Adaptive workspace modeling, using regression methods, and path planning to the alternative guide of mobile robots in environments with obstacles

Abstract

To carry out the navigation task starting with the information given by a sensor, an algorithm has been developed to model environments and plan the path that has to be tracked. It is capable of searching alternative routes if an obstacle has been detected, and chooses the optimal path, tracking smoothly the trajectory, without sudden variations in the robot orientation and movement. The necessary information is received as tuples of X-Z coordinates from an infrared detector, capable of sampling hundreds of points with an aperture angle of 100/spl deg/. Then, the tuples are ordered by the Z coordinate, and making use of regression methods, the object contours in the scene are modeled. Also the environment borders are detected as line segments. The number detected can be modified, so the model fits well into the environment using different granularity. It is possible to attain higher precision allowing an increase in the processing time. Once the environment is modeled, all the possible alternative trajectories are calculated to avoid the obstacles detected and reach the goal point. Those trajectories are computed and stored as cubic polynomial splines, using four reference systems to avoid impossible tracking through an intermediate point. For the case where the goal cannot be reached with a unique spline, the whole path is divided into several trajectories, joining them in the optimal point with the best orientation.