Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm

Qian, Qingwen; Wu, Jian; Wang, Zhe

doi:10.1007/s00779-019-01216-1

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…Initial position of the robot is set to (0, 0), and the coordinates of the target is (9,9), the moving step of the robot is set to 0.1. When the distance between the robot and the target is less than 0.4, we regard that the robot reaches the target and stops, the path planning task finishes.…”

Section: Simulation Environment Constructionmentioning

confidence: 99%

“…Recently, with the development of the artificial intelligence technology, more and more nature inspired intelligent control algorithms have been employed in the path planning of the mobile robot, such as particle swarm optimization algorithm [9], fuzzy logic control [10], neural network [11][12][13], and so on. Among these algorithms, the fuzzy logic control method has achieved extensive applications in path planning of the mobile robots, due to its nice characteristics such as without needing the precise mathematical model of the controlled system, strong robustness and fault tolerance [10,14,15].…”

Section: Introductionmentioning

confidence: 99%

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Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

Wang

Chen

Zhang

et al. 2020

Artif Life Robotics

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Path planning in dynamic environment is a great challenge for mobile robot. A large number of approaches have been used to deal with it. Since the neural network algorithm has the ability to find the optimal solution at high speed and self-learning function, it has achieved extensive applications in the path planning tasks. Considering that the optimization performance of the neural network heavily depends on the quality of the training sample, this paper proposes a novel way to provide the training samples for the neural network. Work space of the robot is divided into two parts: global safe area and local dangerous area. In the global safe area, the robot only receives the attraction force from the target and it moves towards the target directly. In the dangerous area, except the attraction force, the robot also receives the repulsion force from the obstacle(s). The repulsion force and the angle between the obstacle and the target (origin of the coordinate is in the position of the robot) are used to be the inputs of the fuzzy inferencing system, and the deflection angle of the robot is the output. The final moving direction of the robot is determined by summing this deflection angle and the direction of the attraction force. The coordinates of the target and obstacle, and the moving direction of the robot corresponding to this position relationship, constitute the training samples for the neural network. Benefited from the precise moving direction obtained by the fuzzy artificial potential field algorithm, the neural network gets excellent path optimization ability. Simulation and physical experiment results demonstrate the potential of the proposed algorithm.

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Section: Simulation Environment Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

Wang

Chen

Zhang

et al. 2020

Artif Life Robotics

View full text Add to dashboard Cite

show abstract

“…Path planning is one of the main technologies for ensuring a drone's smooth flight and safe and successful obstacle avoidance [3]. A* algorithm [4], genetic algorithm [5], particle swarm optimization [6], ant colony optimization [7], RRT algorithm [8], velocity obstacle [9,10], reinforcement learning method [11][12][13], artificial potential field method [14][15][16][17][18][19][20][21][22], and others are examples of commonly used path planning methods.…”

Section: Introductionmentioning

confidence: 99%

UAV Path Planning Based on Improved Artificial Potential Field Method

Hao

Huang

et al. 2023

Aerospace

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The obstacle avoidance system of a drone affects the quality of its flight path. The artificial potential field method can react quickly when facing obstacles; however, the traditional artificial potential field method lacks consideration of the position information between drones and obstacles during flight, issues including local minima, unreachable targets, and unreasonable obstacle avoidance techniques that lengthen flight times and consume more energy get encountered. Therefore, an improved artificial potential field method is proposed. First, a collision risk assessment mechanism was introduced to avoid unreasonable obstacle avoidance actions and reduce the length of unmanned aerial vehicle flight paths. Then, to solve the problem of local minimum values and unreachable targets, a virtual sub-target was set up and the traditional artificial potential field model was modified to enable the drone to avoid obstacles and reach the target point. At the same time, a virtual sub-target evaluation factor was set up to determine the reasonable virtual sub-target, to achieve a reasonable obstacle avoidance path compared to the traditional artificial potential field method. The proposed algorithm can plan a reasonable path, reduce energy consumption during flight, reduce drone turning angle changes in the path, make the path smoother, and can also be applied in complex environments.

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“…Te particle swarm optimization (PSO) [8] is an intelligent algorithm that boasts several advantages, including a simple structure, few parameters, easy implementation, and fast convergence. However, the algorithm also has limitations such as low search accuracy, poor population diversity, and convergence towards the local optimum [9]. Existing literature shows that intelligent algorithms used for path planning often sufer from limitations such as low accuracy, poor diversity, and the local optimum.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

Yang,

Li,

Zhang

et al. 2023

Journal of Robotics

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To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.

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Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm

Cited by 10 publications

References 19 publications

Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

UAV Path Planning Based on Improved Artificial Potential Field Method

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

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