Robot Path Planning Based on Random Coding Particle Swarm Optimization

Abstract

Abstract-Mobile robot navigation is to find an optimal path to guide the movement of the robot, so path planning is guaranteed to find a feasible optimal path. However, the path planning problem must be solve two problems, i.e., the path must be kept away from obstacles or avoid the collision with obstacles and the length of path should be minimized. In this paper, a path planning algorithm based on random coding particle swarm optimization (RCPSO) algorithm is proposed to get the optimal collision-free path. Dijstra algorithm is applied to search a suboptimal collision-free path in our algorithm; then the RCPSO algorithm is developed to tackle this optimal path planning problem in order to generate the global optimal path. The crossover operator of genetic algorithm and random coding are introduced into the particle swarm optimization to optimize the location of the sub-optimal path. The experiment results show that the proposed method is effective and feasible compared with different algorithms.

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

“…This makes it difficult using FL method to address path planning problems in unstructured environments without combining it with other methods. Despite the strength of good optimization capacity of GA, it is difficult for GA to scale well with complex scenarios and it is characterized with convenience at local minima and oscillation problems [239,240]. Also, due to its complex principle, it may be difficult to deal with dynamic data sets to achieve good results [233].…”

“…Though PSO is described to be simple with less computing time requirement and effective in implementing with varied optimization problems with good results [192][193][194][195], it is difficult to deal with trapping into local minima problems under complex map [194,240]. Although ACO is noted for fast convergence with optimal results [233,234], it requires a lot of computing time and it is difficult to determine the parameters which affects obtaining quick convergence [233,240]. Compared to conventional algorithms, memetic algorithm is described to possess the ability to produce faster convergence and good result; however, it can result in premature convergence [235].…”

“…Possibly, hybrid approaches that possess the ability to take advantage of the benefits and reduce the limitations of each of the methods involved can be considered to obtain [232] (ii) Can help tune the rule base of fuzzy logic [232] (i) Efficiency of neural controllers deteriorates as the number of layers increase [232] (ii) Difficult to acquire its required large dataset of the environment during training to achieve best results. (iii) Using BP easily results in local minima problems [238] (iv) It has a slow convergence speed [232] Genetic Algorithm Simulation (i) Good at solving problems that are difficult to deal with using conventional algorithms and it can combine well with other algorithms (ii) It has good optimization ability (i) Difficult to scale well with complex situations (ii) Can lead to convergence at local minima and oscillations [239,240] (iii) Difficult to work on dynamic data sets and difficult to achieve results due to its complex principle [233] Ant Colony Simulation (i) Good at obtaining optimal result [233] (ii) Fast convergence [234] (i) Difficult to determine its parameters which affects obtaining quick convergence [240] (ii) Require a lot of computing time [233] Particle Swarm Optimization Simulation (i) Faster than fuzzy logic in terms of convergence [132] (ii) Simple and does not require much computing time hence, effective to implement with optimization problems [192][193][194][195] (iii) Performs well on varied application problems [193] (i) Difficult to deal with trapping into local minima under complex map [194,240] (ii) Difficult to generalize its performance since undertaken experiments relied on objects in polygon form [240] Fuzzy Logic…”

Middle Node Optimization algorithm for global optimal path planning

“…3) Random Co ding PSO: Random Coding PSO (RCPSO) [8] is an extension ofthe above explained PSO algorithm. In this algorithm, random coding and genetic algorithm 's crossover operator are introduced to the concept of particle swanns.…”

Impact of External Disturbance and Discontinuous Input on the Redundant Manipulator Robot Behaviour using the Linear Parameter Varying Modelling Approach

“…It has been an important progress several tasks like in planning [4], [5], design [6] and control [7], [8]. It gives more feasibility to robotic mechanisms because of the existence of several solutions in the specified workspace.…”