Multi-UAV Path Planning Algorithm Based on BINN-HHO

Li, Sen; Zhang, Ran; Ding, Yuanming; Qin, Xutong; Han, Y. L.; Zhang, Huiting

doi:10.3390/s22249786

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…For drones flying in the air, path planning is used to efficiently adapt to unfamiliar environments, avoid obstacles, and achieve evasion flights [3]. Moreover, path planning is also utilized in scenarios such as aerial photography, search and rescue, and environmental monitoring to plan the most optimal flight paths for drones.…”

Section: Unmanned Aerial Vehicles (Uavs)mentioning

confidence: 99%

Research on Path Planning Method based on Graph Search Algorithm

Shi

2024

HSET

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Path planning, crucial in robotics and autonomous vehicles, involves finding efficient routes from start to finish while avoiding obstacles. Graph search algorithms like BFS, DFS, and A* are commonly used for this purpose. This research focuses on analyzing these algorithms' performance and applicability in diverse scenarios. The study provides a comprehensive overview of path planning's definition, application domains, and fundamental principles. It then delves into the core graph search algorithms: BFS explores nodes layer by layer, ensuring the shortest path is found first but suffering from high time complexity. DFS explores nodes in a depth-first manner, potentially leading to shorter paths but risking getting trapped in local optima. In contrast, A* combines the strengths of BFS and DFS by using heuristic functions to guide the search for the cheapest path to the goal. Despite their utility, graph search algorithms have limitations. The high time complexity of A* in large-scale environments and the potential for local optima are key challenges. To address these, research explores improved methods like GBFS and bidirectional A* search. The future direction of research includes developing more efficient heuristic functions, exploring hybrid algorithms, parallelizing path planning, and applying these methods to new domains like autonomous drones and self-driving cars. This research paves the way for more robust and efficient path planning algorithms in various applications.

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Section: Unmanned Aerial Vehicles (Uavs)mentioning

confidence: 99%

Research on Path Planning Method based on Graph Search Algorithm

Shi

2024

HSET

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“…In addition, since there are usually a variety of threats in flight environments where multiple UAV systems perform tasks, the planning of a safe path that must simultaneously avoid environmental threats and collisions between individual UAVs is a difficult problem [8]. Furthermore, ground-based navigation systems are often unable to detect the precise location and configuration information of all threats in complex environments [9]. Therefore, it is particularly difficult to coordinate the paths of all UAVs involved and adjust their existing paths in a timely and efficient manner according to real-time environmental information [10], especially for some tasks that require accurate arrival time and dynamic conflict avoidance simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field

Guo

Liu

Wei

et al. 2023

Drones

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In this paper, a four-dimensional (4D) dynamic cooperative path planning algorithm for multiple unmanned aerial vehicles (UAVs) is proposed, in which the cooperative time variables of UAVs, as well as conflict and threat avoidance, are considered. The algorithm proposed in this paper uses a hierarchical framework that is divided into a 4D cooperative planning layer and a local threat avoidance planning layer. In the cooperative planning layer, the proposed algorithm, named dynamic priority rapidly exploring random trees (DPRRT*), would be used for the 4D cooperative path planning of all UAVs involved in a given task. We first designed a heuristic prioritization strategy in the DPRRT* algorithm to rank all UAVs to improve the efficiency of cooperative planning. Then, the improved RRT* algorithm with the 4D coordination cost function was used to plan the 4D coordination path for each UAV. Whenever the environment changes dynamically (i.e., sudden static or moving threats), the proposed heuristic artificial potential field algorithm (HAPF) in the local threat avoidance planning layer is used to plan the local collision avoidance path. After completing local obstacle avoidance planning, the DPRRT* of the 4D cooperative planning layer is again called upon for path replanning to finally realize 4D cooperative path planning for all UAVs. The simulation and comparison experiments prove the feasibility, efficiency, and robustness of the proposed algorithm.

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“…Chen et al [ 10 ], on the other hand, achieved kdiff multi-UAV cooperative autonomous path planning in unknown environments, considering the dynamic and partially observable nature of the environmental state. In addition, Li et al [ 11 ] proposed a multi-drone path-planning algorithm to address challenges such as low stability, long planned paths, and low efficiency in dynamically avoiding obstacles in a three-dimensional mountainous environment. Furthermore, Wang et al [ 12 ] introduced a multi-UAV collaborative path-planning method based on attention reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm

Fu,

Yang,

Liu

et al. 2023

Entropy

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The capacity for autonomous functionality serves as the fundamental ability and driving force for the cross-generational upgrading of unmanned aerial vehicles (UAVs). With the disruptive transformation of artificial intelligence technology, autonomous trajectory planning based on intelligent algorithms has emerged as a key technique for enhancing UAVs’ capacity for autonomous behavior, thus holding significant research value. To address the challenges of UAV trajectory planning in complex 3D environments, this paper proposes a multi-UAV cooperative trajectory-planning method based on a Modified Cheetah Optimization (MCO) algorithm. Firstly, a spatiotemporal cooperative trajectory planning model is established, incorporating UAV-cooperative constraints and performance constraints. Evaluation criteria, including fuel consumption, altitude, and threat distribution field cost functions, are introduced. Then, based on its parent Cheetah Optimization (CO) algorithm, the MCO algorithm incorporates a logistic chaotic mapping strategy and an adaptive search agent strategy, thereby improving the home-returning mechanism. Finally, extensive simulation experiments are conducted using a considerably large test dataset containing functions with the following four characteristics: unimodal, multimodal, separable, and inseparable. Meanwhile, a strategy for dimensionality reduction searching is employed to solve the problem of autonomous trajectory planning in real-world scenarios. The results of a conducted simulation demonstrate that the MCO algorithm outperforms several other related algorithms, showcasing smaller trajectory costs, a faster convergence speed, and stabler performance. The proposed algorithm exhibits a certain degree of correctness, effectiveness, and advancement in solving the problem of multi-UAV cooperative trajectory planning.

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Multi-UAV Path Planning Algorithm Based on BINN-HHO

Cited by 9 publications

References 39 publications

Research on Path Planning Method based on Graph Search Algorithm

Research on Path Planning Method based on Graph Search Algorithm

Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field

Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm

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