Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method

Fu, Jun; Lv, Teng; Li, Bao

doi:10.3390/s22103652

Cited by 17 publications

(8 citation statements)

References 26 publications

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“…For example, it is easy for the robot to fall into the local optimum and sometimes cannot reach the target point. When the integrated potential field of the target point is not the minimum value in the whole integrated potential field, the case of an unreachable target point will be generated [ 39 ]. The motion of the robot in the scene is affected by the resultant force formed by the superposition of attractive forces and repulsive forces, and the robot determines the direction and magnitude of the next movement under the action of the resultant force.…”

Section: Methodsmentioning

confidence: 99%

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

Xing

Wang

Ding

et al. 2023

Sensors

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With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices.

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Section: Methodsmentioning

confidence: 99%

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

Xing

Wang

Ding

et al. 2023

Sensors

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“…Furthermore, our decision to use the ACO meta-heuristic for solving the traffic routing problem is supported by experimental results and studies from other researchers in the field. For instance, authors (Bedi et al ., 2007; Di Caprio et al ., 2022; SS et al ., 2020; Jiao et al ., 2018), recommended ACO as the most suitable algorithm to fix traffic routing problems, like the Traveling Salesman Problem (TSP) (Zukhri and Paputungan, 2013; Le and Peechatt, 2019), Vehicle Routing Problem VRP (Bell and McMullen, 2004; Jiang et al ., 2021), path planning and avoiding obstacles for automated vehicles (Wang et al ., 2019), for robots (Liu et al ., 2017; Cong and Ponnambalam, 2009), for Unmanned Aerial Vehicles UAV (Duan et al ., 2009; Chen et al ., 2022), for Autonomous Underwater Vehicle AUV (Mirjalili et al ., 2020) and for submarines (Fu et al ., 2022; Ma et al ., 2020).…”

Section: Related Workmentioning

confidence: 99%

Urban traffic flow management on large scale using an improved ACO for a road transportation system

Boubedra,

Tolba,

Manzoni

et al. 2023

IJICC

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PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

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“…Swarm algorithms and evolutionary algorithms are widely adopted for solving the optimal path planning algorithms with multiple constraints [ 40 , 41 , 42 , 43 , 44 ]. Whale optimization (WO) based path planning for an underwater robot is mentioned in [ 45 ], where the optimization techniques are used for generating a path with safe and minimal fuel consumption.…”

Section: Related Workmentioning

confidence: 99%

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Pathmakumar

Muthugala

Samarakoon

et al. 2022

Sensors

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Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

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Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method

Cited by 17 publications

References 26 publications

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

Urban traffic flow management on large scale using an improved ACO for a road transportation system

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

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