Self-Exploration of Automated System under Dynamic Environment

Exploring an unknown place autonomously is a challenge for robots, especially when the environment is changing. Moreover, in real world application, efficient path planning is crucial for autonomous vehicles to have timely response to execute a collision-free motion. In this paper we focus on environment exploration which enables an automated system to establish a map of an unknown environment with unforeseen objects moving within it. We introduce an exploration package that enables robots self-exploration with an online collision avoidance planner. The package consists of exploration module, global planner module and local planner module. We modularize the package so that developers can easily make modifications or even substitutions to some modules for their specific application. In order to validate the algorithm, we designed and built a robot car as a low cost validation platform to test the autonomous vehicle algorithms in the real world. The car has a 22.36 x 11.65 x 7.6 inches, 4X4 brush-less short course truck chassis, which has a dynamic model similar to a passenger car, but in a scaled pattern. An NVIDIA Xavier GPU (Ubuntu 18.04) and VESC board are mounted on the chassis which provide computation power and speed control capability. A proportional integral derivative (PID) speed controller and an open loop steering controller is used for the low level control module.