Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

Abstract

The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.

Hierarchical fuzzy obstacle avoidance control of omnidirectional mobile robot for substation inspection

“…In formula (7) and (8), r represents the turning radius (radius), L is the turning angle determined by the robot, and the time is t . 1 q and 2 q are the angular velocities of the left and right wheels of the robot, and the constraint equations constrain the angular velocities of the two driving wheels [7] .…”

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

“…The velocity of each particle is updated according to the following equation [ 42 , 43 ]: where, represents the inertia coefficient, represents the personal acceleration coefficient and represents the social acceleration coefficient. The position of each particle is updated by the equation [ 43 , 44 ]: where and represents the current and updated vectors, respectively.…”

A novel path planning algorithm for mobile robot in dynamic environments using modified bat swarm optimization

“…The local path‐planning problem calculates the path while the environment of the mobile robot is continuously changing due to its motion. Whereas, in the global path planning, the environment is entirely known in advance and the terrain must be stationary [11, 12]. Many researchers have investigated the path‐planning problem in dynamic environments, for instance, authors of [13] suggested a novel scheme to choose the best route of the mobile robot by the ant colony optimization (ACO) algorithm in an unknown dynamic environment.…”