Real-Time Obstacle Avoidance Method based on Polar Coordination Particle Swarm Optimization in Dynamic Environment

Hao, Yong; Zu, Wei; Zhao, Yuxin

doi:10.1109/iciea.2007.4318681

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…Therefore, the sensors can detect the change of environmental information at any time, and the change has no influence on obstacle avoidance path planning. From Figure 2, the rolling window view is the dashed circle including the detected effective information of nine grids (g=13, 14,15,21,22,23,29,30,31).…”

Section: Problem Formulationmentioning

confidence: 99%

“…To solve the problem of path planning, some modern intelligent path planning algorithms, such as genetic algorithm (GA) [5][6][7], simulated annealing (SA) [8,9], neural network (NN) [10][11][12], particle swarm optimization (PSO) [13,14], and ant colony optimization (ACO) [15][16][17][18], are adopted. Among those approaches, the ACO is the only meta-heuristic approach inspired by the behaviour of the biological ants in real world.…”

Section: Introductionmentioning

confidence: 99%

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Route Optimization for Autonomous Container Truck Based on Rolling Window

Huang

Zheng

2016

International Journal of Advanced Robotic Systems

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Route optimization for autonomous container truck is one of the key problems to realize the automatic container port. An environment model for container truck is built by grid method. Considering the complex and unknown construction environment of the container port, an improved ant colony optimization (IACO) algorithm based on rolling window is proposed to achieve path planning for container truck. In the simulations, it is obvious that the IACO will not only achieve a safe collision avoidance path, the length of which is shorter than the truck's traditional route no matter how complex the environment is, but also show good analytical and disposing ability of dead ends in the path planning process. Compared with conventional ant colony optimization (ACO), the running time of IACO is shorter. The results of the simulation experiments demonstrate that the IACO is a good method which is applicable to route optimization for autonomous container truck.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Route Optimization for Autonomous Container Truck Based on Rolling Window

Huang

Zheng

2016

International Journal of Advanced Robotic Systems

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“…In addition, in a very cluttered environment, the RRT may fail to find a path. 3 Methods based on intelligent computing like ant colony algorithm (ACO) 4 , evolutionary algorithm (EA) 5 and particle swarm optimization (PSO) 6 have strong search capabilities. However, the algorithm performance degrades with environmental complexity and may fall into local minima.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system

Wang

Lyu

Yao

et al. 2015

Chinese Journal of Aeronautics

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This paper proposes a method for planning the three-dimensional path for low-flying unmanned aerial vehicle (UAV) in complex terrain based on interfered fluid dynamical system (IFDS) and the theory of obstacle avoidance by the flowing stream. With no requirement of solutions to fluid equations under complex boundary conditions, the proposed method is suitable for situations with complex terrain and different shapes of obstacles. Firstly, by transforming the mountains, radar and anti-aircraft fire in complex terrain into cylindrical, conical, spherical, parallelepiped obstacles and their combinations, the 3D low-flying path planning problem is turned into solving streamlines for obstacle avoidance by fluid flow. Secondly, on the basis of a unified mathematical expression of typical obstacle shapes including sphere, cylinder, cone and parallelepiped, the modulation matrix for interfered fluid dynamical system is constructed and 3D streamlines around a single obstacle are obtained. Solutions to streamlines with multiple obstacles are then derived using weighted average of the velocity field. Thirdly, extra control force method and virtual obstacle method are proposed to deal with the stagnation point and the case of obstacles' overlapping respectively. Finally, taking path length and flight height as sub-goals, genetic algorithm (GA) is used to obtain optimal 3D path under the maneuverability constraints of the UAV. Simulation results show that the environmental modeling is simple and the path is smooth and suitable for UAV. Theoretical proof is also presented to show that the proposed method has no effect on the characteristics of fluid avoiding obstacles. ª 2015 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.

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“…Hao et al 14 apply the polar coordination particle swarm optimization (PPSO) algorithm to search for a global optimal path based on the static obstacle information, when the robot moves along the path, an online real-time path planning strategy is adopted to avoid dynamic obstacles by means of predicting the future positions of moving obstacles. Li et al 15 present an improved artificial potential field algorithm-based simultaneous forward search method for autonomous mobile robot path planning in partially known, unknown, and dynamic complex environments.…”

Section: Introductionmentioning

confidence: 99%

Rapid path planning algorithm for mobile robot in dynamic environment

Zhang

2017

Advances in Mechanical Engineering

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Searching the lowest-cost path through a graph is central to many problems, including path planning for a mobile robot. By combining Dijkstra's algorithm, A* algorithm, and rolling window principle, a new rapid path planning algorithm for a mobile robot in dynamic environment is proposed. First, Dijkstra's algorithm is applied to find an initial path from the initial state to the goal. As a robot moves along the path, if a possible collision is predicted, a local optimal target state within the detection range of the sensors is selected using the rolling window principle. Then, an optimal path from the robot's current location to this local target is searched through A* algorithm and a new path which leads the robot to move from current location to the goal is obtained. Compared to other algorithms, such as ant colony optimization algorithm, A* algorithm, and D* algorithm, the proposed algorithm can always find an optimal path during re-planning and at the meantime greatly reduce the re-planning time. The simulation results prove the feasibility and effectiveness of the proposed algorithm.

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Real-Time Obstacle Avoidance Method based on Polar Coordination Particle Swarm Optimization in Dynamic Environment

Cited by 26 publications

References 5 publications

Route Optimization for Autonomous Container Truck Based on Rolling Window

Route Optimization for Autonomous Container Truck Based on Rolling Window

Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system

Rapid path planning algorithm for mobile robot in dynamic environment

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