Global Path Planning in Grid-Based Environments Using Novel Metaheuristic Algorithm

The global path planning problem is very challenging NP-complete problem in the domain of robotics. Many metaheuristic approaches have been developed up to date, to provide an optimal solution to this problem. In this work we present a novel Quad-Harmony Search (QHS) algorithm based on Quad-tree free space decomposition methodology and Harmony Search optimization. The developed algorithm has been evaluated on various grid based environments with different percentage of obstacle coverage. The results have demonstrated that it is superior in terms of time and optimality of the solution compared to other known metaheuristic algorithms

Global Path Planning in Grid-Based Environments Using Novel Metaheuristic Algorithm

The global path planning problem is very challenging NP-complete problem in the domain of robotics. Many metaheuristic approaches have been developed up to date, to provide an optimal solution to this problem. In this work we present a novel Quad-Harmony Search (QHS) algorithm based on Quad-tree free space decomposition methodology and Harmony Search optimization. The developed algorithm has been evaluated on various grid based environments with different percentage of obstacle coverage. The results have demonstrated that it is superior in terms of time and optimality of the solution compared to other known metaheuristic algorithms

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment