Research on Path Planning of AUV Based on Improved Ant Colony Algorithm

Yan, Shaokun

doi:10.1109/icaibd51990.2021.9458959

Cited by 16 publications

(7 citation statements)

References 3 publications

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“…In recent years, scholars have found that there are related intelligent algorithms available for 3D path planning, such as bio-heuristic intelligence optimization algorithms, neural networks and other algorithms [ 12 ]. Bio-heuristic intelligent optimization algorithms mainly include particle swarm optimization (PSO) [ 13 , 14 ], ant colony optimization (ACO) [ 15 , 16 ], genetic algorithm (GA) [ 17 , 18 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [ 15 ] combined the ACO algorithm and APF algorithm to improve convergence and eliminate local minimum problems. Yan et al [ 16 ] improved the transfer probability and pheromone update strategy to solve the problem of local optimal solution and the slow convergence speed of the traditional ant colony algorithm. Hao et al [ 17 ] proposed an adaptive genetic algorithm to prevent path individuals from falling into the deadlock state during the generation process and reduce the time of global path generation.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Li,

Tang

et al. 2024

Sensors

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In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Li,

Tang

et al. 2024

Sensors

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“…This approach combines global navigation and local path planning, thereby improving the expansion efficiency of random trees [15]. Sharma et al utilized an improved A* algorithm with circular boundaries to ensure the effectiveness of the experimental environment in the presence of static and moving obstacles and changes in ocean currents [16]. However, the lack of consideration for obstacles reduces the applicability of the experiment [17].…”

Section: Introductionmentioning

confidence: 99%

AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology

Hu,

Xiao,

Yang

et al. 2023

Sensors

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In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV’s heading direction, considering its impact on the AUV’s speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV’s navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks.

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“…Because of the intricate nature of multi-constraint conditions and diverse task demands in the problem of collaborative path planning and allocation for multi-UCAV combat, researchers frequently employ meta-heuristics, such as Particle Swarm Optimization (PSO) [ 14 , 15 , 16 ], Grey Wolf Optimizer (GWO) [ 17 , 18 ], the Firefly Algorithm (FA) [ 19 , 20 ], Genetic Algorithms (GAs) [ 21 , 22 ], Differential Evolution (DE) algorithms [ 23 , 24 ], Ant Colony Optimization (ACO) algorithms [ 25 , 26 ], etc., to tackle the relevant computational challenges.…”

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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Research on Path Planning of AUV Based on Improved Ant Colony Algorithm

Cited by 16 publications

References 3 publications

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology

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

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