Real-Time Path Planning for IoT-Enabled Autonomous Vehicle Robotics Using RRT and A * Algorithms

Nagendra Kumar Musham; R. Hemnath

doi:10.70454/IJMRE.2021.10701

Authors

Nagendra Kumar Musham Celer Systems Inc,California,USA Author
R.Hemnath Nandha Arts and Science College, Erode, India Author

DOI:

https://doi.org/10.70454/IJMRE.2021.10701

Keywords:

Autonomous Vehicle Navigation, Dynamic Environments, Energy Efficiency, Machine Learning, Obstacle Avoidance, Path Optimization, Real-Time Navigation

Abstract

One of the main purposes of this work is to provide a path planning framework for IoT-enabled autonomous vehicles through the use of RRTs and A*. These were designed to maximize actual real-time navigation and decision-making in very dynamic and complex situations considering obstacles and uncertainties in the environment. In cases that have unknown or nonregular barriers, the RRT algorithm is employed to visualize the environment rapidly to derive an initial feasible path across the configuration space. Following the developments of RRT paths, the A algorithm* will address topics brought about by their construction in order for the route to be smooth, efficient, and have the shortest length. A synergism between the two techniques makes these systems adapt in real time to changes in the environment and in transportation conditions while preserving computational economy. From the performance evaluation, joining the strategy increases these very important parameters, such as the energy consumption, path length, and the time to reach the destination, by a huge percentage. The model consumes energy that is reduced by about 23% in comparison with conventional approaches, decreases path length by 12-15% and decreases time to objective up to 50%. These results indicate that the RRT + A* model works very well to enhance the effectiveness and efficiency of autonomous vehicle navigation in changing conditions. This framework can be used in applications like robotics and autonomous driving, and it represents a viable answer for real-time energy-efficient optimal path planning.

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