Task Allocation Problem Research Articles

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.

A Task Allocation Algorithm for Coal Mine Mobile Crowd Sensing Based on Weighted Undirected Graph

Existing applications of mining intelligent mobile terminals are all for the sensing of individual terminals, without considering the group attributes of terminal carriers and the cooperation opportunities brought by terminal movement, which is unable to complete large-scale and complex sensing tasks. In this article, mobile crowd sensing and mine safety monitoring are combined to construct the mobile crowd sensing network for coal mine. Aiming at the task allocation problem, a task allocation mechanism based on a weighted undirected graph is proposed. First, considering the similarity of task completion location and time and quality of data collected, the reputation evaluation model of miners is designed. In order to optimize the reputation of miners participating in the task, an emergency task allocation algorithm based on the weighted undirected graph is proposed. Second, based on the fatigue degree of miners and the residual energy of intelligent mobile terminals, the miner status evaluation model is constructed, which is combined with the reputation model to design a nonemergency task allocation algorithm, aiming at further optimizing the task allocation results. The simulation results show that the proposed algorithm has better performance in allocation time for sensing task, task allocation success rate, average reputation, and status value of miners.

Multi-robot exploration in task allocation problem

Multi-robot exploration in task allocation problem

Task coalition formation for Mobile CrowdSensing based on workers' routes preferences

In this paper, we address the problem of task allocation in Mobile Crowdsensing (MCS) by means of forming tasks publisher coalition taking into consideration workers' route preferences. In prior research works, only one of the MCS components (either task publishers, contributors or platform) dominates the task allocation process. Currently, other approaches have investigated tasks coalition based on their geographical locations. In this paper, we address the aforementioned problem by proposing a new scheme taking into accounts the interest of all the participating parties. To this end, our approach provides (1) strategies for selecting the best routes for workers with better long-term earnings and (2) task publishers' coalition formation based on worker's routes selection and preferences regardless of the order of individual execution of tasks. We proposed two models for the coalition formation: i) a centralized approach to solve the problem of the coalition formation together with the route selection, and ii) a simplified heuristic version that first determines disjoint tasks' coalitions based on the preferred routes selected by workers, then, MCS platform sorts the coalitions with best quality of information and selects the best routes for each ordered coalition. Simulation results with real data-set show that the coalition of task publishers together with the distributed route selection per worker does guarantee the quality of information satisfaction of the sensing tasks while enhancing the worker payment.

TAPP: DNN Training for Task Allocation through Pipeline Parallelism Based on Distributed Deep Reinforcement Learning

The rapid development of artificial intelligence technology has made deep neural networks (DNNs) widely used in various fields. DNNs have been continuously growing in order to improve the accuracy and quality of the models. Moreover, traditional data/model parallelism is hard to expand due to communication bottlenecks and hardware efficiency issues. However, pipeline parallelism trains multiple batches, reducing training overheads, so that it can achieve better acceleration effect. Considering the complexity of solving the pipeline parallel task allocation problem in heterogeneous computing resources, in this paper, a task allocation in pipeline parallelism (TAPP) based on deep reinforcement learning, is proposed. In TAPP, the predictive network is trained by a policy gradient until it obtains the optimal pipeline parallel task allocation scheme and speeds up the model training. Experimental results show that, on average, the single-step training time of TAPP is decreased by 1.37 times and the proportion of communication time is reduced by 48.92%, compared with the data parallelism, bulk synchronous parallel (BSP).

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.

Reliability analysis in grid system considering load

This paper discusses the load-balanced task allocation problem in the grid transaction processing system to maximize reliability. Finding the solution of optimal load-balanced task allocation is known to be an NP-hard. This paper presents a load-balanced task allocation based cuckoo search-ant colony optimization (LBTA_CSACO) method for this problem. The LBTA_CSACO is based on the cooperative behavior of cuckoo search and ant colony optimization to nd a collection of task allocation solutions. This paper also models the reliability of the system with the help of the availability of the system. The motive of this work is to maximize the reliability of the system. After the simulation, six existing algorithms are taken for the comparison of the results; Honey Bee Optimization (HBO), Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized respectively.

A Novel Sensor Task Allocation Method Based on Quantum Elite Shuffled Frog Leaping Algorithm in IWSNs

Maximizing the efficiency of sensor task allocation has long been a question of great interest in industrial wireless sensor networks (IWSNs). In IWSNs, different tasks performed by different sensors produce varied benefit values. The purpose of this paper is to obtain the optimal task allocation scheme of IWSNs. Therefore, we design a sensor task allocation model, and propose a quantum elite shuffled frog leaping algorithm (QES-FLA) for optimizing the task allocation in IWSNs. The proposed algorithm combines the quantum operator and elite operator to achieve better performance. By using the concept of quantum probability amplitude and quantum revolving gate, the algorithm can search the solution space in parallel, thus enhancing the efficiency of solving the task allocation problem in IWSNs. In addition, the elite operator keeps the optimal individual in the population, which ensures the performance of the algorithm. Subsequently, the proposed algorithm is compared with two other popular heuristic algorithms to make the conclusion more convincing. According to the simulation results, the algorithm we proposed has higher task benefits and better performance, thus it successfully solves the sensor task allocation problem in IWSNs.

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.

A Genetic Algorithm for Task Allocation Problem in the Internet of Things

In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonical definition for network lifetime in the IoT is to increase the period of cooperation between objects to carry out all the assigned tasks. The main contribution in this paper is to address the problem of task allocation in the IoT as an optimization problem with a lifetime-aware model. A genetic algorithm is proposed as a task allocation protocol. For the proposed algorithm, a problem-tailored individual representation and a modified uniform crossover are designed. Further, the individual initialization and perturbation operators (crossover and mutation) are designed so as to remedy the infeasibility of any solution located or reached by the proposed genetic algorithm. The results showed reasonable performance for the proposed genetic-based task allocation protocol. Further, the results prove the necessity for designing problem-specific operators instead of adopting the canonical counterparts.

Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing

Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers' mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers' competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit.

Distributed task allocation with critical tasks and limited capacity

AbstractThis paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

Event-MILP-Based Task Allocation for Heterogeneous Robotic Sensor Network for Thermosolar Plants

In this paper, an event-Mixed Integer Linear Programming (MILP)-based algorithm is proposed to solve the task allocation problem in a Robotic Sensor Network (RSN). A fleet of two types of vehicles is considered, giving, as a result, a heterogeneous configuration of the network, since each type of vehicle has a nominal velocity and a set of allowed paths to go. The algorithm can be applied to the distributed estimation of the solar irradiance on a parabolic trough thermosolar power plant which can be used to increase the global efficiency of the plant. A simulation environment has been built to test the proposed algorithm, taking into account the behavior of the vehicles and the structure of the solar plant. The algorithm has been compared with traditional methods such as the Optimal Assignment Problem (OAP) using a set of indexes that have been defined to this purpose.

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 Clustering-Based Coverage Path Planning Method for Autonomous Heterogeneous UAVs

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

Anytime and Efficient Multi-agent Coordination for Disaster Response

The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (text {CFLA}2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in text {CFLA}2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. text {CFLA}2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.

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.

Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem.

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.