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A multi-objective path planning method for ships based on constrained policy optimization

OCEAN ENGINEERING 2025年 319卷

作者： Zhu, Man Kong, Mian Wen, Yuanqiao Gu, Shangding Xue, Bai Huang, Tao Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Wuhan Univ Technol State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China Tech Univ Munich Sch Computat Informat & Technol D-85748 Munich Germany Chinese Acad Sci Key Lab Syst Software Beijing 100190 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China

With development of technologies in ships, path planning plays a crucial role in ensuring efficient and safe navigation. However, traditional single-objective path planning methods suffer from inefficiency, and are prone to getting trapped in local optima. As for the traditional multi-objective path planning methods, challenges may also be faced for conflicts among objectives. To address these challenges, this study formulated the path planning problem as a multi-objective optimization model. Constrained policy optimization (CPO) offers key advantages by effectively handling constraints, optimizing and balancing objectives, and ensuring safety. Thus, this study proposed a multi-objective path planning method based on CPO to explore pareto-optimal paths. Results from four environments with different types and distributions of obstacles demonstrated that CPO-based method outperforms NSGA-II-based one and MOEA/D-based one in both path length and energy consumption. CPO-based method achieves the shortest path (261.89 m) compared to NSGA-II-based one (269.13 m) and MOEA/D-based one (267.83 m), and has the lowest energy consumption (1.10 x 106 J), significantly lower than NSGA-II-based one (5.32 x 106 J) and MOEA/D-based one (6.15 x 106 J). The ability of CPO-based method to maintain optimal Pareto fronts and to adapt to various weight settings established it as an advantageous method in complex environments.

关键词： multi-objective path planning MASS Safe reinforcement learning Constrained policy optimization

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An enhanced artificial bee colony algorithm with self-learning optimization mechanism for multi-objective path planning problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 149卷

作者： Ye, Fan Duan, Peng Meng, Leilei Sang, Hongyan Gao, Kaizhou Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Taipa 999078 Macao Peoples R China

In recent years, path planning has been one of the most concerned problems in mobile robotics. This study investigates a multi-objective path planning problem focused on minimizing path length and maximizing path safety. Based on the characteristics of this problem, a mathematical model is established, and then an enhanced artificial bee colony algorithm is proposed to solve this problem. In the proposed algorithm, a new hybrid initialization strategy is designed to generate a high-quality initial population. In the employed bee phase, in addition to the crossover and mutation operators, two objective-oriented evolutionary operators are developed. In the onlooker bee phase, two self-learning optimization mechanisms are applied to the non-dominated and dominated individuals, respectively. Specifically, the collaborative-based optimization mechanism is designed to improve the quality of the non-dominated individuals. The dominance-guide optimization mechanism is developed to guide the dominated individuals to learn from the non-dominated ones. In the scout bee phase, a novel individual-restart strategy that considers the useful information of global best solutions is investigated, which increases the proposed algorithm's exploration ability. Finally, the proposed algorithm is compared with five state-of-the-art algorithms on sixteen instances from four representative environments. Simulation results show that the proposed algorithm achieved average improvements of 2.60% and 90.77% on the hypervolume and inverted generational distance metrics, respectively, compared with the algorithm with the second-best performance. These demonstrate the effectiveness and high performance of the proposed algorithm for solving multi-objective path planning problems in terms of both population diversity and solution quality.

关键词： Mobile robot multi-objective path planning Enhanced artificial bee colony algorithm objective-oriented evolutionary operator Self-learning optimization mechanism Individual-restart strategy

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multi-objective path planning for unmanned surface vehicle with currents effects

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ISA TRANSACTIONS 2018年 75卷 137-156页

作者： Ma, Yong Hu, Mengqi Yan, Xinping Wuhan Univ Technol Sch Nav 1178 Heping Rd Wuhan 430063 Hubei Peoples R China Hubei Key Lab Inland Shipping Technol 1178 Heping Rd Wuhan 430063 Hubei Peoples R China Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Wuhan Univ Technol Natl Engn Res Ctr Water Transport Safety 1178 Heping Rd Wuhan 430063 Hubei Peoples R China

This paper investigates the path planning problem for unmanned surface vehicle (USV), wherein the goal is to find the shortest, smoothest, most economical and safest path in the presence of obstacles and currents, which is subject to the collision avoidance, motion boundaries and velocity constraints. We formulate this problem as a multi-objective nonlinear optimization problem with generous constraints. Then, we propose the dynamic augmented multi-objective particle swarm optimization algorithm to achieve the solution. With our approach, USV can select the ideal path from the Pareto optimal paths set. Numerical simulations verify the effectiveness of our formulated model and proposed algorithm. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Unmanned surface vehicle multi-objective path planning Currents Dynamic augmented multi-objective particle swarm optimization

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multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第5期55卷 1838-1854页

作者： Zhang, De Luo, Run Yin, Ye-bo Zou, Shu-liang Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Hunan Peoples R China Hunan Prov Key Lab Emergency Safety Technol & Equi Hengyang 421001 Hunan Peoples R China Univ South China Sch Comp Sci Hengyang 421001 Hunan Peoples R China Univ South China Sch Resource Environm & Safety Engn 28 Changsheng West Rd Hengyang 421001 Peoples R China

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A* (IACO-A*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.(c) 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： multi-objective path planning Radioactive environment Mobile robot Ant colony optimization A*

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multi-objective path planning for UAV in the Urban Environment Based on CDNSGA-II 13

Multi-Objective Path Planning for UAV in the Urban Environme...

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13th IEEE International Conference on Service-Oriented System Engineering (SOSE) / 10th International Workshop on Joint Cloud Computing (JCC) / IEEE International Workshop on Cloud Computing in Robotic Systems (CCRS)

作者： Ren, Qian Yao, Yuan Yang, Gang Zhou, Xingshe Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (数字)9781728114422

ISBN: (纸本)9781728114422

This paper is concerned with path planning for unmanned aerial vehicles (UAVs) in urban environment. Distance is the fundamental goal of path planning. Besides, safety is indispensable for the riskless UAV flight. In this paper, we propose a multi-objective path planning (MOPP) approach based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find an optimal collision-free path for UAV, considered both distance and safety. To this aim, firstly, we represent urban environment by octree, which is an efficient and hierarchical data structure for spatial subdivision in 3D. Meanwhile, safety index map (SIM) are developed to capture obstacles in the geography map. Then, a CDNSAG-II methods proposed to deal with MOPP problem. The experimental results demonstrate that our approach is able to find an optimal path efficiently under urban environment based on octrees.

关键词： UAV multi-objective path planning NSGA-II octrees urban environment

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Solving the multi-objective path planning problem for mobile robot using an NSGA-II

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Duan, Peng Yu, Zhenao Gao, Kaizhou Meng, Leilei Han, Yuyan Ye, Fan Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Taipa 999078 Macao Peoples R China

path planning is one of the most concerned problems in mobile robotics. Given the NP -hard nature of path planning problems, the non -dominated sorting genetic algorithm (NSGA-II), as one of the outstanding evolutionary algorithms with robust optimization capabilities, is a good candidate to deal with them. In this study, an improved NSGA-II (INSGA-II) is proposed to solve the multi -objective path planning problems, in which path length, path safety and path smoothness are optimized simultaneously. First, a grid -based environment model is created, and each feasible solution is encoded as a discrete ordered point set. Second, a hybrid initialization strategy is employed to enrich the diversity of initial population. Third, besides the usual selection, crossover and mutation operators, problem -specific evolutionary operators are developed. In addition, an adaptive variable neighborhood local search strategy is designed to increase the exploitation ability of the algorithm. Moreover, a hybrid global search strategy is applied to further improve the algorithm's the exploration ability. Finally, the proposed INSGA-II is compared with six state-of-the-art algorithms on 16 instances of four representative maps. Simulation results indicate that INSGA-II can solve the multi -objective path planning problems with high efficiency.

关键词： Mobile robot multi-objective path planning INSGA-II Adaptive variable neighborhood local search Hybrid global search

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Study of multi-objective path planning method for vehicles

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第3期27卷 3257-3270页

作者： Zheng, Yan Chun Wang, Juan Guo, Dong Zhang, Hai Li, Chao Chao Li, Dong Chun Li, Hui Ming Li, Kai Shandong Univ Technol Sch Management Zibo Peoples R China Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo Peoples R China

To address the problems associated with single-objective path planning, herein, we propose a multi-objective path planning method that takes into account emissions, fuel consumption, and time as travel demands. First, a fuel consumption and emission estimation model, suitable for coupling with a dynamic transportation network, is established, and then, the multi-objective path planning model is presented. In addition, the analytic hierarchy process (AHP) and gray relational analysis are used to derive relative priorities of each sub-objective according to driver preference. The above two models are combined with an improved Dijkstra algorithm to perform multi-objective path planning based on dynamic time-dependent road conditions. Finally, the method is verified using real-world vehicle experiments on actual roads. Compared to single-objective path planning, the multi-objective planning method meets travel needs and can save time and fuel, thereby providing greater environmental protection while still considering driver preferences.

关键词： Urban traffic multi-objective path planning Vehicle emissions Travel demand City planning Environment

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A comparative study on Ant Colony Optimization algorithm approaches for solving multi-objective path planning problems in case of unmanned surface vehicles

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OCEAN ENGINEERING 2022年 255卷

作者： Ntakolia, Charis Lyridis, Dimitrios, V Natl Tech Univ Athens Lab Maritime Transport GR-15780 Zografos Greece Hellen AF Acad Dept Aeronaut Studies Sect Mat Engn Machining Technol & Prod Management Acharnes 13672 Greece

The increased use of unmanned surface vehicles (USVs) to various applications in complex environments has led to the need of novel path planning approaches that can effectively address multi-objective path planning problems. Ant Colony Optimization (ACO) is a well-known and popular path search algorithm for graph-based problems that finds the shortest path. It has been widely used for solving path planning problems for robotic vehicles, and unmanned aircraft and surface vehicles, among others. To this end, this study focuses on extending the use of ACO to cope with multiple objectives by using fuzzy inference systems, such as Mamdani and Takagi-Sugeno-Kang (TSK), or the root mean square error (RMSE) criterion adopted to the state-of-the-art SIGPA algorithm. A comparative evaluation of these approaches is conducted in the context of solving different scenarios of multi-objective USV path planning problems. The results showed that ACO with Mamdani reached better performance in terms of solution quality compared to the other approaches under examination, while ACO with RMSE presented higher convergence speed and ACO with TSK balanced better among convergence speed and solution quality. Thus, each of the proposed approaches can be considered for multi-objective path planning of USVs depending the application needs.

关键词： Ant colony optimization multi-objective path planning Unmanned surface vehicles Fuzzy logic

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A multi-objective path planning method for the wave glider in the complex marine environment

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OCEAN ENGINEERING 2022年 264卷

作者： Zhang, Shuai Sang, Hongqiang Sun, Xiujun Liu, Fen Zhou, Ying Yu, Peiyuan Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Adv Mechatron Equipment Technol Tianjin 300387 Peoples R China Ocean Univ China Phys Oceanog Lab Qingdao 266100 Peoples R China Ocean Univ China Inst Adv Ocean Study Qingdao 266100 Peoples R China

Aiming to energy-efficient, short path, and safety, an online dynamic multi-objective path planning method for the wave glider (WG) is proposed to make the WG operate efficiently in the uncertain marine environment. In this paper, a novel kinematic constrained safe rapidly-exploring random tree (KS-RRT*) path planning algorithm is developed, which is subject to the safe area boundaries and kinematics. Biased random sampling method is introduced which the kinematic constraints is used as heuristic function and the KS-RRT* can reduce the extended area of random points. And a path safe (PS) scheme is developed, which can ensure that the WG safely passes through the planned path. In addition, energy cost, path length, and safety metric (SM) of each feasible trajectory are obtained by the KS-RRT*, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve multi-objective path optimization problem, and the final path is smoothed by the B -spline algorithm. The results of simulations show that the proposed KS-RRT* is more suitable for the WG's path planning than traditional path planning algorithm and the proposed multi-objective path optimization method can solve the path planning problem of the WG in complex marine environments.

关键词： Wave glider multi-objective path planning Optimization Kinematic constrained safe Rapidly-exploring random tree (KS-RRT*) Technique for order Preference by similarity to ideal situation (TOPSIS)

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multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms

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SOFT COMPUTING 2013年第7期17卷 1283-1299页

作者： Ahmed, Faez Deb, Kalyanmoy Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India Aalto Univ Sch Econ Dept Informat & Serv Econ Helsinki 00100 Finland

A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm-a well-known soft computing approach. Four different path representation schemes that begin their coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance and maximization of path safety are considered as objectives of this study while path smoothness is considered as a secondary objective. This study makes an extensive analysis of a number of issues related to the optimization of path planning task-handling of constraints associated with the problem, identifying an efficient path representation scheme, handling single versus multiple objectives, and evaluating the proposed algorithm on large-sized grids and having a dense set of obstacles. The study also compares the performance of the proposed algorithm with an existing GA-based approach. The evaluation of the proposed procedure against extreme conditions having a dense (as high as 91 %) placement of obstacles indicates its robustness and efficiency in solving complex path planning problems. The paper demonstrates the flexibility of evolutionary computing approaches in dealing with large-scale and multi-objective optimization problems.

关键词： multi-objective path planning Potential field path length path safety path smoothness NSGA-II Genetic algorithms

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