Robot Arm Trajectory Planning Optimization Based on Integration of Particle Swarm Optimization and A* Algorithm

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.

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Task Allocation and Path Planning of a Multi-Robot System Using Heuristic Coupled Particle Swarm Optimization Algorithm

Handbook of Research on Developments and Trends in Industrial and Materials Engineering - Advances in Chemical and Materials Engineering ◽

10.4018/978-1-7998-1831-1.ch009 ◽

2020 ◽

pp. 194-209

Author(s):

Arindam Majumder ◽

Rajib Ghosh

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Optimization Algorithm ◽

Task Allocation ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

A Algorithm ◽

Swarm Optimization ◽

Multiobjective Particle Swarm Optimization ◽

Multi Robot

This study deals with a plant layout where there were ninety predefined locations which have to be inspected by using three multiple robots in such a way that there would not be any collisions between the robots. A heuristic integrated multiobjective particle swarm optimization algorithm (HPSO) is developed for allocating tasks to each robot and planning of path while moving from one task location to another. For optimal path planning of each robot the research utilized A* algorithm. The task allocation for each robot is carried out using a modified multiobjective particle swarm optimization algorithm where the earliest completion time (ECT) inspired technique is used to make the algorithm applicable in multi robot task allocation problems. At the later stage of this study, in order to test the capability of HPSO an instance is solved by the algorithm and is compared with the existing solutions of a genetic algorithm with the A* algorithm. The computational results showed the superiority of the proposed algorithm over existing algorithms.

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A trajectory planning model of tiltrotor considering multi-phase and multi-mode flight

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410019875345 ◽

2019 ◽

Vol 233 (16) ◽

pp. 6019-6031

Author(s):

Jing Zhang ◽

Wu Yu ◽

Xiangju Qu

Keyword(s):

Particle Swarm Optimization ◽

Trajectory Planning ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Model Simulation ◽

Particle Swarm ◽

Planning Model ◽

Swarm Optimization ◽

Multi Phase ◽

Multi Mode

A trajectory planning model of tiltrotor with multi-phase and multi-mode flight is proposed in this paper. The model is developed to obtain the trajectory of tiltrotor with consideration of flight mission and environment. In the established model, the flight mission from take-off to landing is composed of several phases which are related to the flight modes. On the basis of the flight phases and the flight modes, the trajectory planning model of tiltrotor is described from three aspects: i.e. tiltrotor dynamics including motion equations and maneuverability, flight mission requirements, and flight environment including different no-fly zones. Then, particle swarm optimization algorithm is applied to generate the trajectory of tiltrotor online. The strategy of receding horizon optimization is adopted, and the control inputs in the next few seconds are optimized by particle swarm optimization algorithm. Flight mission simulations with different situations are carried out to verify the rationality and validity of the proposed trajectory planning model. Simulation results demonstrate that the tiltrotor flying with multi-mode can reach the target in three cases and can avoid both static and dynamic obstacles.

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