Implementation of path planning with obstacle avoidence using SLAM in Services Robot

Abstract

Obstacle avoidance is an important aspect in service robot navigation. This research proposes an approach that integrates Lidar sensors with Simultaneous Localization and Mapping (SLAM) methods to improve the robot's ability to identify, understand, and avoid obstacles in dynamic environments. This data is then processed using SLAM algorithms to create a real-time map of the environment while estimating the robot's position within it. In this research, lidar is used to detect obstacles to be encountered by the robot. Dynamic environment mapping allows the robot to detect changes and adjust its path plan on the fly. An obstacle avoidance algorithm is designed to interact with SLAM data, so that the robot can adaptively change its movement path to avoid newly appearing or moving obstacles In robotic path planning, Dijkstra's algorithm can be applied to generate the shortest, most efficient route from the robot's current location to a target location. The algorithm operates on a predefined map or a dynamically updated map (as in SLAM), considering obstacles and the cost associated with traversing different parts of the environment. Dijkstra algorithm is chosen to determine the path to be traveled by the robot.Djikstra algorithm is chosen to determine the path to be traveled by the robot. The djikstra algorithm takes the robot through the obstacle barrier well from the starting position to the destination point with excellent.Combining SLAM with Lidar obstacle avoidance improves the robot's robustness in complex and rapidly changing environmental situations. Simulation experiments and field testing show that this approach is effective in improving the robot's performance in dealing with obstacles and optimizing its autonomous navigation. By utilizing Lidar and SLAM technologies, this research contributes to the development of reliable robot navigation systems in various application contexts.