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

Alhaqbani, Amjaad; Kurdi, Heba

doi:10.3390/rs13010027

Cited by 34 publications

(11 citation statements)

References 65 publications

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“…Unlike mathematical optimization algorithms, heuristic algorithms use simple and efficient iterative processes with greedy rules to optimize mission planning schemes. In [16], a bio-inspired heuristic algorithm based on the schooling and foraging behaviors of a fish swarm was proposed, where the UAVs were adaptively aggregated into multiple groups to perform search and rescue missions. Considering the suffered combat threat of the UAV cluster, Zhen et al propose an ant colony optimization algorithm to solve the cooperative search-attack planning problem, where the UAV was modeled as a Dubins vehicle [17].…”

Section: Artificial Intelligencementioning

confidence: 99%

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

Zuo

Gao

et al. 2022

Remote Sensing

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Large Unmanned Aerial Vehicle (UAV) clusters, containing hundreds of UAVs, have widely been used in the modern world. Therein, mission planning is the core of large UAV cluster collaborative systems. In this paper, we propose a mission planning method by introducing the Simple Attention Model (SAM) into Dynamic Information Reinforcement Learning (DIRL), named DIRL-SAM. To reduce the computational complexity of the original attention model, we derive the SAM with a lightweight interactive model to rapidly extract high-dimensional features of the cluster information. In DIRL, dynamic training conditions are considered to simulate different mission environments. Meanwhile, the data expansion in DIRL guarantees the convergence of the model in these dynamic environments, which improves the robustness of the algorithm. Finally, the simulation experiment results show that the proposed method can adaptively provide feasible mission planning schemes with second-level solution speed and that it exhibits excellent generalization performance in large-scale cluster planning problems.

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Section: Artificial Intelligencementioning

confidence: 99%

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

Zuo

Gao

et al. 2022

Remote Sensing

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“…According to (13) Based on the role-task matching mechanism, transforms its role according the updated and then it performs the new task that matches the updated role.…”

Section: Individualindividual Interactionmentioning

confidence: 99%

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Zhan

et al. 2021

J. of Syst. Eng. Electron.

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Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed selforganizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

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“…Decentralized algorithms based on market-based approaches [16]- [18], bio-nspired approaches [19], [20], and consensus algorithms [21] have been proposed to address the issues associated with centralized approaches. However, most of the decentralized approaches assume the communication is reliable with unlimited bandwidth, or they assume the agents are communicating with the agents in a fully connected network during the task allocation [22].…”

Section: Introductionmentioning

confidence: 99%

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

2022

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This paper presents a deep reinforcement learning framework in which agents learn how to schedule and censor themselves amongst the other agents competing for access to a limited communication medium. In particular, the process learns to schedule the communication between agents to improve the performance of task allocation in environments with constrained communication in terms of limited bandwidth and message collision. The proposed approach, called Communication-Aware Consensus-Based Bundle Algorithm (CA-CBBA), extends the previous CBBA [1] in that the learned communication policy enables efficient utilization of the shared medium by prioritizing agents with messages that are important for the mission. Furthermore, agents in denser parts of the network are censored appropriately to alleviate the message collision and hidden node problems. We evaluate our approach in various task assignment scenarios, and the results show that CA-CBBA outperforms CBBA in terms of convergence time, rate of conflict resolution, and task allocation reward. Moreover, we show that CA-CBBA yields a policy that generalizes beyond the training set to handle larger team sizes. Finally, the results on time-critical problems, such as a search-and-rescue mission, show that CA-CBBA also outperforms the baselines considered (e.g., CBBA, MCDGA [2], and ACBBA [3]) in terms of number of unassigned and conflicted tasks in most of the scenarios.

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

Cited by 34 publications

References 65 publications

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

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