PTG-based reactive navigation

PTG-based Reactive Navigation

Initially, we addressed the problem of reactively driving a kinematically-constrained, any-shape mobile robot in a planar scenario. This problem requires finding movements that approach the target location while avoiding obstacles and fulfilling the robot kinematic restrictions. Our main contribution is related to the process of detecting free-space around the robot, which is the basis for a reactive navigator to decide the best instantaneous motor command.



For this task, we propose a PTG-based reactive navigator that reduces the dimensionality of the Configuration Space (C-Space) from 3D (xyφ) to 2D, incorporating in the transformation the geometrical and kinematical constraints of the robot. The robot thus becomes a free-flying point over this 2D manifold embedded in C-Space and the collision avoidance problem is easier and faster to solve.

This dimensional reduction is accomplished by restricting the robot motion to one of a set of parametric path models which are compliant with the robot kinematics. The user can define and utilize as many path models as they wish, overcoming the limitation of moving along circular arcs. To choose the best among all the possible motions, we define an objective function that trades off several navigational criteria such (as?) the collision-free distance, the expected deviation from? the target, the difference between the new (tentative) and the previous motion commands (to soften the robot motion), etc.

Our approach was tested with two sets of experiments. In the first one, the PTG-based obstacle avoidance method is compared against the more common usage of only one circular path model (with the robot Sancho). The second set was carried out with the robotic wheelchair SENA, and demonstrated safe navigation in a crowded and dynamic area (the entrance of our building at the University of Malaga), with dozens of students walking amid the robot path. 


The code is available online within the MRPT: Reactive Navigation App



Remote Optimal, Adaptive Control of Mobile Robots with Non-Deterministic Components