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

Abstract

This paper describes the path planning of an autonomous mobile robot using an advanced version of the swarm bat algorithm in a time-varying situation. The foremost objective of this present contribution is to acquire the shortest path between the initial and endpoint avoiding dynamic obstacles. In this work, an adaptation of the frequency parameter of the standard bat algorithm to develop an improved version known as the modified frequency bat (MFB) algorithm is proposed. The modified frequency bat algorithm works in two steps, in the first step, the robot path is generated when there are no obstacles in the environment using the proposed method. As soon as an obstacle is detected near the robot, the second mode is initiated to avoid any obstacle. Extensive simulations are performed to validate the effectiveness of the proposed modified frequency bat algorithm by implementing it on standard benchmark functions and path planning problems and simultaneously matching its performance with the original bat algorithm. It has been concluded that the modified frequency bat algorithm shows superior performance as compared to the standard bat algorithm by drawing a shorter and collision-free path for the mobile robot path-planning problem.

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

“…where the parameter β i is a random number that increases through time for every individual bat and producing low-frequency pulses at the beginning periods of the searching task. These frequency pulses increase through time to enhance the global hunting convergence, 46 r is a random number (0,1), t is iteration index, ρ = 0.01 (in our work), z * is the current best position solution, obtained by comparing all m competing solutions. For exploitation phase, when a point is nominated among the present best points, a new point is produced for every individual bat locally using a random walk…”

Bio-inspired methods for planning the path of mobile robots

“…Однією з основних переваг алгоритму кажана є швидкість його виконання, завдяки чому він знайшов широке використання для планування шляху АМР [44][45][46].…”

Nonlinear Robust Control of Unknown Robot Manipulator Systems With Actuators and Disturbances Using System Identification and Integral Sliding Mode Disturbance Observer

“…In particular, neural network based methods are proposed in [20][21][22] and adaptive fuzzy control methods are studied in [23][24][25]. The combination of neural network and adaptive fuzzy control methods are also designed in [26], [27] and swarm optimization based method is proposed in [28]. In order to obtain the robustness in the intelligent control methods, neural network-based SMC methods are developed in [29][30][31].…”

“…In addition, because precise values of the inertial parameters of the links constituting the manipulator cannot be readily obtained, the resulting overall robot manipulator dynamics are complicated and difficult to obtain. Several studies ( [20][21][22][23][24][25][26][27][28][29]) have considered a few of these issues and are based on model-based control methods in which nominal manipulator dynamics are still required. Although the adaptive decentralized manipulator control methods proposed in [33], [34] do not require the manipulator dynamics for control, and the resulting residual dynamics are considered as uncertainties, the effects of the uncertainties are significant in this case; thus, the uncertainties cannot be sufficiently compensated by SMC and DOBs, as will be verified in the experimental results of this study.…”

“…Although existing control methods for robot manipulators ( [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]) do not include the actuator dynamics in the robot manipulator dynamics, they must be included during the controller design owing to their significant impact on the performance of the manipulator system. The actuator dynamics have been considered in the manipulator control in [20][21][22][23][24][25][26][27][28][29], which, however, require knowledge of the manipulator dynamics, actuator dynamics, frictional forces, and unknown parameters constituting the manipulator, which cannot be readily obtained. Accordingly, these issues cannot be resolved by using the existing manipulator control methods.…”