scholarly journals Particle swarm optimization algorithms with selective differential evolution for AUV path planning

2020 ◽  
Vol 9 (2) ◽  
pp. 94
Author(s):  
Hui Sheng Lim ◽  
Shuangshuang Fan ◽  
Christopher K.H. Chin ◽  
Shuhong Chai ◽  
Neil Bose
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Differential Evolution ◽  
Computational Efficiency ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computational Requirement ◽  
Path Planner

Particle swarm optimization (PSO)-based algorithms are suitable for path planning of the Autonomous Underwater Vehicle (AUV) due to their high computational efficiency. However, such algorithms may produce sub-optimal paths or require higher computational load to produce an optimal path. This paper proposed a new approach that improves the ability of PSO-based algorithms to search for the optimal path while maintaining a low computational requirement. By hybridizing with differential evolution (DE), the proposed algorithms carry out the DE operator selectively to improve the search ability. The algorithms were applied in an offline AUV path planner to generate a near-optimal path that safely guides the AUV through an environment with a priori known obstacles and time-invariant non-uniform currents. The algorithm performances were benchmarked against other algorithms in an offline path planner because if the proposed algorithms can provide better computational efficiency to demonstrate the minimum capability of a path planner, then they will outperform the tested algorithms in a realistic scenario. Through Monte Carlo simulations and Kruskal-Wallis test, SDEAPSO (selective DE-hybridized PSO with adaptive factor) and SDEQPSO (selective DE-hybridized Quantum-behaved PSO) were found to be capable of generating feasible AUV path with higher efficiency than other algorithms tested, as indicated by their lower computational requirement and excellent path quality.

scholarly journals Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

2019 ◽  
Vol 52 (21) ◽  
pp. 315-322 ◽  
Author(s):  
Hui Sheng Lim ◽  
Shuangshuang Fan ◽  
Christopher K.H. Chin ◽  
Shuhong Chai ◽  
Neil Bose ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Swarm Optimization

scholarly journals A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 99 ◽  
Author(s):  
Haiyan Wang ◽  
Zhiyu Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robots ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Elastic Particle

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

scholarly journals UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yudong Zhang ◽  
Lenan Wu ◽  
Shuihua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Minimum Probability ◽  
Chaotic Particle Swarm Optimization

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

scholarly journals A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haoqian Huang ◽  
Chao Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Synthesis Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Feedback Mechanism ◽  
Swarm Optimization ◽  
Learning Mechanism

In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.

scholarly journals A Real-Time Path Planning Algorithm for AUV in Unknown Underwater Environment Based on Combining PSO and Waypoint Guidance

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 20 ◽  
Author(s):  
Zheping Yan ◽  
Jiyun Li ◽  
Yi Wu ◽  
Gengshi Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Time Path ◽  
Optimization Parameters ◽  
Planning Algorithm

It is a challengeable task to plan multi-objective optimization paths for autonomous underwater vehicles (AUVs) in an unknown environments, which involves reducing travel time, shortening path length, keeping navigation safety, and smoothing trajectory. To address the above challenges, a real-time path planning approach combining particle swarm optimization and waypoint guidance is proposed for AUV in unknown oceanic environments in this paper. In this algorithm, a multi-beam forward looking sonar (FLS) is utilized to detect obstacles and the output data of FLS are used to produce those obstacles’ outlines (polygons). Particle swarm optimization is used to search for appropriate temporary waypoints, in which the optimization parameters of path planning are taken into account. Subsequently, an optimal path is automatically generated under the guidance of the destination and these temporary waypoints. Finally, three algorithms, including artificial potential field and genic algorithm, are adopted in the simulation experiments. The simulation results show that the proposed algorithm can generate the optimal paths compared with the other two algorithms.

UAV Path Planning Based on Particle Swarm Optimization with Global Best Path Competition

2018 ◽  
Vol 32 (06) ◽  
pp. 1859008 ◽  
Author(s):  
Chen Huang ◽  
Jiyou Fei
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Competition Strategy ◽  
Swarm Optimization ◽  
Evaluation Functions ◽  
Planning Methods

Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.

scholarly journals An Improved Simulated Annealing Particle Swarm Optimization Algorithm for Path Planning of Mobile Robots Using Mutation Particles

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jianzhang Lu ◽  
Zhihao Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Path Planning ◽  
Mobile Robots ◽  
Human Life ◽  
Optimal Path ◽  
Particle Swarm ◽  
Autonomous Driving ◽  
Pso Algorithm ◽  
Swarm Optimization

Artificial intelligence technology has brought tremendous changes to human life and production methods. Mobile robots, UAVs, and autonomous driving technology have gradually entered people’s daily life. As a typical issue for a mobile robot, the planning of an optimal mobile path is very important, especially in the military and emergency rescue. In order to ensure the efficiency of operation and the accuracy of the path, it is crucial for the robot to find the optimal path quickly and accurately. This paper discusses a new method and MP-SAPSO algorithm for addressing the issue of path planning based on the PSO algorithm by combining particle swarm optimization (PSO) algorithm with the simulated annealing (SA) algorithm and mutation particle and adjusting the parameters. The MP-SAPSO algorithm improves the accuracy of path planning and the efficiency of robot operation. The experiment also demonstrates that the MP-SAPSO algorithm can be used to effectively address path planning issue of mobile robots.

A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaohuan Liu ◽  
Degan Zhang ◽  
Ting Zhang ◽  
Jie Zhang ◽  
Jiaxu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Research Direction ◽  
Swarm Optimization ◽  
Optimal Position ◽  
Content Type ◽  
Set Operation

PurposeTo solve the path planning problem of the intelligent driving vehicular, this paper designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO).Design/methodology/approachFirst, the authors optimized the hyper-parameters of RL to make it converge quickly and learn more efficiently. Then the authors designed a pre-set operation for PSO to reduce the calculation of invalid particles. Finally, the authors proposed a correction variable that can be obtained from the cumulative reward of RL; this revises the fitness of the individual optimal particle and global optimal position of PSO to achieve an efficient path planning result. The authors also designed a selection parameter system to help to select the optimal path.FindingsSimulation analysis and experimental test results proved that the proposed algorithm has advantages in terms of practicability and efficiency. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.Originality/valueThe authors designed a pre-set operation to reduce the participation of invalid particles in the calculation in PSO. And then, the authors designed a method to optimize hyper-parameters to improve learning efficiency of RL. And then they used RL trained PSO to plan path. The authors also proposed an optimal path evaluation system. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

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