Modified Particle Swarm Optimization based Path Planning for Multi-Uav Formation

2019 ◽  
Author(s):  
Ankur Patley ◽  
Ashish Bhatt ◽  
Arnab Maity ◽  
Kaushik Das ◽  
Shashi Ranjan Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Multi Uav

scholarly journals Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

2019 ◽  
Vol 9 (13) ◽  
pp. 2621 ◽  
Author(s):  
Zhuang Shao ◽  
Fei Yan ◽  
Zhou Zhou ◽  
Xiaoping Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Kinematic Constraints ◽  
Pythagorean Hodograph ◽  
Curvature Continuity ◽  
Continuous Curvature ◽  
Ph Curve ◽  
Multi Uav

This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.

scholarly journals Robot Arm Path Planning Using Modified Particle Swarm Optimization based on D* algorithm

2017 ◽  
Vol 13 (3) ◽  
pp. 27-37 ◽  
Author(s):  
Ahmed T. Sadiq ◽  
Firas A. Raheem
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Time Interval ◽  
Robot Arm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Cartesian Space ◽  
Quintic Polynomial

Abstract Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization algorithm is proposed to find an optimal path based on the local search, D* and Euclidean distances.  The quintic polynomial equation is utilized to provide a smooth trajectory path. According to the observe results, the modified PSO algorithm is efficiently performs to find an optimal path even in difficult environments.   Keywords: D*, Free Cartesian Space, Path Planning, Particle Swarm Optimization (PSO), Robot Arm.

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