the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates numerous problem dimensions, and it aims at providing formulations and solutions to various problem configurations, i.e., complex multi-agent missions. One dimension of the MRTA problem has not caught much of the research attention. In particular, problem configurations including Multi-Task (MT) robots have been neglected. However, the increase in computational power, in robotic systems, has allowed the utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex robotic missions; however, it came at the cost of increased problem complexity. Our contribution to the aforementioned domain can be grouped into three categories. First, we model the problem using two different approaches, Integer Linear Programming and Constraint Programming. With these models, we aim at filling the gap in the literature related to the formal definition of MT robot problem configuration. Second, we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of parallel task execution. This distinction allows the modeling of a wider range of missions while exploiting possible parallel task execution. Finally, we provide a comprehensive performance analysis of both models, by implementing and validating them in CPLEX and CP Optimizer on the set of problems. Each problem consists of the same set of test instances gradually increasing in complexity, while the percentage of virtual tasks in each problem is different. The analysis of the results includes exploration of the scalability of both models and solvers, the effect of virtual tasks on the solvers’ performance, and overall solution quality.

Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms

Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.

Multi-robot task allocation clustering based on game theory

A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances.

Multirobot Adaptive Task Allocation of Intelligent Warehouse Based on Evolutionary Strategy

To solve the dynamic and real-time problem of multirobot task allocation in intelligent warehouse system under parts-to-picker mode, this paper presents a combined solution based on adaptive task pool strategy and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm. In the first stage of the solution, a variable task pool is used to store dynamically added tasks, which can dynamically divide continuous and large-scale task allocation problems into small-scale subproblems to solve them to meet dynamic requirements. And an adaptive control strategy is used to automatically adjust the total number of tasks in the task pool to achieve a trade-off among throughput, energy consumption, and waiting time, which has better adaptability than manually adjusting the size of the task pool. In the second stage of the solution, when the task pool is full, tasks in the task pool will be assigned to robots. For the task allocation problem, this paper regards it as an optimization problem and uses the CMA-ES algorithm to find the optimal task assignment solution for all the robots. By comparing with fixed threshold method under 56 different task pool sizes, the experimental results show that the throughput can be close to reaching the optimal level, and the average distance traveled by robots to handle each unit is lower using adaptive threshold method; so, adaptive task pool solution has better adaptability and can find the optimal task pool size by itself. This method can satisfy the dynamic and real-time requirements and can be effectively applied to the intelligent warehouse system.

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.

Multi-robot task allocation in e-commerce RMFS based on deep reinforcement learning.

A Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order fulfillment system where multiple robots coordinate to complete a large number of order picking tasks. The multi-robot task allocation (MRTA) problem in RMFS is complex and dynamic, and it cannot be well solved by traditional MRTA methods. This paper proposes a task allocation method for multiple mobile robots based on multi-agent deep reinforcement learning, which not only has the advantage of reinforcement learning in dealing with dynamic environment but also can solve the task allocation problem of large state space and high complexity utilizing deep learning. First, a multi-agent framework based on cooperative structure is proposed according to the characteristics of RMFS. Then, a multi agent task allocation model is constructed based on Markov Decision Process. In order to avoid inconsistent information among agents and improve the convergence speed of traditional Deep Q Network (DQN), an improved DQN algorithm based on a shared utilitarian selection mechanism and priority empirical sample sampling is proposed to solve the task allocation model. Simulation results show that the task allocation algorithm based on deep reinforcement learning is more efficient than that based on a market mechanism, and the convergence speed of the improved DQN algorithm is much faster than that of the original DQN algorithm.

Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms

The paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.

Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints

This paper tackles a class of multi-robot task allocation (MRTA) problems called “Single-Task Robots and Single-Robot Tasks” or SR–ST problems, subject to the following additional characteristics: tasks with deadlines, tasks that are generated during the mission, and robots with range and payload constraints (thus requiring multiple tours per robot). While these characteristics are typical of various disaster response and commercial applications, there is a lack of online MRTA solutions to address them. To solve this class of complex MRTA problems, an efficient online method (which is also suitable for decentralized deployment) is developed based on the construction and weighted matching of bipartite graphs. An exact integer linear programming (ILP) formulation of this class of MRTA problems is also developed, the solution of which serves both as an offline MRTA approach and as a provably optimal benchmark against which the online method is compared. The new methods are applied to a flood response problem where multiple unmanned aerial vehicles must respond to victims spread out over a large area. The results show that the new online algorithm closely trails the offline ILP method in terms of task completion performance, while being >103 times more computationally efficient compared to the ILP method. Dedicated case studies provide further insights into the favorable scalability of the online method with an increasing number of UAVs — offering up to 46% higher task completion compared to a random walk baseline in huge 1000-task problems. Lastly, application to a slightly different class of SR–ST problems and comparison of the ensuing results with that of corresponding state-of-the-art methods demonstrate the potential wider applicability of the proposed online MRTA method.