Multi-robot Task Allocation Problem Research Articles

A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be $\mathcal {NP}$ -hard even with small numbers of robots and tasks. The field of survivors’ search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.

A Multi-Level Architecture for Solving the Multi-Robot Task Allocation Problem Using a Market-Based Approach

A Multi-Level Architecture for Solving the Multi-Robot Task Allocation Problem Using a Market-Based Approach

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

The multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function, subject to some constraints: e.g. allocate the maximum number of tasks, minimize the distances travelled by robots, etc. In this paper, we propose some objective functions, and our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method (based on parallel distributed guided genetic algorithms) and compare it to two state-of-the-art algorithms. Performed simulations and obtained results show the effectiveness and scalability of our solution, even with a large number of robots and tasks.

Multi-Robot Dynamic Task Allocation for Exploration and Destruction

Environmental exploration is one of the common tasks in the robotic domain which is also known as foraging. In comparison with the typical foraging tasks, our work focuses on the Multi-Robot Task Allocation (MRTA) problem in the exploration and destruction domain, where a team of robots is required to cooperatively search for targets hidden in the environment and attempt to destroy them. As usual, robots have the prior knowledge about the suspicious locations they need to explore but they don’t know the distribution of interested targets. So the destruction task is dynamically generated along with the execution of exploration task. Each robot has different strike ability and each target has uncertain anti-strike ability, which means either the robot or target is likely to be damaged in the destruction task according to that whose ability is higher. The above setting significantly increases the complexity of exploration and destruction problem. The auction-based approach, vacancy chain approach and a deep Q-learning approach based on strategy-level selection are employed in this paper to deal with this problem. A new simulation system based on Robot Operating System and Gazebo is specially built for this MRTA problem. Subsequently, extensive simulation results are provided to show that all proposed approaches are able to solve the MRTA problem in exploration and destruction domain. In addition, experimental results are further analyzed to show that each method has its own advantages and disadvantages.

ACD3GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization

PurposeThe multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD3GPSO.Design/methodology/approachThis approach is made out of two phases: phase I groups the tasks into clusters using the ACD3GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD3GPSO for better results. First, ACD3GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid.FindingsComputational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time.Practical implicationsThe proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks.Originality/valueIn this methodology, owing to the ACD3GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

Robots in the Huddle: Upfront Computation to Reduce Global Communication at Run Time in Multirobot Task Allocation

In this article, we study multirobot task allocation problems where task costs vary. The variation may be, for example, due to the revelation of new information or other dynamic circumstances. As robots update their cost estimates, typically they will update task assignments to reflect the new information using additional communication and computation. In dynamic settings, the robots are continually repairing the optimality of the system's task assignments, which can incur substantial communication and computation. We investigate how one can reduce communication and centralized computation expense during execution by using a prior model of how costs may change and performing upfront computation of possible robot–task assignments. First, we develop an algorithm that partitions a team of robots into several independent subteams that are able to maintain global optimality by communicating entirely amongst themselves. Second, we propose a method for computing the worst-case cost suboptimality if robots persist with the initial assignment and perform no further communication and computation. Finally, we introduce an algorithm to assess whether cost changes affect the optimality of the current assignment through a succession of local communication exchanges. Experimental results show that the proposed methods are helpful in reducing the degree of centralization needed by a multirobot system (e.g., the third method gave at least 45% reduction of global communication across all scenarios studied). The methods are valuable in transitioning multirobot techniques, which have met with success in structured applications (such as factories and warehouses) to the broader, wilder world.

Distributed task allocation method based on self-awareness of autonomous robots

Multi-robot task allocation problem is an important research field in multi-robot systems. The multi-robot task allocation problem combined with affective computing is an interesting frontier problem. This paper proposes a distributed affective robot pursuit task allocation algorithm based on self-awareness, which combines cognitive intelligence with emotional intelligence to establish self-awareness for affective robots. This paper defines the cognitive mechanism, environmental detection mechanism and self-decision mechanism of affective robots, so that the self-aware affective robots can make decisions autonomously by grasping the information of itself and the environment. The experimental results show better efficiency of this task allocation algorithm at each task scale, and the allocation efficiency is gradually improved as the number of robot increases.

FA–QABC–MRTA: a solution for solving the multi-robot task allocation problem

The problem of task allocation in a multi-robot system is the situation where we have a set of tasks and a number of robots; then each task is assigned to the appropriate robots with the aim of optimizing some criteria subject to constraints, e.g., allocate the maximum number of tasks. We propose an effective solution to address this problem. It implements a two-stage methodology: first, a global allocation based of the well-known firefly algorithm, and then, a local allocation combining advantages of quantum genetic algorithms and artificial bee colony optimization. We compared our proposed solution to one solution from the state of the art. The simulation results show that our scheme significantly performs better than this solution. Our solution allocated $$100\%$$ of the tasks (in every configuration tried in the experiments) and enhanced the allocation time by $$75\%$$ .

A distributed method for dynamic multi-robot task allocation problems with critical time constraints

This paper considers the task allocation problems in a distributed multi-robot system under critical time constraints. Considering the requirement of distributed computing, many existing distributed heuristic task allocation approaches tend to trap in local optimal and cannot obtain high-quality solutions. For a dynamic task allocation problem in a multi-robot system, not only the task information and the robot state may be subject to changes, but also the network status. That is, robots that each robot can communicate with may change over time, and sometimes there may even be no robots that it can communicate with. To solve these problems, a dynamic grouping allocation method is proposed. It builds upon the state-of-the-art consensus-based auction algorithms, extending them in both task inclusion phase and consensus phase. First, a cluster-first strategy and a task inclusion procedure that can be easily applied to the task inclusion phase of the algorithms are proposed, so that the solution quality of each iteration of the algorithms are significantly improved with a reasonable amount of computation. In addition, to increase the exploration capabilities, a proportional selection method is used in the task inclusion procedure when it is likely to trap in a local optimal. Second, the block-information-sharing strategy is used to avoid the possible conflicts that dynamic changes may bring. Numerical simulations demonstrate that the proposed method can provide conflict-free solutions in dynamic environments and can achieve outstanding performance in comparison with the state-of-the-art algorithms.

Q-Learning Based Failure Detection and Self-Recovery Algorithm for Multi-Robot Domains

Task allocation is the essential part of multi-robot coordination researches and it plays a significant role to achieve desired system performance. Uncertainties in multi-robot systems’ working environment due to nature of them are the major hurdle for perfect coordination. When learning-based task allocation approaches are used, firstly robots learn about their working environment and then they benefit from their experiences in future task allocation process. These approaches provide useful solutions as long as environmental conditions remain unchanged. If permanent changes in environment characteristics or some failure in multi-robot system occur undesirably e.g. in disaster response which is a good example to represent such cases, the previously-learned information becomes invalid. At this point, the most important mission is to detect the failure and to recover the system initial learning state. For this purpose, Q-learning based failure detection and self-recovery algorithm is proposed in this study. According to this approach, multi-robot system checks whether these variations permanent, then recover the system to learning state if it is required. So, it provides dynamic task allocation procedure having great advantages against unforeseen situations. The experimental results verify that the proposed algorithm offer efficient solutions for multi-robot task allocation problem even in systemic failure cases. DOI: 10.5755/j01.eie.25.2.23197

Q-Learning Based Failure Detection and Self-Recovery Algorithm for Multi-Robot Domains

Task allocation is the essential part of multi-robot coordination researches and it plays a significant role to achieve desired system performance. Uncertainties in multi-robot systems’ working environment due to nature of them are the major hurdle for perfect coordination. When learning-based task allocation approaches are used, firstly robots learn about their working environment and then they benefit from their experiences in future task allocation process. These approaches provide useful solutions as long as environmental conditions remain unchanged. If permanent changes in environment characteristics or some failure in multi-robot system occur undesirably e.g. in disaster response which is a good example to represent such cases, the previously-learned information becomes invalid. At this point, the most important mission is to detect the failure and to recover the system initial learning state. For this purpose, Q-learning based failure detection and self-recovery algorithm is proposed in this study. According to this approach, multi-robot system checks whether these variations permanent, then recover the system to learning state if it is required. So, it provides dynamic task allocation procedure having great advantages against unforeseen situations. The experimental results verify that the proposed algorithm offer efficient solutions for multi-robot task allocation problem even in systemic failure cases.DOI: 10.5755/j01.eie.25.1.22728

Auctions for multi-robot task allocation in communication limited environments

We consider the problem of multi-robot task allocation using auctions, and study how lossy communication between the auctioneer and bidders affects solution quality. We demonstrate both analytically and experimentally that even though many auction algorithms have similar performance when communication is perfect, different auctions degrade in different ways as communication quality decreases from perfect to nonexistent. Thus, if a multi-robot system is expected to encounter lossy communication, then the auction algorithm that it uses for task allocation must be chosen carefully. We compare six auction algorithms including: standard implementations of the Sequential Auction, Parallel Auction, Combinatorial Auction; a generalization of the Prim Allocation Auction called G-Prim; and two multi-round variants of a Repeated Parallel Auction. Variants of these auctions are also considered in which award information from previous rounds is rebroadcast by the auctioneer during later rounds. We consider a variety of valuation functions used by the bidders, including: the total and maximum distance traveled (for distance based cost functions), the expected profit or cost to a robot (assuming robots’ task values are drawn from a random distribution). Different auctioneer objectives are also evaluated, and include: maximizing profit (max sum), minimizing cost (min sum), and minimizing the maximum distance traveled by any particular robot (min max). In addition to the cost value functions that are used, we are also interested in fleet performance statistics such as the expected robot utilization rate, and the expected number of items won by each robot. Experiments are performed both in simulation and on real AscTec Pelican quad-rotor aircraft. In simulation, each algorithm is considered across communication qualities ranging from perfect to nonexistent. For the case of the distance-based cost functions, the performance of the auctions is compared using two different communication models: (1) a Bernoulli model and (2) the Gilbert–Elliot model. The particular auction that performs the best changes based on the the reliability of the communication between the bidders and the auctioneer. We find that G-Prim and its repeated variant perform relatively well when communication is poor, and that re-sending winner data in later rounds is an easy way improve the performance of multi-round auctions, in general.

PSO-based Dynamic Distributed Algorithm for Automatic Task Clustering in a Robotic Swarm

The Multi-Robot Task Allocation (MRTA) problem has recently become a key research topic. Task allocation is the problem of mapping tasks to robots, such that the most appropriate robot is selected to perform the most fitting task, leading to all tasks being optimally accomplished. Expanding the number of tasks and robots may cause the collaboration among the robots to become tougher. Since this process requires high computational time, this paper describes a technique that reduces the size of the explored state space, by partitioning the tasks into clusters. In real-world problems, the absence of information regarding the number of clusters is ordinarily occurring. Hence, a dynamic clustering is auspicious for partitioning the tasks to an appropriate number of clusters. In this paper, we address the problem of MRTA by putting forward a new simple, automatic and efficient clustering algorithm of the robots’ tasks based on a dynamic distributed particle swarm optimization, namely, ACD2PSO. Our approach is made out of two stages: stage I groups the tasks into clusters using the dynamic distributed particle swarm optimization (D2PSO) algorithm and stage II allocates the robots to the clusters. The assignment of robots to the clusters is represented as multiple traveling salesman problems (MTSP). Computational experiments were carried out to prove the effectiveness of our approach in term of clustering time, cost, and the MRTA time, compared to the distributed particle swarm optimization (dPSO) and genetic algorithm (GA). Thanks to the D2PSO algorithm, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing the fast convergence of PSO.

Iterated Local Search for Time-extended Multi-robot Task Allocation with Spatio-temporal and Capacity Constraints

Abstract We propose a method for task allocation to multiple physical agents that works when tasks have temporal and spatial constraints and agents have different capacities. Assuming that the problem is over-constrained, we need to find allocations that maximize the number of tasks that can be done without violating any of the constraints. The contribution of this work is the study of a new multi-robot task allocation problem and the design and the experimental evaluation of our approach, an iterated local search that is suitable for time critical applications. We created test instances on which we experimentally show that our approach outperforms a state-of-the-art approach to a related problem. Our approach improves the baseline’s score on average by 2.35% and up to 10.53%, while responding in times shorter than the baseline’s, on average, 1.6 s and up to 5.5 s shorter. Furthermore, our approach is robust to run replication and is not very sensitive to parameters tuning.

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

A Comparative Study of Game-theoretical and Markov-chain-based Approaches to Division of Labour in a Robotic Swarm

This paper compares two swarm intelligence frameworks that we previously proposed for multi-robot task allocation problems: the game-theoretical approach based on anonymous hedonic games, called GRAPE, and the Markov-Chain-based approach under local information consistency assumption, called LICA-MC. We implement both frameworks into swarm distribution guidance problem, the objective of which is to distribute a swarm of robots into a set of tasks in proportion to the tasks’ demands, and then we perform extensive numerical experiments with various environmental settings. The statistical results show that LICA-MC provides excellent scalability regardless of the number of robots, whereas GRAPE is more efficient in terms of convergence time (especially when accommodating a moderate number of robots) as well as total travelling costs. Furthermore, this study investigates other implicit advantages of the frameworks such as mission suitability, additionally-built-in decision-making functions, and sensitivity to traffic congestion or robots’ mobility.

EXPERIENCED TASK-BASED MULTI ROBOT TASK ALLOCATION

In multi robot system applications, it is possible that the robots transform their past experiences into useful information which will be used for next task allocation processes by using learning-based task allocation mechanisms. The major disadvantages of multi-robot Q-learning algorithm are huge learning space and computational cost due to generalized state and joint action spaces of robots. In this study, experienced task-based multi robot task allocation approach is proposed. According to this approach, robots believe to be experienced about the tasks most frequently done. Robots prefer to do these tasks rather than the inexperienced ones. Then, robots refuse to execute inexperienced tasks over time. This means that the system has reduced learning space. The proposed approach plays a crucial role to achieve required system performance and provides effective solutions to learning space dimensions. The effectiveness of the proposed algorithm is demonstrated by simulations on multi-robot task allocation problem.

Multirobot task allocation based on an improved particle swarm optimization approach

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

DTTA - Distributed, Time-division Multiple Access based Task Allocation Framework for Swarm Robots

Swarm robotic systems, unlike traditional multi-robotic systems, deploy number of cost effective robots which can co-operate, aggregate to form patterns/formations and accomplish missions beyond the capabilities of individual robot. In the event of fire, mine collapse or disasters like earthquake, swarm of robots can enter the area, conduct rescue operations, collect images and convey locations of interest to the rescue team and enable them to plan their approach in advance. Task allocation among members of the swarm is a critical and challenging problem to be addressed. DTTA- a distributed, Time-division multiple access (TDMA) based task allocation framework is proposed for swarm of robots which can be utilised to solve any of the 8 different types of task allocation problem identified by Gerkey and Mataric´. DTTA is reactive and supports task migration via extended task assignments to complete the mission in case of failure of the assigned robot to complete the task. DTTA can be utilised for any kind of robot in land or for co-operative systems comprising of land robots and air-borne drones. Dependencies with other layers of the protocol stack were identified and a quantitative analysis of communication and computational complexity is provided. To our knowledge this is the first work to be reported on task allocation for clustered scalable networks suitable for handling all 8 types of multi-robot task allocation problem. Effectiveness and feasibility of deploying DTTA in real world scenarios is demonstrated by testing the framework for two diverse application scenarios.

Multi-Robot Allocation of Tasks with Temporal and Ordering Constraints

Task allocation is ubiquitous in computer science and robotics, yet some problems have received limited attention in the computer science and AI community. Specifically, we will focus on multi-robot task allocation problems when tasks have time windows or ordering constraints. We will outline the main lines ofresearch and open problems.