A Scalable Tree Based Path Planning for a Service Robot

Path planning plays a vital role in a mobile robot navigation system. It essentially generates the shortest traversable path between two given points. There are many path planning algorithms that have been proposed by researchers all over the world; however, there is very little work focussing on path planning for a service environment. The general assumption is that either the environment is fully known or unknown. Both cases would not be suitable for a service environment. A fully known environment will restrict further expansion in terms of the number of navigation points and an unknown environment would give an inefficient path. Unlike other environments, service environments have certain factors to be considered, like user-friendliness, repeatability, scalability, and portability, which are very essential for a service robot. In this paper, a simple, efficient, robust, and environment-independent path planning algorithm for an indoor mobile service robot is presented. Initially, the robot is trained to navigate to all the possible destinations sequentially with a minimal user interface, which will ensure that the robot knows partial paths in the environment. With the trained data, the path planning algorithm maps all the logical paths between all the destinations, which helps in autonomous navigation. The algorithm is implemented and tested using a 2D simulator Player/Stage. The proposed system is tested with two different service environment layouts and proved to have features like scalability, trainability, accuracy, and repeatability. The algorithm is compared with various classical path planning algorithms and the results show that the proposed path planning algorithm is on par with the other algorithms in terms of accuracy and efficient path generation.