A Distributed-Centralized Dynamic Task Allocation Algorithm for UAVs Tracking Moving Targets

Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle (UAV) swarms task planning and the key technology to improve autonomy. The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms, the target and environment state, and the high real-time allocation requirements. Hence, dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning. In this work, a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects: the establishment of an allocation model and the solution of the allocation model. First, the basic concept and trigger scenario are introduced. Second, the research status and the advantages and disadvantages of the two allocation models are analyzed. Third, the research status and the advantages and disadvantages of several common dynamic task allocation algorithms, such as the algorithm based on market mechanisms, intelligent optimization algorithm, and clustering algorithm, are evaluated. Finally, the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted, and future research directions are established. This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.

Edge computing (EC) extends the ability of cloud computing to the network edge to support diverse resource-sensitive and performance-sensitive IoT applications. However, due to the limited capacity of edge servers (ESs) and the dynamic computing requirements, the system needs to dynamically update the task allocation policy according to real-time system states. Service migration is essential to ensure service continuity when implementing dynamic task allocation. Therefore, this article investigates the long-term dynamic task allocation and service migration (DTASM) problem in edge-cloud IoT systems where users’ computing requirements and mobility change over time. The DTASM problem is formulated to achieve the long-term performance of minimizing the load forwarded to the cloud while fulfilling the seamless migration constraint and the latency constraint at each time of implementing the DTASM decision. First, the DTASM problem is divided into two subproblems: 1) the user selection problem on each ES and 2) the system task allocation problem. Then, the DTASM problem is formulated as a Markov decision process (MDP) and an approach based on deep reinforcement learning (DRL) is proposed. To tackle the challenge of vast discrete action spaces for DTASM task allocation in the system with a mass of IoT users, a training architecture based on the twin-delayed deep deterministic policy gradient (DDPG) is employed. Meanwhile, each action is divided into a differentiable action for policy training and one mapped action for implementation in the IoT system. Simulation results demonstrate that the proposed DRL-based approach obtains the long-term optimal system performance compared to other benchmarks while satisfying seamless service migration.

On a dynamic and decentralized energy-aware technique for multi-robot task allocation

When it comes to Multi-Robot Systems (MRS), Multi-Robot System Task Allocation (MRTA) is one of the key issues in realizing multi-robot systems for real-world applications. In this paper, we propose an optimization-based Hungarian algorithm to solve the task allocation problem of multi-robot systems. Firstly, each robot is allowed to autonomously evaluate the task cost and send it to the central processor and an equilibrium compensation approach is proposed to compensate the constraints of the algorithm for the cost matrix to be square. Then a task cache pool is used to avoid the problem from degenerating into Single-Robot (SR) task allocation, and a prioritization strategy considering time is also designed to prevent the task points with lower generation values from lingering for a long time in the dynamic task allocation. Through simulation experiments, we demonstrate that the algorithm has a positive effect on the efficiency of assigned tasks in both dynamic and static situations.

In this article, we explore how to optimize task allocation for robot swarms in dynamic environments, emphasizing the necessity of formulating robust, flexible, and scalable strategies for robot cooperation. We introduce a novel framework using a decentralized partially observable Markov decision process (Dec-POMDP), specifically designed for distributed robot swarm networks. At the core of our methodology is the local information aggregation multiagent deep deterministic policy gradient (LIA-MADDPG) algorithm, which merges centralized training with distributed execution. During the centralized training phase, a local information aggregation (LIA) module is meticulously designed to gather critical data from neighboring robots, enhancing decision-making efficiency. In the distributed execution phase, a strategy improvement method is proposed to dynamically adjust task allocation based on changing and partially observable environmental conditions. Our empirical evaluations show that the LIA module can be seamlessly integrated into various centralized training and decentralized execution (CTDE)-based multiagent reinforcement learning (MARL) methods, significantly enhancing their performance. Additionally, by comparing LIA-MADDPG with six conventional reinforcement learning algorithms and a heuristic algorithm, we demonstrate its superior scalability, rapid adaptation to environmental changes, and ability to maintain both stability and convergence speed. These results underscore LIA-MADDPG's outstanding performance and its potential to significantly improve dynamic task allocation in robot swarms through enhanced local collaboration and adaptive strategy execution.

This paper discusses the use of cognitive models as augmented metacognition on task allocation for tasks requiring visual attention. In the domain of naval warfare, the complex and dynamic nature of the environment makes that one has to deal with a large number of tasks in parallel. Therefore, humans are often supported by software agents that take over part of these tasks. However, a problem is how to determine an appropriate allocation of tasks. Due to the rapidly changing environment, such a work division cannot be fixed beforehand: dynamic task allocation at runtime is needed. Unfortunately, in alarming situations the human does not have the time for this coordination. Therefore system-triggered dynamic task allocation is desirable. The paper discusses the possibilities of such a system for tasks requiring visual attention.KeywordsVisual attentioncognitive modelingaugmented metacognition

The advancement of UAV technology makes the use of UAVs more and more widespread, and the swarm is the main mode of UAV applications owing to its robustness and adaptability. Meanwhile, task allocation plays an essential role in a swarm to obtain overall high performance and unleash the potential of each UAVs owing to the complexity of the large-scale swarm. In this paper, we pay attention to the real-time allocation problem of dynamic tasks. We design models for the task assigning problem to construct the constraints model and assigning objectives. In addition, we introduce a novel agent-based allocating mechanism based on the auction process, including the design for three kinds of agents and the cooperation mechanism among different agents. Moreover, we proposed a new algorithm to calculate the bidding values of UAVs, by which the messages passed between UAVs can be reduced. On the basis of the assigning mechanism, we put up with a novel agent-based real-time task allocation algorithm named NECTAR for dynamic tasks in the UAV swarm. Furthermore, we conduct extensive experiments to evaluate the performance of our NECTAR, and the results indicate that NECTAR is able to solve the real-time task allocation for dynamic tasks and achieve high performance of the UAV swarm.

Unanimous action to achieve specific goals is crucial for the success of a robotic swarm. This requires clearly defined roles and precise communication between the robots of a swarm. An optimized task allocation algorithm defines the mechanism and logistics of decision-making that enable the robotic swarm to achieve such common goals. With more nodes, the traffic of messages that are required to communicate inside the swarm relatively increases to maintain decentralization. Increased traffic eliminates real-time capabilities, which is an essential aspect of a swarm system. The aim of this research is to reduce execution time while retaining efficient power consumption rates. In this research, two novel decentralized swarm communication algorithms are proposed, namely Clustered Dynamic Task Allocation–Centralized Loop (CDTA-CL) and Clustered Dynamic Task Allocation–Dual Loop (CDTA-DL), both inspired by the Clustered Dynamic Task Allocation (CDTA) algorithm. Moreover, a simulation tool was developed to simulate different swarm-clustered communication algorithms in order to calculate the total communication time and consumed power. The results of testing the proposed CDTA-DL and CDTA-CL against the CDTA attest that the proposed algorithm consumes substantially less time. Both CDTA-DL and CDTA-CL have achieved a significant speedup of 75.976% and 54.4% over CDTA, respectively.

Use of neural networks to achieve dynamic task allocation: a flexible manufacturing system example

In collective robotics, researchers are successfully using models derived from swarm insect behavior to solve problems like coordination or task allocation. It is often assumed that in a homogeneous group of agents, every agent has to become more complicated when the complexity of the task increases, which decreases simplicity of design and robustness. Role diversification and therefore task specialization within a group may help counter the need for more complex agents. Because experiments on self-organization and dynamic task allocation in robot populations focused mainly on homogeneous groups, the relation between these models and pre-specified role diversification remains mainly unknown. In this work the interchangeability of homogeneous and heterogeneous agent populations is investigated. It is shown that in a simple simulated environment, a mixed population of specialized agents can not be easily substituted by a homogeneous group of multi-tasking agents. Results lead to the conclusion that the ability of dynamic task switching, i.e. adaptive task allocation in respect to changes in the environment, have strong effects on the behavior on a population level. Although a pre-defined heterogeneous group can produce the same result for a given environment and a specific population composition, the group behavior differs when the environment changes

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.

In accordance with the allocation of random arriving tasks, queuing network is proposed and then utilized to establish combat model for air-to-ground attack of formation. With the methodology of Markov Decision Processes (MDP), the paper establishes the dynamic task assignment model under the complete information condition. Allowing for the issue of dynamic task allocation with incomplete information, load threshold and value function sharing mechanism are proposed. Afterwards, two layers Q-learning algorithm is presented in this context to establish the dynamic task allocation model, which aims to achieve the cooperation with in formation and between formations. The simulation result indicates that the model aforementioned can enhance the long-term profit value, as well as avoid omitting the target with bigger income. Furthermore, it sheds light on the study on the dynamic task allocation and combat effectiveness evaluation for air-to-ground attack of formation problem in theory and method.

Automation in operator assistant systems requires accurate functional analysis and decomposition to allow allocation of tasks between the human operator and the system. In most cases, a dynamic task allocation is needed in which the level of automation (or system autonomy) is dependent on changing operator needs in a dynamic operational environment. This paper provides a step-by-step concept for automation. Candidate tasks for automation are identified by their specific control and display interface characteristics. Control and display characteristics of each task will then lead to a hierarchical decomposition of task levels. The relationship between these levels is that the operator will only pay full attention to the next level once all tasks allocated to the previous one are handled adequately. This hierarchy is translated into a framework of different classes of automation and operator assistance.

The run-time strategy for allocating the application tasks to platform resources in homogeneous Networks-on-Chip (NoCs) is enhanced with respect to minimising the communication energy consumption. As novel contribution, the entity Life-Time (LT) is incorporated to a node of an application characteristics graph (ACG), used for describing an incoming application. Several algorithms are then modified for solving the task allocation problem, while minimizing the communication energy consumption and network contention. If Life-Time(LT) is taken into consideration, it is observed that after each allocation, the nodes having lower value of life-time contains more available neighbors, compared to the existing solution, hence helps in allocation of an application as the system load increases, whereas the existing solution may fail to allocate the new application onto the current system configuration. Again, a node(used to describe a task in an application) belonging to an ACG is deallocated when its Life-Time expires which results in minimizing the communication energy consumption.

An experiment is described that aimed at evaluating a principle of dynamic task allocation (DTA) of conflict resolution between aircraft in air-traffic control on a large scale simulator. It included three cognitive agents: the radar controller (RC), in charge of conflict detection and resolution; the planning controller (PC), in charge of entry-exit coordination and of workload regulation; and a conflict resolution computer system (SAINTEX), able to manage only simple conflicts. Within this three-agent paradigm, three conditions were compared: (a) a control condition (without computer assistance); (b) an explicit condition (PC and RC in charge of the allocation); and (c) an assisted explicit condition (SAINTEX proposed allocations which could be changed by PC). Comparisons were made on the basis of a detailed cognitive analysis of verbal protocols. The more the assistance, the more anticipative the mode of operation in controllers and the easier the human-human cooperation (HHC). These positive effects of the computer support are interpreted in terms of decreased workload and increased shared information space. In addition, the more the controllers felt responsible for task allocation, the more they criticized the machine operation.