Smooth and Safe Path Planning based on Probabilistic Foam for Autonomous Robotic Systems
Probabilistic Foam, Path Planning, Assistive Robotics, Path Safety, Path Smoothing.
Planning a path for a robot to navigate between two points in a given environment and avoiding colliding with obstacles is one of the main issues for autonomous robotics. The search for short paths and reduced search time are aspects of most planning methods, but for applications where the robots interact directly with human beings, such as assistive robotics, ensuring a greater degree of safety in movements is a fundamental requirement. In this context, this Ph.D. thesis presents a set of new strategies for the planning of safe paths for autonomous robots. The methods developed are fundamentally based on the concepts of the Probabilistic Foam Method (PFM). PFM is a sampling-based path planning method capable of generating paths bounded by a set of connected bubbles, which guarantees a volumetric region in the free space for safe maneuverability. In order to compute the bubbles, PFM requires an explicit representation of the obstacles region in the configuration space, which is computationally impracticable considering its application for most problems. Thus, we applied a new strategy to compute bubbles without representing these obstacles regions. Besides, we present an analysis to reduce the number of PFM parameters. To improve the quality of the paths, two optimization procedures were implemented in order to reduce the path length and increase the path smoothness, maintaining the high clearance. New variants of PFM were developed to explore different mechanisms for the propagation of the foam, to ensure the planning of shorter paths, with short searching time, and guaranteeing paths with high clearance. In order to demonstrate the main contributions of this Ph.D. thesis, some simulated experiments were performed, considering the path planning for two assistive robots: The first one is a lower limbs exoskeleton, with the tasks of overcoming obstacles, walking up and down a stair, resulting in smooth movements, with a more anthropomorphic pattern. These results illustrate the ability of PFM to plan safe and smooth paths for open kinematic-chain robots without the explicit representation of the obstacles region in configuration space. The Smart Walker was the second robot considered in this work. It was possible to illustrate the planning of safe paths for a mobile robot with differential drive in addition to showing some aspects of the paths planned by the new methods developed.