Smart Optimal Path Planning Algorithm for Mobile Robot Navigation in an Indoor Environment

Abstract

Mobile robot has the primary function of navigating in the environment; however, navigation is not an easy task to be performed. The navigation relies on various factors such as path length minimization, smooth trajectory, low computation load, environmental constraints, path planning techniques, etc. Path planning term is defined as finding the optimal path from starting point to the destination for the reliable navigation of mobile robot. The optimality of the path not only depends upon finding the obstacles free path, but it also includes other significant parameters such as time efficiency, path length minimization, lesser number of operations, low time and space complexity and smooth trajectory etc. To incorporate these stated parameters, researchers have developed various path planning algorithms such as A*, Bidirectional A*, D*, RRT (Rapidly-exploring Random Trees), RRT*, Ant Colony Optimization based, Particle Swarm Optimization, etc. But the optimality in path planning is not achieved because each algorithm has some limitations such as high number of operations, lengthminimization problem, sharp turns and curves, high computational load, time and space complexity, etc. To address these issues and to achieve the optimality in path planning, this research proposes several improved variants of the A* algorithm. The first variant proposed is the A** algorithm, which selects the shortest path with a smooth trajectory to reach the destination by reducing the number of sharp turns. The algorithm is evaluated through experimental analysis, where it is observed that the proposed A** algorithm reduces the sharp turn in the trajectory to 13.25% with respect to the conventional A* algorithm, resulting in an increase in the acceleration of mobile robots by 5.7% (cm/sec2) and reducing the time to reach the destination by 15.1 seconds. This technique is found to be applicable for various mobile robot applications such as path planning/shortest path selection, autonomous navigation, trajectory planning, etc. The second variant proposed is the Time Optimized A* (TOA*) algorithm, which is a modified bidirectional path planning technique that always chooses the time-efficient and shortest path with a lesser number of operations to reach the destination from the start position. The proposed TOA* algorithm is tested in various simulated tests and mobile robot experiments, where a significant reduction in execution time and number of operations is observed compared to conventional A*, Breadth First Search, and Jump Point Search algorithms. Additionally, a significant reduction of 31.33% in the number of closed nodes and 14.08% reduction in sharp turns has been achieved through simulated experiments. The third variant proposed is the implementation of Adaptive Bidirectional A* (ABA*) algorithm along with new strategy of Flexible Controlling Points technique (FCP). The proposed technique is implemented in various trials and it is experimentally obtained that the number of collisions are reduce to 100% and by implementing the new FCP technique the number of sharp turns are decreases to zero that resulted in reduction in the time lag by 38% with respect to conventional approaches. The proposed techniques are quite a good solution for the fast and reliable autonomous navigation of mobile robot by selecting the smoother trajectories for navigation. In conclusion, this thesis proposes three variants of the A* algorithm that address the limitations of the conventional A* algorithm and achieve optimality in path planning for mobile robotics. The proposed algorithms are evaluated through experimental analysis, where they are found to be effective in reducing the number of sharp turns, execution time, and number of operations, resulting in a faster, smoother, and more reliable path planning for mobile robots.