Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm

Long, Yang; Song, Lixin; Qiu, Da; Li, Changzhen; Guo, Xuan; Shi, Binghua; AbouOmar, Mahmoud S.

doi:10.3390/jmse11030489

Cited by 18 publications

(13 citation statements)

References 25 publications

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“…Long et. al [75] employed the bacterial foraging optimization (BFO) algorithm to address the task of PP in the context of swarm robotics. The BFO algorithm demonstrates a capacity for adapting to dynamic environments, although it may encounter challenges with slow convergence rates, particularly in intricate strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Robot Path Planning through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Zhang,

Zhang

2024

IEEE Access

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The importance of efficient path planning (PP) cannot be overstated in the domain of robots, as it involves the utilization of intelligent algorithms to determine the optimal trajectory for robot to navigate between two given points. The main target of PP is to determine potential trajectories for robot operating in a complex environment containing various obstacles. The implementation of these movements should facilitate robot in traversing a path without encountering any collisions, starting from its initial location and reaching the intended destination. In order to address the challenges associated with robot PP, this study applies the chimp optimization algorithm (CHOA) as a local searching (LS) technique and the evolutionary programming algorithm (EPA) to enhance the potential route discovered via a collection of LSs. In order to address CHOA's tendency to converge to local minima, a new updating technique called twin-reinforced (TR) is developed. In order to assess the effectiveness of the TRCHOA, we conducted a comparative analysis with other widely used meta-heuristic algorithms that are typically employed for solving robot PP problems. Additionally, we included the conventional probabilistic roadmap method (PRM) in our evaluation. We evaluated the planning performances of these algorithms on a standardized set of benchmark problems. Our findings indicate that the TRCHOA outperforms the other algorithms in terms of its planning performance. The evaluation of planning effectiveness encompasses several key criteria, namely path length, consistency of scheduled paths, time complexity, and rate of success. The experiments conducted in this study provide evidence of the statistically significant value of the enhancements obtained through the implementation of the proposed method. The findings derived from the TRCHOA provide compelling evidence of its capacity to accurately determine the most optimal route within the specified test map.INDEX TERMS Chimp optimization algorithm; evolutionary programming; path planning; robot; twinreinforced technique.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Robot Path Planning through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Zhang,

Zhang

2024

IEEE Access

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“…This ensures that the USV does not pose any safety risks to other ships and safeguards its navigation security in public waters. Numerous studies have been conducted by researchers to develop route planning techniques that consider the guidelines set by the COLREGS [24]. In the contemporary era, simulation plays a crucial role in the development of systems across various engineering domains.…”

Section: Introductionmentioning

confidence: 99%

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Hashali,

Yang,

Xiang

2024

JMSE

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This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.

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“…The algorithm's collision avoidance performance, compliance with COLREGS, path feasibility, and optimality are thoroughly discussed and verified through simulation. Long et al [32] introduced a hybrid bacterial foraging optimization algorithm that incorporates a simulated annealing mechanism while considering COLREGS constraints and dynamic obstacle constraints. The effectiveness of the algorithm was evaluated through numerical simulation analysis and computer simulation experiments.…”

Section: Introductionmentioning

confidence: 99%

“…It is easy to fall into local optimization, and difficult to adjust parameters. [17,26,32] It is apparent that existing local path planning methods for ships inadequately consider navigation situation analysis for the target ship in complex multi-ship encounter situations that involve both multiple dynamic and static obstacles. In the entire process of local path planning, there has been relatively little research utilizing logical flow to ensure that collision avoidance strategies align with COLREGS, and it has not received sufficient attention.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning Method for Unmanned Ship Based on Encounter Situation Inference and COLREGS Constraints

Wang,

Wang

et al. 2024

JMSE

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Local path planning, as an essential technology to ensure intelligent ships’ safe navigation, has attracted the attention of many scholars worldwide. In most existing studies, the impact of COLREGS has received limited consideration, and there is insufficient exploration of the method in complex waters with multiple interfering ships and static obstacles. Therefore, in this paper, a generation method for a time–space overlapping equivalent static obstacle line for ships in multi-ship encounter scenarios where both dynamic and static obstacles coexist is proposed. By dynamically inferring ships’ encounter situations and considering the requirements of COLREGS, the influence of interfering ships and static obstacles on the navigation of the target ship at different times in the near future is represented as static obstacle lines. These lines are then incorporated into the scene that the target ship encountered at the path planning moment. Subsequently, the existing path planning methods were extensively utilized to obtain the local path. Compared with many common path planning methods in random scenarios, the effectiveness and reliability of the method proposed are verified. It has been demonstrated by experimental results that the proposed method can offer a theoretical basis and technical support for the autonomous navigation of unmanned ships.

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Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm

Cited by 18 publications

References 25 publications

Enhancing Robot Path Planning through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Enhancing Robot Path Planning through a Twin-Reinforced Chimp Optimization Algorithm and Evolutionary Programming Algorithm

Route Planning Algorithms for Unmanned Surface Vehicles (USVs): A Comprehensive Analysis

Local Path Planning Method for Unmanned Ship Based on Encounter Situation Inference and COLREGS Constraints

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