Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

Abstract

 The proposed hybrid algorithm outperforms the PSO, IPSO, and GWO algorithms.  The proposed method outperforms class PSO and GWO algorithms in determining the shortest and collision-free path for a mobile robot under the same environmental restrictions.  The performance made the hybrid algorithm more effective in finding the best potential solution.In the mobile robot workplace, the path planning problem is crucial. Robotic systems employ intelligence algorithms to plan the robot's path from one point to another. This paper proposes the fastest and optimal path planning of the wheeled mobile robot with collision avoidance to find the optimal route during wheeled mobile robot navigation from the start point to the target point. It is done using a modern meta-heuristic hybrid algorithm called IPSOGWO by combining Improved Particle Swarm Optimization (IPSO) with Grey Wolf Optimizer (GWO). The principal idea is based on boosting the ability to exploit in PSO with the exploration ability in GWO to the better-automated alignment between local and global search capabilities towards a targeted, optimized solution. The proposed hybrid algorithm tackles two objectives: the protection of the path and the length of the path. During, Simulation tests of the route planning by the hybrid algorithm are compared with individual results PSO, IPSO, and GWO concepts about the minimum length of the path, execution time, and the minimum number of iterations required to achieve the best route. This work's effective proposed navigation algorithm was evaluated in a MATLAB environment. The simulation results indicated that the developed algorithm reduced the average path length and the average computation time, less than PSO by (1%, 1.7%), less than GWO by (1%, 1.9%), and less than IPSO by (0.05%, 0.4%), respectively. Furthermore, the superiority of the proposed algorithm was proved through comparisons with other famous path planning algorithms with different static environments.

Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm

“…The fifth category is multi-fusion-based algorithms, which combine two or more algorithms; these algorithms seek to solve and enhance route planning problems by increasing their quality, stability, and convergence performance [19]. Examples include a probabilistic roadmap based on ant colony optimization [20], improved particle swarm optimization (IPSO), grey wolf optimizer (GWO) [19], a hybrid flower pollination and genetic algorithm (FPA-GA) [21], (BAS-GA) is a combination of the genetic algorithm and the beetle antennae search algorithm [22], and others. Depending on the situation, each of these algorithms has advantages and disadvantages.…”