Task Allocation Approach Research Articles

Task Allocation and Saturation Attack Approach for Unmanned Underwater Vehicles

In modern marine warfare, unmanned underwater vehicles (UUVs) have fast and efficient attack capabilities. However, existing research on UUV attack strategies is relatively limited, often ignoring the requirement for the effective allocation of different strategic value areas, which restricts its performance in the marine combat environment. To this end, this paper proposes an innovative UUV task allocation and saturation attack strategy. The strategy first divides the area according to the distribution density of enemy UUVs, and then reasonably allocates tasks according to the enemy’s regional value and the attack capability of our UUVs. Our UUVs then sail to the enemy area and are evenly distributed in the encirclement to ensure accurate saturation attacks. In the task allocation link, the grey wolf optimizer is improved by introducing Logistic chaos mapping and differential evolution mechanism, which improves the search efficiency and allocation accuracy. At the same time, the combination of the optimal matching algorithm and Bezier curve dynamic path control ensures the accuracy and flexibility of a coordinated attack. The simulation experimental results show that the strategy shows high attack efficiency and practicality in marine combat scenarios, providing an effective solution for UUV attack tasks in complex marine environments.

Optimized Task Deployment in Dynamic Voltage and Frequency Scaling-Enabled Network-on-Chip Systems: Enhancing Energy Efficiency and Real-Time Responsiveness

In modern multi-design computing systems, which employ dynamic voltage and frequency scaling (DVFS) and network-on-chip (NoC) communications, the optimization of task deployment is precarious for enhancing overall system performance. It introduces a comprehensive methodology that integrates task allocation, scheduling, frequency management, redundancy handling, and diverse data routing approaches. The aim is to optimize energy intake, real-time responsiveness, and system heftiness. The system design features a primary processing element associated with three slave computing units (CUs) within a 2D mesh network, with the primary CU connected to a co-processor for dependent task scheduling. This research also proposes innovative algorithms for co-processor and real-time operating system (RTOS) scheduling to reduce latency and boost power efficiency. These methodologies aim to maximize job scheduling efficiency in RTOS environments, thus refining overall system performance.

A lexicographic optimization-based approach for efficient task allocation in industrial transportation multi-robot systems

In industrial transportation applications, multi-robot systems (MRS) are assigned to perform transportation tasks until their batteries are depleted, requiring them to move to battery charging stations. This temporary unavailability of the robots during charging decreases system productivity that measures the number of transportation tasks accomplished with the available robot energy. As a result, maximizing task completion becomes crucial, especially with prioritized tasks and increased workload. This can be achieved through an energy management strategy. In this context, the Lexicographic Optimization-based Multi-Robot Task Allocation (LO-MRTA) approach is proposed to maximize the reward in terms of system productivity with a limited energy consumption. The existing multi-robot energy management approaches consider the energy management during the tasks execution and neglect workload scaling issue with increasing tasks, whereas the LO-MRTA approach accounts for the energy consumption in the large-scale tasks allocation process. The main idea of the proposed approach is to consider the global state of the robots in the MRS system before carrying out the tasks execution, enhancing the task allocation decisions in terms of the energy management. The evaluation scenarios show the promising performance of the LO-MRTA approach in comparison with three existing baselines in terms of the system productivity, overall energy consumption and workload scaling effects.

SHIELD: A Secure Heuristic Integrated Environment for Load Distribution in Rural-AI

The increasing adoption of edge computing in rural areas is leading to a substantial rise in data generation, necessitating the need for development of advanced load balancing algorithms. This is particularly important in applications that utilise existing, though limited, computational and data communication infrastructures. Furthermore, rural communities have growing concerns regarding the privacy, security, and ownership of the data produced within their agricultural fields. Load distribution in rural edge devices can enhance agricultural practices by improving resource usage, decision-making, and addressing network connectivity challenges. Managing resource utilisation in this way also improves economic investments made in managing and deploying edge devices in rural environments. In this work, we propose SHIELD, a security-aware load balancing framework, primarily designed for edge-based systems in rural areas. For handling environments with limited connectivity, SHIELD efficiently manages tasks and computational resources by categorising them into restricted, public and private, shared respectively. It also allocates tasks considering key performance factors such as completion time, resource utilisation, failure rate, and security. The framework is evaluated on a weed detection scenario in precision agriculture, using three federated learning (FL) variants (local model training, global model aggregation, and model prediction) with the ResNet-50 model trained on the DeepWeeds image classification dataset. The proposed framework also integrates encryption and task replication techniques for data confidentiality, integrity, and availability. Experimental results show that SHIELD demonstrates an average of 23% (using Parsl), 29% (using OpenWhisk) improvement in failure rate and 18 s (Parsl), 13 s (OpenWhisk) average improvement in makespan compared to other task allocation approaches, such as secure variants of random, round robin, and least loaded.

Game-theoretic distributed approach for heterogeneous-cost task allocation with budget constraints

This paper investigates heterogeneous-cost task allocation with budget constraints (HCTAB), wherein heterogeneity is manifested through the varying capabilities and costs associated with different agents for task execution. In contrast with the centralized optimization-based method, the HCTAB problem is solved using a fully distributed framework, and a coalition formation game is introduced to provide a theoretical guarantee for this distributed framework. To solve the coalition formation game, a convergence-guaranteed log-linear learning algorithm based on heterogeneous cost is proposed. This algorithm incorporates two improvement strategies, namely a cooperative exchange strategy and a heterogeneous-cost log-linear learning strategy. These strategies are specifically designed to be compatible with the heterogeneous cost and budget constraints characteristic of the HCTAB problem. Through ablation experiments, we demonstrate the effectiveness of these two improvements. Finally, numerical results show that the proposed algorithm outperforms existing task allocation algorithms and learning algorithms in terms of solving the HCTAB problem.

Collaborative approach for swarm robot systems based on distributed DRL

Navigation and task allocation for swarm robot systems are more difficult than for a single robot. In swarm robot systems, navigation and task allocation difficulties are compounded by challenges such as a dynamic environment, exponentially increasing complexity, and collaboration. In this study, a deep reinforcement learning (DRL) based collaborative approach for navigation and task allocation is proposed, which can be trained at the same time as the training time of a single robot and achieve a high success rate. The proposed approach implements DRL as a distributed architecture. Furthermore, the proposed approach uses adaptive reward mechanisms, a leaderless control approach for environment control, and a simultaneous learning approach for learning. The proposed approach is evaluated by the success rate of reaching the target, the success rate of reaching the target by the shortest path, the success rate of selecting a task, and the success rate of selecting the nearest task. In addition, the costs of the non-collaborative approach are compared with the costs of the collaborative approach. The evaluations show that the success rate of navigation and collaboration of the robots increased and the cost decreased. Thus, the performance of the proposed approach is verified.

Why it does not work? Metaheuristic task allocation approaches in Fog-enabled Internet of Drones

Several scenarios that use the Internet of Drones (IoD) networks require a Fog paradigm, where the Fog devices, provide time-sensitive functionality such as task allocation, scheduling, and resource optimization. The problem of efficient task allocation/scheduling is critical for optimizing Fog-enabled Internet of Drones performance. In recent years, many articles have employed meta-heuristic approaches for task scheduling/allocation in Fog-enabled IoT-based scenarios, focusing on network usage and delay, but neglecting execution time. While promising in the academic area, metaheuristic have many limitations in real-time environments due to their high execution time, resource-intensive nature, increased time complexity, and inherent uncertainty in achieving optimal solutions, as supported by empirical studies, case studies, and benchmarking data. We propose a task allocation method named F-DTA that is used as the fitness function of two metaheuristic approaches: Particle Swarm Optimization (PSO) and The Krill Herd Algorithm (KHA). We compare our proposed method by simulation using the iFogSim2 simulator, keeping all the settings the same for a fair evaluation and only focus on the execution time. The results confirm its superior performance in execution time, compared to the metaheuristics.

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.

Dynamic task allocation approaches for coordinated exploration of Subterranean environments

Dynamic task allocation approaches for coordinated exploration of Subterranean environments

Joint time and energy-optimal approach to allocate task to actors in wireless sensor actor networks

In most applications of wireless sensor actor networks (WSANs) in harsh environments, minimizing the make-span and maximizing the residual energy are of paramount importance. The majority of existing task allocation approaches is typically concerned with one of the energy savings or time constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform. Moreover, the limitation on the capacity of task queues has been overlooked by these approaches, and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, a novel queue length aware task allocation (QLATA) approach is proposed that considers the energy consumption as well as the make-span. QLATA is aware of task queues constraint, types of tasks, and the distribution necessities of WSANs with hybrid architecture. QLATA comprises of two protocols, namely a Make-span Calculation Protocol (MsCP) and an Energy Consumption Calculation Protocol (ECCP). Through considering both time and energy, QLATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors by solving the joint optimization for make-span and energy consumption, simultaneously. A series of extensive simulation results on typical scenarios show shorter make-span and higher remaining energy in comparison to when opportunistic load balancing (OLB), minimum make-span task allocation (MMTa), stochastic task allocation (STA), and task assignment algorithm based on quasi-newton interior point (TA-QNIP) approaches is used. It is also shown that the proposed approach performs significantly better than the four compared algorithms in terms of network lifetime.

Enhancing Load Balancing in Heterogeneous High Performance Cloud Computing with MMWRR+: A Novel Task Allocation Approach

Enhancing Load Balancing in Heterogeneous High Performance Cloud Computing with MMWRR+: A Novel Task Allocation Approach

Multi-criterion multi-UAV task allocation under dynamic conditions

Multi-UAV systems are essential in accomplishing specific tasks across various research applications. UAVs can be used in search and rescue (SR), which requires them to explore disaster areas and rescue survivors. A significant challenge in such a scenario is allocating tasks to UAVs to support as many survivors as possible. The allocation of tasks to a multi-UAV system is a highly intricate issue that falls under the NP-hard classification. Dealing with multiple attributes and constraints for tasks and UAVs can complicate the problem. Based on several limiting factors and distinctive characteristics, this paper proposes a compromised task allocation (TA) approach for multi-UAVs operating in SR scenarios. The proposed algorithm enhances the performance impact (PI) algorithm by incorporating compromised PI (CPI) with specific constraints. Additionally, CPI is extended to compromised dynamic PI (CDPI) to handle any new tasks that may arise during task execution to accommodate dynamic events. Simulation results demonstrate a performance improvement of 22% in task allocation and 28.57% for task prioritization as compared with the PI algorithm.

Deep graph neural network with fish-inspired task allocation algorithm for heart disease diagnosis

Heart disease is a very hazardous disease and many people suffered from this disease globally. The major aim is “to diagnose the heart disease with higher accuracy through decreasing error rate including computational complexity”. The existing techniques did not proffer adequate accuracy and also increased the error rate. Therefore, a Deep Graph Neural Network with Fish-Inspired Task Allocation Algorithm is proposed in this manuscript for categorizing heart disease diagnosis (DGNN-FITA-HDD). Synthetic Minority Oversampling and standard scalar strategies are utilized for pre-processing process. The pre-processed output is given to feature selection process. Two-Stage Feature Selection method selects the most important features from pre-processing output. Extracted features are transferred to Deep Graph Neural Network (DGNN) for categorizing presence and absence of heart disease. DGNN does not expose any adoption of optimization strategies for calculating the optimum parameters to assure accurate prediction. Fish-Inspired Task Allocation approach is proposed for optimizing the weight parameters of DGNN. The proposed approach is executed at MATLAB. The performance of algorithm is analyzed with/without feature selection method. By this, the proposed DGNN-FITA-HDD method attains higher accuracy with feature selection of 13.41%, 18.53%, 10.38% and 9.31% and without feature selection attains 6.5%, 8.64%, 4.39%, and 10.28% compared with existing methods, like EDGA-AHHO-HDD, XGB-MAPO-HDD, AGAFL-HDD and RFBM-HDD respectively.

Review of task allocation for human-robot collaboration in assembly

ABSTRACT Due to the high cost pressure and the increasing variant diversity, the cooperation of humans and robots represents a promising technological solution to achieve higher flexibility and efficiency in assembly. It is therefore attracting significant interest from both researchers and practitioners. As a result, numerous reviews have been published addressing different aspects of human-robot collaboration, such as safety, interaction modalities, programming, and applications. However, in this paper, for the first time, the aspect of task allocation for collaborative assembly is methodologically examined through a systematic literature review. This paper presents the current state of the art in task allocation approaches, investigates the criteria for deciding on a suitable task assignment, and discusses challenges and future research needs. After filtering the 521 publications that resulted from the initial search process, 37 relevant publications were included in the analysis and grouped into a proposed classification consisting of two main categories, static and dynamic task allocation approaches. Based on the results of the literature review, this paper presents a reference model for human-robot collaborative assembly.

An Empirical Study on Workers' Preferences in Human–Robot Task Assignment in Industrial Assembly Systems

Collaborative industrial robotic arms (cobots) are integrated industrial assembly systems relieving their human coworkers from monotonous tasks and achieving productivity gains. The question of task allocation arises in the organization of these human–robot interactions. State of the art shows static, compensatory task allocation approaches in current assembly systems and flexible, adaptive task sharing (ATS) approaches in human factors research. The latter should exploit the economic and ergonomic advantages of cobot usage. Previous research results did not provide a clear insight into whether industrial workers prefer static or adaptive task allocation and which tasks workers do prefer to assign to cobots. Therefore, we set up a cobot demonstrator with a realistic industrial assembly use case and did a user study with experienced workers from the shop floor (n = 25). The aim of the user study is to provide a systematic understanding and evaluation of workers' preferences in a practical context of human–robot interaction (HRI) in assembly. Our main findings are that participants preferred the ATS concept to a predetermined task allocation and reported increased satisfaction with the allocation. Results show that participants are more likely to give manual tasks to the cobot in contrast to cognitive tasks. It shows that workers do not entrust all tasks to robots, but like to take over cognitive tasks by themselves. This work contributes to the design of human-centered HRI in industrial assembly systems.

Implementation and Evaluation of Dynamic Task Allocation for Human–Robot Collaboration in Assembly

Human–robot collaboration is becoming increasingly important in industrial assembly. In view of high cost pressure, resulting productivity requirements, and the trend towards human-centered automation in the context of Industry 5.0, a reasonable allocation of individual assembly tasks to humans or robots is of central importance. Therefore, this article presents a new approach for dynamic task allocation, its integration into an intuitive block-based process planning framework, and its evaluation in comparison to both manual assembly and static task allocation. For evaluation, a systematic methodology for comprehensive assessment of task allocation approaches is developed, followed by a corresponding user study. The results of the study show for the dynamic task allocation on the one hand a higher fluency in the human–robot collaboration with good adaptation to process delays, and on the other hand a reduction in the cycle time for assembly processes with sufficiently high degrees of parallelism. Based on the study results, we draw conclusions regarding assembly scenarios in which manual assembly or collaborative assembly with static or dynamic task allocation is most appropriate. Finally, we discuss the implications for process planning when using the proposed task allocation framework.

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data.

Scheduling of Missions with Constrained Tasks for Heterogeneous Robot Systems

We present a formal tasK AllocatioN and scheduling apprOAch for multi-robot missions (KANOA). KANOA supports two important types of task constraints: task ordering, which requires the execution of several tasks in a specified order; and joint tasks, which indicates tasks that must be performed by more than one robot. To mitigate the complexity of robotic mission planning, KANOA handles the allocation of the mission tasks to robots, and the scheduling of the allocated tasks separately. To that end, the task allocation problem is formalised in first-order logic and resolved using the Alloy model analyzer, and the task scheduling problem is encoded as a Markov decision process and resolved using the PRISM probabilistic model checker. We illustrate the application of KANOA through a case study in which a heterogeneous robotic team is assigned a hospital maintenance mission.

Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction

In order to accomplish diverse tasks successfully in a dynamic (i.e., changing over time) construction environment, robots should be able to prioritize assigned tasks to optimize their performance in a given state. Recently, a deep reinforcement learning (DRL) approach has shown potential for addressing such adaptive task allocation. It remains unanswered, however, whether or not DRL can address adaptive task allocation problems in dynamic robotic construction environments. In this paper, we developed and tested a digital twin-driven DRL learning method to explore the potential of DRL for adaptive task allocation in robotic construction environments. Specifically, the digital twin synthesizes sensory data from physical assets and is used to simulate a variety of dynamic robotic construction site conditions within which a DRL agent can interact. As a result, the agent can learn an adaptive task allocation strategy that increases project performance. We tested this method with a case project in which a virtual robotic construction project (i.e., interlocking concrete bricks are delivered and assembled by robots) was digitally twinned for DRL training and testing. Results indicated that the DRL model’s task allocation approach reduced construction time by 36% in three dynamic testing environments when compared to a rule-based imperative model. The proposed DRL learning method promises to be an effective tool for adaptive task allocation in dynamic robotic construction environments. Such an adaptive task allocation method can help construction robots cope with uncertainties and can ultimately improve construction project performance by efficiently prioritizing assigned tasks.

A Task Allocation Approach of Multi-Heterogeneous Robot System for Elderly Care

Roboticized nursing technology is a significant means to implement efficient elderly care and improve their welfare. Introducing multi-heterogeneous robot systems (MHRS) and sensor networks into a smart home is a promising approach to improve the safety and acceptability of elderly care services in daily life. Among them, the energy consumption and task planning of MHRS determine nursing safety, which is particularly important in the real nursing process. Therefore, we established a novel smart home for elderly care based on seven heterogeneous nursing robots, and proposed a multi-robot task allocation (MRTA) algorithm, considering execution time and energy consumption. The whole system efficiency makes up for the functional limitations and service continuity of traditional MHRS. To realize efficiently conducted multitasks, we established an architecture with centralized task allocation center, robot alliance layer and distributed execution layer for the MHRS. The self-organizing architecture contributes to overall task allocation, communication and adaptive cooperative control between different robots. Then, to clearly describe the continuous nursing process with multiple simultaneous demands and emergency tasks, we modeled the whole nursing process with continuity, multi-priority, and interpretability. A novel MRTA algorithm with a dynamic bidding mechanism was proposed. Comprehensive experiments showed that the proposed algorithm could effectively solve the three key problems of multi-priority tasks, multi-robot safe and adaptive cooperation, and emergency task call in the scene of elderly care. The proposed architecture regarding the smart home could be applied in nursing centers, hospitals, and other places for elderly care.