Task Allocation Problem Research Articles

A multi-objective teaching-learning-based optimizer for a cooperative task allocation problem of weeding robots and spraying drones

In recent years, both intelligent robots and drones have been widely used in agriculture. This paper studies a cooperative task allocation problem of weeding robots and spraying drones (WRSDCTA) with two objectives of minimizing the maximum completion time and minimizing the total residual herbicide. Based on the characteristics of the problem, a mathematical model is established. An effective multi-objective teaching-learning-based optimizer (MOTLBO) is proposed to solve this problem. At first, two heuristic initialization methods are designed to generate high quality initial population. And then, a multi-teacher strategy is presented when selecting teachers and students. For teachers, two heuristic search operators are designed to improve personal ability. For students, an improved OBX crossover operator is designed to learn from selected teachers. In addition, a multi-neighborhood search strategy is presented to further improve the local search capability of the algorithm. Finally, the proposed MOTLBO algorithm is compared with several state-of-the-art algorithms from close-related literature, and the results demonstrate the effectiveness and high performance of MOTLBO for solving the WRSDCTA problem.

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.

Online Planning for Autonomous Mobile Robots with Different Objectives in Warehouse Commissioning Task

Recently, multi-agent systems have become widespread as essential technologies for various practical problems. An essential problem in multi-agent systems is collaborative automating picking and delivery operations in warehouses. The warehouse commissioning task involves finding specified items in a warehouse and moving them to a specified location using robots. This task is defined as a spatial task-allocation problem (SPATAP) based on a Markov decision process (MDP). It is considered a decentralized multi-agent system rather than a system that manages and optimizes agents in a central manner. Existing research on SPATAP involving modeling the environment as a MDP and applying Monte Carlo tree searches has shown that this approach is efficient. However, there has not been sufficient research into scenarios in which all agents are provided a common plan despite the fact that their actions are decided independently. Thus, previous studies have not considered cooperative robot behaviors with different goals, and the problem where each robot has different goals has not been studied extensively. In terms of the cooperative element, the item exchange approach has not been considered effectively in previous studies. Therefore, in this paper, we focus on the problem of each robot being assigned a different task to optimize the percentage of picking and delivering items in time in social situations . We propose an action-planning method based on the Monte Carlo tree search and an item-exchange method between agents. We also generate a simulator to evaluate the proposed methods. The results of simulations demonstrate that the achievement rate is improved in small- and medium-sized warehouses. However, the achievement rate did not improve in large warehouses because the average distance from the depot to the items increased.

Multigene and Improved Anti-Collision RRT* Algorithms for Unmanned Aerial Vehicle Task Allocation and Route Planning in an Urban Air Mobility Scenario.

Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks' completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed.

An innovative method to solve the maintenance task allocation and packing problem

PurposeThis paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.Design/methodology/approachThe study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.FindingsThe solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.Originality/valueThis is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

Dynamic task allocation in multi autonomous underwater vehicle confrontational games with multi-objective evaluation model and particle swarm optimization algorithm

In this paper, a non-zero-sum game model based on a multi-objective evaluation model was adopted to solve the dynamic task allocation problem of underwater multiple Autonomous Underwater Vehicle (multi-AUV). By introducing a functional coordination mechanism, the high degree of coordination in practical confrontation scenarios was reflected, and the autonomous decision-making and task allocation process of multiple AUVs were clarified. Secondly, a multi-objective evaluation model including survival value, strike income, and ammunition loss was constructed, and weighted processing was performed on the multiple objectives using the Analytic Hierarchy Process (AHP) method, obtaining the benefits of each non-zero-sum strategy in dynamic games. In addition, a particle swarm optimization (PSO) algorithm that combines the theory of good point sets theory (G) and speed control factors (S) for improvement, called GSPSO, was used to find the optimal strategy in the game and allocate tasks. Finally, simulation analysis showed that the collaborative system with functional coordination mechanisms significantly improved the combat capabilities of underwater multiple AUVs. The multi-objective evaluation model combined with AHP can correctly and comprehensively evaluate the advantages and disadvantages of each strategy and correctly respond to changing confrontation tasks and decision preferences. The proposed algorithm improved the convergence speed and global search ability by enhancing the diversity of the population and the effectiveness of iterative solutions, ensuring real-time decision-making and task allocation in complex high-dimensional dynamic games.

Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers

The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms.

BDBM: A distributed network simplification method for solving task allocation problems

Existing task allocation algorithms often fail to consider communication load and do not meet the scalability requirements of large-scale systems in practical applications. Furthermore, many general network simplification algorithms are centralized and not designed for task allocation scenarios, thus losing their superiority when applied to solve task allocation problems in distributed systems. Inspired by recent network simplification algorithms, this paper introduces a novel network simplification algorithm called bid-based distributed broken-motifs (BDBM), which simplifies the communication network by reducing the number of closed-loop triangles. The BDBM algorithm keeps those communication edges that facilitate the deconfliction based on the initial bids, thus avoiding the surge in the number of convergence iterations caused by the simplification. In addition, CBBA is distributed and requires only two iterations to complete the communication network simplification. Theoretical analysis confirms that the simplified network of BDBM remains connected and can be used in combination with improved task allocation algorithms based on consensus-based bundle algorithm (CBBA). In terms of experiments, we conducted comprehensive experiments in different settings and found that the proposed algorithm does not lead to worse allocation solutions. The statistical results also show that BDBM outperforms state-of-the-art network simplification algorithms in terms of solving efficiency and scalability in assisting CBBA solve large-scale complex task allocation problems.

Efficient and Robust Multirobot Navigation and Task Allocation Using Soft Actor Critic

In this research, we present a novel solution to the problem of multi-robot task allocation (MRTA) by utilizing a variant of the state-of-art Deep Reinforcement Learning algorithm, Soft Actor-Critic (SAC) within Multirobot Navigation Systems(MRS). Our approach addresses the existing research gaps in MRTA for MRS, which have received limited attention and have proven challenging to solve using conventional methods. Through extensive simulation evaluations, we demonstrate that our system outperforms current state-of-the-art methods in terms of performance metrics. These findings highlight the effectiveness and superiority of our proposed approach in overcoming the inherent challenges of large-scale MRS. Additionally, we contribute to the field by proposing improvements to the Task Allocation Index (TAI) metric, enabling a more comprehensive evaluation of MRTA performance.

Emergence Mechanism of Lion Pride Cooperative Behaviours for Task Allocation Problem and its Implementation

Emergence Mechanism of Lion Pride Cooperative Behaviours for Task Allocation Problem and its Implementation

Emergence mechanism of lion pride cooperative behaviours for task allocation problem and its implementation

Emergence mechanism of lion pride cooperative behaviours for task allocation problem and its implementation

Cooperative task allocation with simultaneous arrival and resource constraint for multi-UAV using a genetic algorithm

In recent years, the application of multi-UAV cooperation systems has expanded across various domains. Enhancing the coordination performance of multi-UAV systems can be achieved through task allocation methods, typically relying on a hierarchical structure. This paper proposes a novel approach using a modified genetic algorithm (GA) to address the integrated task allocation and path planning problems for multi-UAV attacking multi-target. To create a more realistic mission scenario, multiple constraints, such as resource requirement and simultaneous target arrival, are considered. The modified GA incorporates tailored crossover and mutation operators that ensure compliance with the aforementioned constraints. Furthermore, an unlocking strategy is devised to prevent the occurrence of a chromosome deadlock condition, in which several UAVs become stuck in an infinite waiting state. Through simulation results, the modified GA is demonstrated to effectively delivers feasible solutions to the coupled task allocation and path planning problems, preserving the integrated nature of the optimization process. Monte Carlo simulations are conducted to highlight the superiority of the proposed method in comparison to conventional approaches.

An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario

With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations. The problem is formulated as a multi-objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two-stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem’s characteristics. Secondly, a two-stage search strategy is designed. In the first stage, a fixed population size and shift-based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances. These results underscore the efficacy and competitiveness of our approach in tackling the task allocation problem in multi-robot systems within nuclear accident rescue.

Distributed dynamic task allocation for unmanned aerial vehicle swarm systems: A networked evolutionary game-theoretic approach

Task allocation is a key aspect of Unmanned Aerial Vehicle (UAV) swarm collaborative operations. With an continuous increase of UAVs’ scale and the complexity and uncertainty of tasks, existing methods have poor performance in computing efficiency, robustness, and real-time allocation, and there is a lack of theoretical analysis on the convergence and optimality of the solution. This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios. A task allocation model is designed with the local utility of an individual and the global utility of the system. Then, the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game. Additionally, a PayOff-based Time-Variant Log-linear Learning Algorithm (POTVLLA) is proposed, which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter. The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness, while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one. Numerical simulation results show that the approach is optimal, robust, scalable, and fast adaptable to environmental changes, even in some realistic situations where some UAVs or tasks are likely to be lost and increased, further validating the effectiveness and superiority of the proposed framework and algorithm.

Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment

Task allocation is a vital challenge in a multi-robot environment. A hybrid fuzzy response threshold-based method is proposed to address the problem of task allocation in a heterogeneous mobile robot environment. The method follows a distributed task allocation approach where every robot chooses its task and performs it, resulting in concurrent execution. The algorithm uses a fuzzy inference system to determine the capability of the robot to carry out a task. Then, the robot employs the response threshold model, utilizing the obtained capability to decide on the task to complete. The objective here is to maximize the tasks completed with the resources available while balancing the affinity with which the task is done. The proposed algorithm is initially applied to the static scenario where there is no failure among the mobile robots. The algorithm is then improved to run in the dynamic scenario to study the effect on the allocation. The proposed algorithm is empirically evaluated in simulation for multiple runs under different environment instances. The results show a good increase in tasks performed successfully across all the instances in static and dynamic scenarios. The proposed algorithms are validated using FireBird V mobile robots in an experimental environment.

Difficulty and complexity definitions for assembly task allocation and assignment in human–robot collaborations: A review

This paper presents a literature review on the different aspects of task allocation and assignment problems in human–robot collaboration (HRC) tasks in industrial assembly environments. In future advanced industrial environments, robots and humans are expected to share the same workspace and collaborate to efficiently achieve shared goals. Difficulty- and complexity-aware HRC assembly is necessary for human-centric manufacturing, which is a goal of Industry 5.0. Therefore, the objective of this study is to clarify the definitions of difficulty and complexity used to encourage effective collaboration between humans and robots to leverage the adaptability of humans and the autonomy of robots. To achieve this goal, a systematic review of the following relevant databases for computer science was performed: IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and ASME Digital Collection. The results extracted from 74 peer-reviewed research articles published until July 2022 were summarized and categorized into four taxonomies for 145 difficulty and complexity definitions from the perspectives of (1) definition-use objectives, (2) evaluation objectives, (3) evaluation factors, and (4) evaluation variables. Next, existing definitions were primarily classified according to the following two criteria to identify potential future studies on the formulation of new definitions for human-centric manufacturing: (1) agent specificity and (2) common aspects in manual and robotic assemblies.

Optimal Task Allocation for Battery-Assisted and Price-Aware Mobile Edge Computing

In this paper, we propose a battery-assisted approach to improve energy efficiency for mobile edge computing (MEC) networks by utilizing the space-time-varying characteristics of electricity price. We formulate a price-aware task allocation problem (PATA) that jointly considers the cost for task computation, the cost of task offloading, and the cost of battery degradation. PATA is seemingly a mixed integer non-linear programming problem. By a graph-based reformulation, solving PATA is mapped to finding minimum cost flows or convex cost flows in the graph. This discovery reveals that the global optimum of PATA is obtained in polynomial time. Performance evaluation manifests that the proposed approach significantly outperforms other approaches.

Multi-Agent Task Allocation with Interagent Distance Constraints

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

Modification of the Ant Colony Optimization Algorithm for Solving Multi-Agent Task Allocation Problem in Agricultural Application

This paper considers the problem of task allocation where the goal is to find a coalition of UAVs (agents) to complete on-farm agricultural tasks. In this study, Ant Colony Optimization (ACO) algorithm is employed to find the best coalition of agents. The performance of the basic ACO algorithm for solving task allocation is improved by modifying the efficiency factor. In the proposed algorithm, the efficiency factor is defined as a function that relates not only to the capability of the agents and the distance between the agents, but also to the distance between the agents and the target. To solve the task allocation problem, the capability list of the agents was also adjusted using common UAV capabilities in agricultural application. Simulation results showed that the proposed ACO algorithm with the modified efficiency factor improved the performance of basic ACO algorithm for solving task allocation problem in terms of the average total travel cost for each agent. The optimum number of ants and agents in the proposed algorithm was also analysed for robust performance. Simulation results revealed that the addition of the numbers of agents and ants increases the average efficiency of the algorithm. In this study, we have also added a function to calculate the system capability utilization. By employing such a function, simulation results show that the total resource used by the agents and total communication cost can be optimized. In addition, a simple experiment using five ground robots with a centralized control was also carried out as a proof of concept for the proposed algorithm.

A method of user recruitment and adaptation degree improvement via community collaboration in sparse mobile crowdsensing systems

The task allocation problem in sparse mobile crowdsensing is simplified as a subarea selection problem. However, the lack of participants in some high-value subareas leads to the low quality of the final inferred sensing map. To solve this problem, a method of user recruitment and adaptation degree improvement via community collaboration is proposed. Firstly, the adjacency matrix is constructed based on the social relationship of the participants, and then all the participants are classified into communities by the non-negative matrix factorization method of deep autoencoder-like; secondly, the perception platform matches the perception tasks with the centroids of the perception communities based on the different eigenvalues of the classified perception communities and the location characteristics of the perception tasks. After the matching is completed, some participants in the matched communities will be selected to complete the perceptual tasks under the constraint of perceptual cost; finally, the perceptual data provided by the participants is used to obtain the complete perceptual map using the compressed perceptual algorithm. We designed this user recruitment method to obtain high-quality sensing data by recruiting a small number of users after community classification based on their social relationships and then accurately inferring the entire perceptual map. The experimental results based on the Gowalla and U-Air datasets show that the user recruitment method proposed in this paper can infer accurate data with fewer sensing areas, which is significantly better than other comparison methods.