2D-Span Resampling of Bi-RRT in Dynamic Path Planning

Path planning is an essential task in robot soccer. The purpose of path planning brings a robot to quickly achieve a desired location, showing that the robot has a better chance to shoot or dribble a ball to a goal. Previous work in path planning adopted sensors such as sonars and lasers to obtain local information to avoid obstacles and reach to a goal. By doing this, the robot may move slowly and collide easily with other robots that using similar obstacle-avoidance algorithms. This work proposes a 2D-span resampling method and post processing including pruning and smoothening of bi-directional rapidly-exploring random tree (Bi-RRT) to improve the path route and computational time of path planning. To avoid obstacles, this work re-plans the path by a novel 2D-span resampling method in Bi-RRT. The post processing of pruning unnecessary Bi-RRT nodes and smoothening path route enables a robot to reach a goal with a short path. This simulations validate the proposed method by the performance comparison with several common path-planning methods, showing that it generally has a shorter route distance and less computational time than the other methods

Human-Aware Motion Planning for Safe Human-Robot Collaboration

With the rapid adoption of robotic systems in our daily lives, robots must operate in the presence of humans in ways that improve safety and productivity. Currently, in industrial settings, human safety is ensured through physically separating the robotic system from the human. However, this greatly decreases the set of shared human-robot tasks that can be accomplished and also reduces human-robot team fluency. In recent years, robots with improved sensing capabilities have been introduced and the feasibility of humans and robots co-existing in shared spaces has become a topic of interest. This thesis proposes a human-aware motion planning approach building on RRT-Connect, dubbed Human-Aware RRT-Connect, that plans in the presence of humans. The planner considers a composite cost function that includes human separation distance and visibility costs to ensure the robot maintains a safety distance during motion while being as visible as possible to the human. A danger criterion cost considering two mutually dependent factors, human-robot center of mass distance and robot inertia, is also introduced into the cost formulation to ensure human safety during planning. A simulation study is conducted to demonstrate the planner performance. For the simulation study, the proposed Human-Aware RRT-Connect planner is evaluated against RRT-Connect through a set of problem scenarios that vary in environment and task complexity. Several human-robot configurations are tested in a shared workspace involving a simulated Franka Emika Panda arm and human model. Through the problem scenarios, it is shown that the Human-Aware RRT-Connect planner, paired with the developed HRI costs, performs better than the baseline RRT-Connect planner with respect to a set of quantitative metrics. The paths generated by the Human-Aware RRT-Connect planner maintain larger separation distances from the human, are more visible and also safer due to the minimization of the danger criterion. It is also shown that the proposed HRI cost formulation outperforms formulations from previous work when tested with the Human-Aware RRT-Connect planner

A PRM-based motion planner for dynamically changing environments

This paper presents a path planner for robots operating in dynamically changing environments with both static and moving obstacles. The proposed planner is based on probabilistic path planning techniques and it combines techniques originally designed for solving multiple-query and single-query problems. The planner first starts with a preprocessing stage that constructs a roadmap of valid paths with respect to the static obstacles. It then uses lazy-evaluation mechanisms combined with a single-query technique as local planner in order to rapidly update the roadmap according to the dynamic changes. This allows to answer queries quickly when the moving obstacles have little impact on the free-space connectivity. When the solution can not be found in the updated roadmap, the planner initiates a reinforcement stage that possibly results into the creation of cycles representing alternative paths that were not already stored in the roadmap. Simulation results show that this combination of techniques yields to efficient global planner capable of solving with a real-time performance problems in geometrically complex environments with moving obstacles