Motion planning is an important domain since its performance can significantly affect the utilization of robots. This paper addresses our work to developing a path planner for wheeled a mobile robot using a swarm Intelligence technique for optimal path planning within a short computational time to get better path planning results. Through this technique, we developed particle swarm optimization (PSO) for generating fast and optimal path planning. Inertia weight technique is used for performance comparison of PSO Algorithms to get optimal path planning within a complex environment, PSO with a time-varying mechanism for the inertia weight values (TV-IWPSO), to analyze the performance proposed approach on the of PSO algorithm performance. Finally, the comparison has been done in between TV-IWPSO with both particle swarm optimization with constant inertia weight (B-PSO), and standard particle swarm optimization (S-PSO), in two different maps to performing analysis for algorithms through various environments. The simulation results, which carried out using Matlab 2018a showed that the PSO algorithm with inertia-weight strategy made good results for generating optimal path planning and efficiently than (S-PSO) and (B-PSO) in terms of path distance, execution-time

Motion planning is an important topic for researchers working in the field of autonomous robots, it finds an optimal feasible path from start to target point with avoiding the collision, this paper aims to improve motion planning of mobile robot by particle swarm optimization as a method for finding the collision-free optimal path. The objectives considered in this research for optimization are optimal static navigation path with taking into consideration the affect population size on performance for the algorithm to find the optimal path through various environments with population sizes 100, 80, 40, 20. The simulate and evaluate the proposed algorithm proved no strong affected to population size parameter on the optimal path length and its points, hence we can use a small population size for the minimum time in finding the optimal path between start point to goal point with colliding avoidance.

This paper discusses our research in developing a track planner for a mobile robot using a swarm intelligence technique for optimal track planning in a short computational time to achieve better results in track planning. Through this technique, we proposed grey wolf optimization (GWO) for generating fastest and optimal path planning. this paper introduces an algorithm for rapid and global motion preparation for a mobile robot in a complex environment with static obstacles. the performing analysis for GWO algorithm was evaluated in two different maps. Finally, A comparative study was evaluated between the algorithm built and the other algorithm exist, the simulation results, which carried out using Matlab 2018a showed that the GWO algorithm made results for generating optimal path planning and efficiently in terms of path distance, execution-time.

 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.

There are serious environmental problems in all countries of the world, due to the waste material such as crushed clay bricks (CCB) and in huge quantities resulting from the demolition of buildings. In order to reduce the effects of this problem as well as to preserve natural resources, it is possible to work on recycling (CCB) and to use it in the manufacture of environmentally friendly loaded building units by replacing percentages in coarse aggregate by volume. It can be used as a powder and replacing of percentages in cement by weight and study the effect on the physical and mechanical properties of the concrete and the masonry unit. Evaluation of its performance through workability, dry density, compressive strength, thermal conductivity, and absorption test, and the experimental results obtained confirmed the possibility of using the recycling of clay bricks waste as aggregates instead of natural aggregates and reducing the weight, as well as recycling clay bricks waste and using it as a powder. It contains suitable pozzolanic that can be used as a supplementary cement material that reduces the cement content in concrete used to produce load-bearing units.