Task Allocation Algorithm Research Articles

A multi-robot task allocation algorithm based on universal gravity rules

A multi-robot task allocation algorithm based on universal gravity rules

Vehicular Computation Offloading for Industrial Mobile Edge Computing

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost.

Product design-time optimization using a hybrid meta-heuristic algorithm

Production management is a function including a process of planning, directing, controlling, and organizing ongoing activities to convert inputs into the product. Industrial production management systems allow users to manage all aspects of mass processing in a centralized and decentralized environment. They focus on the overall process of production and distribution of goods and services. The purpose of this paper is to continuously improve the flow of materials so that the quality of the product and the final services are increased, and at the same time, the cost for the consumer is reduced. Since a crucial step in the process of managing the production is the product design phase, therefore, there are always many efforts to optimize and reduce product design time. In this paper, a new algorithm for scheduling design tasks has been suggested to minimize design time in production management systems using a hybrid algorithm. Considering that the scheduling of design tasks and allocating them to engineers concerning prioritizing tasks, skill levels, and preferences of designers is an NP-hard issue, many evolutionary and meta-heuristic approaches to solve this problem have been proposed. Therefore, the offered technique in this paper utilizes a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for task allocation to designers. The proposed method is simulated via a MATLAB programming tool. The results of the experiments on a set of real-world data and random data indicate that the method improves the time of product design. Considering the different design processes for different kinds of products to improve the performance of designers is not done in this research.

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.

Task Allocation and Utility Distribution Algorithms Based on Nash Bargaining Solution

In practical applications, there are many task allocation problems involving the participation of self-interested agents, including Witkey, crowdsourcing and electronic markets. In these cases, to improve the efficiency of task allocation, a reasonable distribution of utilities is critical. To the best of our knowledge, few studies have examined the complex task allocation and utility distribution of self-interested agents, and good solutions are lacking. To address this issue, the following works are done in this paper: first, based on a task allocation model for self-interested agents and by studying the Nash bargaining solution and the bargaining characteristics of the agents, an efficiency utility distribution algorithm satisfying individual rationality and budget effectiveness is proposed. Second, based on the best response strategy of the self-interested agent, a complex task allocation algorithm for multiple self-interested agents is proposed. Finally, the effectiveness of the proposed algorithm is verified by comparing the system revenues with other utility distribution and task allocation algorithms.

Communication-Aware Multi-Agent Metareasoning for Decentralized Task Allocation

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metareasoning</i> refers to reasoning about one’s own decision making process. This paper considers metareasoning about the decision making process in multi-agent settings. We present a multi-agent metareasoning approach that enables a multi-agent team to select which task allocation algorithm to use as a function of changing communication quality level. Given a set of multi-agent task allocation algorithms, we synthesize a policy that prescribes the best algorithm to use among a predefined set of algorithms for a given communication level. Since each agent in the team runs the same policy, the team (or a part of the team) will collectively switch between task allocation algorithms as a function of the observed level of communication. We apply reactive synthesis to generate the policy from high-level specifications written in Linear Temporal Logic encoding the agents’ switching behavior with respect to the state of the environment. We perform experiments in simulation to identify the best performing algorithms under different communication levels. The communication environment is modeled using the Rayleigh fading model and communication estimation is done through the exchange of heartbeat messages among agents. We test our metareasoning policy in three types of scenarios: search & rescue, fire monitoring, and ship protection scenarios. For each scenario, we demonstrate that our policy achieved better performance with respect to either max distance traveled, max number of transmitted messages or both compared to running any single algorithm.

Task Allocation for Affective Robots Based on Willingness

Affection is regarded as a new solution to coordinate task allocation problems in the multi-robot system. In this paper, we proposed an interpretable and computational affection model based on willingness and a new personality model named CASE to measure the heterogeneity of the robot. Emotion contagion model is used to calculate affective interaction among robots, and we use willingness to quantify the impact of affective factors on task allocation and as an important factor for task allocation. Task allocation algorithm is designed for affective robots, and simulate the application of the affection model at task allocation by a typical example. It displays the characteristics and novelty of affection in task allocation and to verify the correctness, effectiveness, and novelty of affective robot task allocation algorithm. The result of the simulation experiment shows that affective robots in multi-robot cooperation systems obtain a better solution than rational robots and express the state of the art of task allocation for effective robots.

Corrections to “A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation”

Corrections to “A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation”

A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

In intelligent unmanned warehouse goods-to-man systems, the allocation of tasks has an important influence on the efficiency because of the dynamic performance of AGV robots and orders. The paper presents a hierarchical Soft Actor-Critic algorithm to solve the dynamic scheduling problem of orders picking. The method proposed is based on the classic Soft Actor-Critic and hierarchical reinforcement learning algorithm. In this paper, the model is trained at different time scales by introducing sub-goals, with the top-level learning a policy and the bottom level learning a policy to achieve the sub-goals. The actor of the controller aims to maximize expected intrinsic reward while also maximizing entropy. That is, to succeed at the sub-goals while moving as randomly as possible. Finally, experimental results for simulation experiments in different scenes show that the method can make multi-logistics AGV robots work together and improves the reward in sparse environments about 2.61 times compared to the SAC algorithm.

Local Privacy-Preserving Dynamic Worker Locations in Spatial Crowdsourcing

An outsourcing service named spatial crowdsourcing (SC) becomes popular, whereby the SC-server allocates nearby tasks to the workers based on the outsourced task and worker locations. Exposing real locations can cause serious privacy leakage. However, traditional differential privacy (DP) and encryption methods do not consider the dynamic worker location and correlation privacy. Here, a Local DP-based dynamic worker location protection (LDPDW) scheme is proposed to achieve high-quality task allocation and locally protect the correlation and location privacy of dynamic workers. Specifically, LDPDW generates noisy high correlated graph classes and obfuscates the worker locations in a static case by adopting a LDP-based correlation graph (LDPCG) algorithm and distance score-based LDP (DSLDP) algorithm, thereby achieving controlled noise addition and ensuring the correlation and location privacy. To support the privacy-preserving dynamic locations, a dynamic correlation graph-based location obfuscation (DCGLO) algorithm is proposed to allocate reasonable privacy budget $\epsilon $ , which ensures the data utility. Finally, a linear acceptance model-based task allocation (LAMTA) algorithm is used to allocate tasks to the workers with high acceptance rates. Privacy analysis and the extensive experimental results show that our LDPDW scheme follows $\epsilon $ -LDP while allocating tasks with high data utility.

Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems

We propose a decentralized auction-based algorithm for the solution of dynamic task allocation problems for spatially distributed multi-agent systems. In our approach, each member of the multi-agent team is assigned to at most one task from a set of spatially distributed tasks, while several agents can be allocated to the same task. The task assignment is dynamic since it is updated at discrete time stages (iterations) to account for the current states of the agents as the latter move towards the tasks assigned to them at the previous stage. Our proposed methods can find applications in problems of resource allocation by intelligent machines such as the delivery of packages by a fleet of unmanned or semi-autonomous aerial vehicles. In our approach, the task allocation accounts for both the cost incurred by the agents for the completion of their assigned tasks (e.g., energy or fuel consumption) and the rewards earned for their completion (which may reflect, for instance, the agents' satisfaction). We propose a Greedy Coalition Auction Algorithm (GCAA) in which the agents possess bid vectors representing their best evaluations of the task utilities. The agents propose bids, deduce an allocation based on their bid vectors and update them after each iteration. The solution estimate of the proposed task allocation algorithm converges after a finite number of iterations which cannot exceed the number of agents. Finally, we use numerical simulations to illustrate the effectiveness of the proposed task allocation algorithm (in terms of performance and computation time) in several scenarios involving multiple agents and tasks distributed over a spatial 2D domain.

Multi-Round Incentive Mechanism for Cold Start-Enabled Mobile Crowdsensing

Mobile CrowdSensing (MCS) has emerged as a novel paradigm for performing large-scale sensing tasks. Many incentive mechanisms have been proposed to encourage user participation in MCS. However, most of them ignore the inevitable cold start stage of MCS, where the MCS system has just begun releasing tasks. Also, they all adopt the single-round incentive without considerations of the continuous cumulative effect. Given the severe shortage of participants in the cold start stage of MCS, this paper proposes a Multi-Round Incentive Mechanism (MRIM). MRIM is based on monetary incentives by adopting multi-round cooperation and alternating between task information diffusion and task allocation operations, both of which are NP-hard problems even without inter-round coupling imposed by system budget constraints. We explore a method to predict the probability of users participating in tasks accurately. Furthermore, we present an efficient task information diffusion algorithm to maximize the number of users participating in tasks by submitting bids. We propose a fast task allocation algorithm based on truthful auction, comprising an approximation algorithm for solving the one-round winner selection and payment calculation. With budget constraints, MRIM maximizes the number of completed tasks by iteratively performing task information diffusion and task allocation. We also prove that MRIM also possesses desired properties such as computational efficiency, user rationality, platform profitability, and price truthfulness, which can further guarantee the robustness of MRIM. The extensive simulations conducted on real-world datasets have proved the efficiency of MRIM.

Task Allocation Approach for Minimizing Make-Span in Wireless Sensor Actor Networks

Wireless Sensor Actor Networks (WSANs) have contributed to the development of pervasive computing wherein time consideration to perform the tasks of pervasive applications is necessary. Hence, time constraint is one of the major challenges of WSANs. In this paper, we propose an analytical approach based on queuing theory to minimize the total time taken for completion of tasks, i.e., make-span, in WSANs with hybrid architecture. The best allocation rates of tasks to actor nodes are figured out through solving inequities and qualities resulting from a steady state analysis of the proposed model. Applying the calculated tasks arrival rates at each of the actors, the make-span could be minimized. To assess the accuracy of the tasks assignment rates to each of the actors attained from the suggested analytical approach and to provide a graphical representation of the WSAN a formal model in terms of the generalized stochastic Petri net (GSPN) is presented. The proposed GSPN model is analyzed, tasks distribution weights to the actors are determined, and then tasks allocation rates can be computed. Comparing the results achieved from the analytical approach and the GSPN model demonstrates that allocation rates and hence, the make-span figured out from proposed approach and the formal model are the same. Experimental results in typical scenarios show shorter make-span and longer network lifetime compared to when one of the two popular traditional task allocation algorithms, namely, opportunistic load balancing (OLB), and stochastic allocation (SA) algorithms, is used.

Real‐Time Task Allocation of Heterogeneous Unmanned Aerial Vehicles for Search and Prosecute Mission

In recent years, the Internet of Things (IoT) has developed rapidly after the era of computers and smart phones, which is expected to be applied to cities to improve the quality of life and realize the intelligence of smart cities. In particular, with the outbreak of coronavirus disease 2019 (COVID‐19) last year, in order to reduce contact, some IoT devices, such as robots, unmanned aerial vehicles (UAVs), and unmanned vehicles, have played a great role in temperature monitoring, goods delivery, and so on. In this paper, we study the real‐time task allocation problem of heterogeneous UAVs searching and delivering goods in the city. Considering the resource requirement of task and resource constraints of the UAV, when the resource of a single UAV cannot meet the requirement of the task, we propose a method of forming a UAV coalition based on contract net protocol. We analyze the coalition formation problem from two aspects: mission completion time and UAV’s energy consumption. Firstly, the mathematical model is established according to the optimization objective and condition constraints. Then, according to the established mathematical model, different coalition formation algorithms are proposed. To minimize the mission completion time, we propose a two‐stage coalition formation algorithm. Aiming at minimizing the UAV’s energy consumption, it is transformed into a zero‐one integer programming problem, which can be solved by the existing solver. Then, considering both mission completion time and energy consumption, we propose a coalition formation algorithm based on a resource tree. Finally, we design some simulation experiments and compare with the task allocation algorithm based on resource welfare. The simulation results show that our proposed algorithms are feasible and effective.

Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions

The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

Genetic Algorithm for Dynamic Task Allocation of Multiautonomous Unmanned Air Vehicles (Dept.C)

Uninanned aerial vehicles (UAVs) have useful military applications, including reconnaissance, search and destroy, search and rescue missions in hazardous environments such as battlefields or disaster areas. Recently, there has been considerable interest in the possibility of using large teams of UAVs functioning cooperatively to accomplish a large number of tasks e.g. attacking targets However, this requires the assignment of multiple spatially distributed tasks lo each UAV along with a feasible path thai minimizes eflori and avoids threats. Task Allocation (TA) is one of the core steps 10 effectively exploit the capabilities of cooperative control of multiple UAV teams. It is an NP-complete problem non-deterministic polynomial time“. So the computation can't be implemented in real time, no chance for cooperation among the team members, and no autonomy for these vehicles. The reported papers in this field consider the problem in static condition using different techniques (e.g. auction based, scheduling, linear programming). In this paper, a nerve dynamic task allocation algorithm is presented that is based on the principles of genetic algorithm GA). It discusses the adaptation and implementation of the GA search strategy to the task allocation problem in the cooperative control of multiple UAVs. Simulation results indicate that the GA strategy is a feasible approach for the task allocation problem, and the resulted task assignment is near optimal. This means that the total cost of the team is minimized. A major advantage sits low computation cost

A Cloudware Architecture for Collaboration of Multiple AGVs in Indoor Logistics: Case Study in Fabric Manufacturing Enterprises

In Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously using multiple AGVs. However, some challenges require special solutions for AGVs to be used in industrial autonomous transportation. These challenges can be addressed under four main headings: positioning, optimum path planning, collision avoidance and optimum task allocation. The solutions produced for these challenges may require special studies that vary depending on the type of tasks and the working environment in which AGVs are used. This study focuses on the problem of automated indoor logistics carried out in the simultaneous production of textile finishing enterprises. In the study, a centralized cloud system that enables multiple AGVs to work in collaboration has been developed. The finishing enterprise of a denim manufacturing factory was handled as a case study and modelling of mapping-planning processes was carried out using the developed cloud system. In the cloud system, RestFul APIs, for mapping the environment, and WebSocket methods, to track the locations of AGVs, have been developed. A collaboration module in harmony with the working model has been developed for AGVs to be used for fabric transportation. The collaboration module consists of task definition, battery management-optimization, selection of the most suitable batch trolleys (provides mobility of fabrics for the finishing mills), optimum task distribution and collision avoidance stages. In the collaboration module, all the finishing processes until the product arrives the delivery point are defined as tasks. A task allocation algorithm has been developed for the optimum performance of these tasks. The multi-fitness function that optimizes the total path of the AGVs, the elapsed time and the energy spent while performing the tasks have been determined. An assignment matrix based on K nearest neighbor (k-NN) and permutation possibilities was created for the optimal task allocation, and the most appropriate row was selected according to the optimal path totals of each row in the matrix. The D* Lite algorithm has been used to calculate the optimum path between AGVs and goals by avoiding static obstacles. By developing simulation software, the problem model was adapted and the operation of the cloud system was tested. Simulation results showed that the developed cloud system was successfully implemented. Although the developed cloud system has been applied as a case study in fabric finishing workshops with a complex structure, it can be used in different sectors as its logistic processes are similar.

Privacy-Preserving Incentive Mechanisms for Truthful Data Quality in Data Crowdsourcing

Data crowdsourcing is a promising paradigm that leverages the “wisdom” of a potentially large crowd of “workers” in many application domains. Quality-aware crowdsourcing is beneficial as it makes use of workers' data quality to perform task allocation and data aggregation. However, a worker's quality and data can be her private information that she may misreport to the crowdsourcing requester. Moreover, a worker's quality and data can depend on her sensitive information (e.g., location), which can be inferred from the outcomes of task allocation and data aggregation. In this paper, we devise Privacy-preserving crowdsourcing mechanisms for truthful Data Quality Elicitation (PDQE). In these mechanisms, we design differentially private task allocation and data aggregation algorithms to prevent the inference of a worker's quality and data from the outcomes of these algorithms. The mechanisms also incentivize workers to truthfully report their quality and data and make desired efforts. We further show that both mechanisms for a single task and multiple tasks achieve a bounded performance gap compared to the optimal strategy. We evaluate the proposed mechanisms using simulations based on real-world data, which corroborate their highly-desired properties on truthful data quality elicitation, data accuracy and privacy protection.

An energy‐aware method for task allocation in the Internet of things using a hybrid optimization algorithm

SummaryInternet of Things (IoT) is utilized as an emerging sample for defining the future of technology in which physical items like sensors, radio‐frequency identification tags, mobile phones, actuators, and so on, can have interaction together and have cooperation with their neighbors for obtaining joint objectives. The performance of the deployed tasks and applications on the network is considered as one of the critical goals in this model, which is achieved by the task allocation mechanism. Task allocation in the IoT is so complicated due to the intricate connection among machines. The task allocation problem is considered as an NP‐hard problem, so a new task allocation algorithm in the IoT environment is proposed using the combination of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithm. Also, the issues of the PSO algorithm, such as getting stuck in local optimization and not achieving an optimal response, forced us to present a method based on the combination of SA and the PSO algorithms. The results of simulation in MATLAB environment illustrated that the suggested method performs better compared to the PSO and SA‐based methods.

Unsupervised learning based coordinated multi-task allocation for unmanned surface vehicles

In recent decades, unmanned surface vehicles (USVs) are attracting increasing attention due to their underlying capability in autonomously undertaking complex maritime tasks in constrained environments. However, the autonomy level of USVs is still limited, especially when being deployed to conduct multiple tasks simultaneously. This paper, therefore, aims to improve USVs autonomy level by investigating and developing an effective and efficient task management algorithm for multi-USV systems. To better deal with challenging requirements such as allocating vast tasks in cluttered environments, the task management has been de-composed into two submissions, i.e., task allocation and task execution. More specifically, unsupervised learning strategies have been used with an improved K-means algorithm proposed to first assign different tasks for a multi-USV system then a self-organising map (SOM) been implemented to deal with the task execution problem based upon the assigned tasks for each USV. Differing to other work, the communication problem that is crucial for USVs in a constrained environment has been specifically resolved by designing a new competition strategy for K-means algorithm. Key factors that will influence the communication capability in practical applications have been taken into account. A holistic task management architecture has been designed by integrating both the task allocation and task execution algorithms, and a number of simulations in both simulated and practical maritime environments have been carried out to validate the effectiveness of the proposed algorithms.