Distributed Task Allocation Algorithm Research Articles

Distributed Task Allocation Algorithm for Heterogeneous UAV Cluster Based on Game Theory

This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources, two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands, airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.

Distributed task allocation algorithm for heterogeneous unmanned aerial vehicle swarm based on coalition formation game

Distributed task allocation algorithm for heterogeneous unmanned aerial vehicle swarm based on coalition formation game

An Efficient Distributed Task Allocation Method for Maximizing Task Allocations of Multirobot Systems

This paper addresses the distributed task allocation problem for maximizing the total number of successfully executed tasks of multirobot systems. Due to the deadline time of tasks and fuel limits of robotic vehicles, not all tasks can be successfully executed sometimes. Based on the performance impact (PI) algorithm, an effective and efficient performance impact (EEPI) algorithm is proposed, its novelty lies in its cost function and task release procedure. The fundamental ideas of the proposed cost function are as follows. First, the traveling time from the initial position of each vehicle to the positions of its tasks is minimized, so that more time can be left for the vehicle to execute more tasks due to the limited fuel. Second, the start time of each task should be close enough to its deadline, so that tasks with earlier deadlines can be assigned earlier than those with later deadlines. To avoid invalid removal performance impacts (RPIs) and inclusion performance impacts (IPIs), the tasks assigned to a vehicle are all released if the number of tasks removed by the vehicle during the task removal phase is the most, which further increases the total number of successfully executed tasks. Both simulations and hardware-in-the-loop experiments suggest that compared with the state-of-the-art distributed task allocation algorithms, the proposed EEPI is not only effective in maximizing the number of successfully executed tasks but efficient in saving the number of iterations and time to converge. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the limitations of the existing distributed task allocation algorithms for maximizing the total number of successfully executed tasks. The consensus-based bundle algorithm (CBBA) has been proven to guarantee convergence and 50% optimality under the diminishing marginal gain (DMG) assumption in previously published works. Based on CBBA, a performance impact (PI) algorithm was proposed, and simulations show that it can assign more tasks than CBBA when applied to time-critical scenarios with low task-to-vehicle ratios. Starting from the results of PI, a rescheduling method named PI for maximizing assignments (PI-maxAss) was proposed, which has been demonstrated to assign more tasks than PI with high task-to-vehicle ratios. However, much more iterations and time are required by PI-maxAss to converge to globally consistent assignments because of the rescheduling. Due to the above considerations, an effective and efficient performance impact (EEPI) algorithm is proposed in this paper to maximize the number of successfully executed tasks without any rescheduling. Both simulations and hardware-in-the-loop experiments suggest that compared with the algorithms mentioned above, the proposed EEPI is effective in maximizing the number of successfully executed tasks and efficient in saving the number of iterations and time to converge. In future work, the distributed task allocation problem in which several vehicles execute a task at the same time cooperatively or a vehicle executes several tasks simultaneously will be further addressed.

Auction-Based Distributed Task Allocation Algorithm for Drone Swarms

Research on drone swarms has gained traction in recent times due to their ability to outperform single drones in various tasks. In order for autonomous drone swarms to carry out tasks effectively, it is crucial that the tasks are distributed among the drones. This poses a considerable challenge that must be addressed in the advancement of drone swarm technology. The assignment problem is basically defined as the process of assigning M tasks to N drones in a way that minimizes the total cost. This paper presents an auction-based distributed task allocation algorithm called Harmony DTA (Drone Task Allocation) for heterogeneous drone swarms. Prior research primarily focuses on minimizing overall costs associated with assignments. In contrast, Harmony DTA not only minimizes total costs through an enhanced cost calculation function, but also ensures equitable distribution of workload among drones. Additionally, the proposed two-stage auction process reduces the total message size utilized during communication. Matlab simulations and field tests were conducted to assess the effectiveness of the proposed algorithm. Moreover, the algorithm's performance was evaluated in comparison to the CBBA (Consensus Based Bundle Algorithm). The results obtained indicated that the proposed algorithm is capable of assigning tasks with high efficiency, without encountering conflicts.

Comparison of Distributed Task Allocation Algorithms Considering Non-ideal Communication Factors for Multi-UAV Collaborative Visit Missions

Comparison of Distributed Task Allocation Algorithms Considering Non-ideal Communication Factors for Multi-UAV Collaborative Visit Missions

A Distributed Collaborative Allocation Method of Reconnaissance and Strike Tasks for Heterogeneous UAVs

Unmanned aerial vehicles (UAVs) are becoming more and more widely used in battlefield reconnaissance and target strikes because of their high cost-effectiveness, but task planning for large-scale UAV swarms is a problem that needs to be solved. To solve the high-risk problem caused by incomplete information for the combat area and the potential coordination between targets when a heterogeneous UAV swarm performs reconnaissance and strike missions, this paper proposes a distributed task-allocation algorithm. The method prioritizes tasks by evaluating the swarm’s capability superiority to tasks to reduce the search space, uses the time coordination mechanism and deterrent maneuver strategy to reduce the risk of reconnaissance missions, and uses the distributed negotiation mechanism to allocate reconnaissance tasks and coordinated strike tasks. The simulation results under the distributed framework verify the effectiveness of the distributed negotiation mechanism, and the comparative experiments under different strategies show that the time coordination mechanism and the deterrent maneuver strategy can effectively reduce the mission risk when the target is unknown. The comparison with the centralized global optimization algorithm verifies the efficiency and effectiveness of the proposed method when applied to large-scale UAV swarms. Since the distributed negotiation task-allocation architecture avoids dependence on the highly reliable network and the central node, it can further improve the reliability and scalability of the swarm, and make it applicable to more complex combat environments.

Distributed Task Allocation Algorithms for Multi-Agent Systems With Very Low Communication

In this paper we explore the problem of task allocation when communication is very low, e.g., when the probability of a successful message between agents is ≪ 0.01. Such situations may occur when agents choose not to communicate for reasons of stealth or when agent-to-agent communication is actively jammed by an adversary. In such cases, agents may need to divide tasks without knowing the locations of each other. Given the assumption that agents know the total number of agents in the workspace, we investigate solutions that ensure all tasks are eventually completed—even if some of the agents are destroyed. We present two task allocation algorithms that assume communication may not happen, but that benefit whenever communications are successful. (1) The Spatial Division Playbook Algorithm divides task among agents based on an area decomposition. (2) The Traveling Salesman Playbook Algorithm considers mission travel distance by leveraging Christofides’ 3/2 approximation algorithm. These algorithms have task completion runtime complexity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> log <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), respectively, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> refers to the total number of tasks. We compare both algorithms to four state-of-the-art task allocation algorithms—ACBBA, DHBA, PIA and GA — across multiple communication levels and multiple numbers of targets, and using three different communication models. The new algorithms perform favorably, in terms of the time required to ensure all targets are visited, in the special case when communication is very low.

Game theoretic self-organization in multi-satellite distributed task allocation

This paper addresses self-organized task allocation in multi-satellite systems by formulating it as a potential game and proposing a distributed task allocation algorithm (DT2A) via learning in games. We show that each Nash equilibrium of the game constitutes a task cover that maximizes the number of executed tasks. Moreover, we also prove that the proposed DT2A converges with probability 1 to near optimal assignments in finite time. Numerical experiments demonstrate the algorithm robustness against incidental disturbances and reveal the positive effect of the memory length on solution efficiency refinement. Comparison experiments in both small-scale and large-scale scenarios highlight the superiority of the presented methodology over existing typical methods that are distributed or centralized.

A new task offloading algorithm in edge computing

In the last few years, the Internet of Things (IOT), as a new disruptive technology, has gradually changed the world. With the prosperous development of the mobile Internet and the rapid growth of the Internet of Things, various new applications continue to emerge, such as mobile payment, face recognition, wearable devices, driverless, VR/AR, etc. Although the computing power of mobile terminals is getting higher and the traditional cloud computing model has higher computing power, it is often accompanied by higher latency and cannot meet the needs of users. In order to reduce user delay to improve user experience, and at the same time reduce network load to a certain extent, edge computing, as an application of IOT, came into being. In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this scenario to improve user experience and balance resource utilization. Simulations show that the algorithm has a certain adaptability compared to the traditional algorithm in the scenario of multi-user single cell, and reduces the complexity of the algorithm compared to the centralized algorithm, and reduces the average response delay of the overall user. And balance the load of each edge computing server, improve the robustness and scalability of the system.

Distributed task allocation algorithm based on connected dominating set for WSANs

Proper allocation of tasks between actors is one of the critical issues for Wireless Sensor and Actor Networks (WSANs), which can improve the execution efficiency of the network, prolong the network life and so on. Traditional task allocation algorithms assume that the underlying network of the wireless systems are always connected, thus the only thing they should consider is how to improve the performance of task allocation. However, due to the movements of the actors, the assumption is hard to come into existence in WSANs. To solve this problem, we proposed a task allocation algorithm named MIA-TA in this paper. This approach consists of two stages. We apply the improved connected dominating set (CDS) formation algorithm MIA to construct the backbone of the network firstly. Tasks are assigned on the basic of CDS in the second stage. By considering the network conditions of executor’s leave and arrive positions, we can ensure the entire network’s connectivity. Simulation results confirm that MIA-TA algorithm can be used to solve both task allocation and connectivity maintenance problems with reasonable movements and messages generated in the whole process.

How Good are Distributed Allocation Algorithms for Solving Urban Search and Rescue Problems? A Comparative Study With Centralized Algorithms

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO. Note to Practitioners —This paper was motivated by the limitation of current distributed task allocation algorithms as they cannot achieve performances that are as good as the centralized ones. Therefore, a centralized algorithm is designed to evaluate the performance gap between the state-of-the-art distributed and centralized approaches. In the future research section, a new distributed particle swarm optimization (PSO) algorithm is proposed based on this paper as the research has shown that the proposed centralized PSO algorithm delivers the best results so it is potentially a strong candidate for adaptation.

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

This paper addresses two main problems with many heuristic task allocation approaches—solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as performance impact (PI) is used as the vehicle for developing solutions to these problems as it has been shown to outperform the state-of-the-art consensus-based bundle algorithm for time-critical problems with tight deadlines, but is both static and suboptimal with a tendency toward trapping in local minima. This paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft-max action-selection procedure that increases the algorithm’s exploratory properties. This paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft-max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state of the art for multiagent, time-critical task assignment. Note to Practitioners —This work was motivated by the limitations of current agent-to-task allocation algorithms that do not use a central server for communication. In previously published work, the current state-of-the-art consensus-based bundle algorithm has demonstrated poor performance when applied to model task allocation problems with critical time limits, often failing to assign all of the tasks, especially when the deadlines are tight. The performance impact (PI) algorithm has a much better success rate with these model problems but would be flawed when applied to real missions because it has no mechanism for online replanning when new information becomes available. In addition, it is somewhat restricted in the way it searches for a problem solution, meaning that more efficient plans are often available but are not discovered. This paper tackles both of these shortcomings. The PI algorithm is extended to include a module that permits rescheduling when necessary, and a further module is introduced that widens the scope of the solution search. A third module that is able to offer robust plans, even for large-scaled missions involving many agents and tasks, has also been developed, although it is not discussed here. Implementation and testing of a version of PI that incorporates all three of these modules are the final goal of this research.

Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

A Distributed Task Allocation Algorithm for a Multi-Robot System in Healthcare Facilities

Various ambient assisted living (AAL) technologies have been proposed for improving the living conditions of elderly people. One of them is to introduce robots to reduce dependency on support staff. The tasks commonly encountered in a healthcare facility such as a care home for elderly people are heterogeneous and are of different priorities. A care home environment is also dynamic and new emergency priority tasks, which if not attended shortly may result in fatal situations, may randomly appear. Therefore, it is better to use a multi-robot system (MRS) consisting of heterogeneous robots than designing a single robot capable of doing all tasks. An efficient task allocation algorithm capable of handling the dynamic nature of the environment, the heterogeneity of robots and tasks, and the prioritisation of tasks is required to reap the benefits of introducing an MRS. This paper proposes Consensus Based Parallel Auction and Execution (CBPAE), a distributed algorithm for task allocation in a system of multiple heterogeneous autonomous robots deployed in a healthcare facility, based on auction and consensus principles. Unlike many of the existing market based task allocation algorithms, which use a time extended allocation of tasks before the actual execution is initialised, the proposed algorithm uses a parallel auction and execution framework, and is thus suitable for highly dynamic real world environments. The robots continuously resolve any conflicts in the bids on tasks using inter-robot communication and a consensus process in each robot before a task is assigned to a robot. We demonstrate the effectiveness of the CBPAE by comparing its simulation results with those of an existing market based distributed multi-robot task allocation algorithm and through experiments on real robots.

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.

A Distributed Algorithm for Parallel Multi-task Allocation Based on Profit Sharing Learning

Task allocation via coalition formation is a fundamental research challenge in several application domains of multi-agent systems (MAS), such as resource allocation, disaster response management, and so on. It mainly deals with how to allocate many unresolved tasks to groups of agents in a distributed manner. In this paper, we propose a distributed parallel multi-task allocation algorithm among self-organizing and self-learning agents. To tackle the situation, we disperse agents and tasks geographically in two-dimensional cells, and then introduce profit sharing learning (PSL) for a single agent to search its tasks by continual self-learning. We also present strategies for communication and negotiation among agents to allocate real workload to every tasked agent. Finally, to evaluate the effectiveness of the proposed algorithm, we compare it with Shehory and Kraus' distributed task allocation algorithm which were discussed by many researchers in recent years. Experimental results show that the proposed algorithm can quickly form a solving coalition for every task. Moreover, the proposed algorithm can specifically tell us the real workload of every tasked agent, and thus can provide a specific and significant reference for practical control tasks.

A Distributed Deterministic Annealing Algorithm for Limited-Range Sensor Coverage

This paper presents a distributed coverage algorithm for a network of mobile agents. Unlike previous work that uses a simple gradient descent algorithm, here we employ an existing deterministic annealing (DA) technique to achieve more optimal convergence values since typical coverage objective functions contain many local minima. We replicate the results of the classical DA algorithm while imposing a limited-range constraint to sensors. As the temperature is decreased, phase changes lead to a regrouping of agents, which is decided through a distributed task allocation algorithm. While simple gradient descent algorithms are heavily dependent on initial conditions for such non-convex coverage objective functions, annealing techniques are generally less prone to this phenomena. The results of our simulations confirm this fact, as we show in the manuscript.

An Integrated Approach for Achieving Multirobot Task Formations

In this paper, a problem, called the initial formation problem, within the multirobot task allocation domain is addressed. This problem consists in deciding which robot should go to each of the positions of the formation in order to minimize an objective. Two different distributed algorithms that solve this problem are explained. The second algorithm presents a novel approach that uses cost means to model the cost distribution and improves the performance of the task allocation algorithm. Also, we present an approach that integrates distributed task allocation algorithms with a behavior-based architecture to control formations of robot teams. Finally, simulations and real experiments are used to analyze the formation behavior and provide performance metrics associated with implementation in realistic scenarios.

Application of a hybrid distributed multi-agent task allocation mechanism to soccer robot systems

The advantages and disadvantages of primary multi-agent task allocation mechanisms were analyzed.A hybrid distributed task allocation algorithm for the role allocation of soccer robots was presented,which combined the market-based task allocation mechanism with the regulation-based mechanism.This algorithm can dynamically allocate roles for robots,and at the same time unexpected oscillations of roles were avoided.Simulations and actual matches validate the presented algorithm.