Performance analysis of fast marching-based motion planning for autonomous mobile robots in ITER scenarios

Operations of transportation in cluttered environments require robust motion planning algorithms. Specially with large and heavy vehicles under hazardous operations of maintenance, such as in the ITER, an international nuclear fusion research project. The load transportation inside the ITER facilities require smooth and optimized paths with safety margin of 30 cm. The transportation is accomplished by large rhombic-like vehicles to exploit its kinematic capabilities. This paper presents the performance analysis of a motion planning algorithm to optimize trajectories in terms of clearance, smoothness and execution time in cluttered scenarios. The algorithm is an upgraded version of a previous one used in ITER, replacing the initialization implemented using Constrained Delaunay Triangulation by the Fast Marching Square. Exhaustive simulated experiments have been carried out in different levels of ITER buildings, comparing the performance of the algorithm using different metrics.This work was supported by the TECHNOFUSION R&D program funded by the Community of Madrid, project DPI2010-17772 funded by the Spanish Ministry of Science. IST activities received financial support from “Fundação para a Ciência e Tecnologia” through project Pest-OE/SADG/LA0010/2013. The views and opinions expressed herein do not necessarily reflect those of the European Commission.Publicad

Fast Marching Methods in path and motion planning: improvements and high-level applications

Mención Internacional en el título de doctorPath planning is defined as the process to establish the sequence of states a system must go through in order to reach a desired state. Additionally, motion planning (or trajectory planning) aims to compute the sequence of motions (or actions) to take the system from one state to another. In robotics path planning can refer for instance to the waypoints a robot should follow through a maze or the sequence of points a robotic arm has to follow in order to grasp an object. Motion planning is considered a more general problem, since it includes kinodynamic constraints. As motion planning is a more complex problem, it is often solved in a two-level approach: path planning in the first level and then a control layer tries to drive the system along the specified path. However, it is hard to guarantee that the final trajectory will keep the initial characteristics. The objective of this work is to solve different path and motion planning problems under a common framework in order to facilitate the integration of the different algorithms that can be required during the nominal operation of a mobile robot. Also, other related areas such as motion learning are explored using this framework. In order to achieve this, a simple but powerful algorithm called Fast Marching will be used. Originally, it was proposed to solve optimal control problems. However, it has became very useful to other related problems such as path and motion planning. Since Fast Marching was initially proposed, many different alternative approaches have been proposed. Therefore, the first step is to formulate all these methods within a common framework and carry out an exhaustive comparison in order to give a final answer to: which algorithm is the best under which situations? This Thesis shows that the different versions of Fast Marching Methods become useful when applied to motion and path planning problems. Usually, high-level problems as motion learning or robot formation planning are solved with completely different algorithms, as the problem formulation are mixed. Under a common framework, task integration becomes much easier bringing robots closer to everyday applications. The Fast Marching Method has also inspired modern probabilistic methodologies, where computational cost is enormously improved at the cost of bounded, stochastic variations on the resulting paths and trajectories. This Thesis also explores these novel algorithms and their performance.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Antonio Giménez Fernández.- Vocal: Isabel Lobato de Faria Ribeir

Motion planning using fast marching squared method

Robotic motion planning have been, and still is, a very intense research field. Many problems have been already solved and even real-time, optimal motion planning algorithms have been proposed and successfully tested in real-world scenarios. However, other problems are not satisfactory solved yet and also new motion planning subproblems are appearing. In this chapter we detail our proposed solution for two of these problems with the same underlying method: non-holonomic planning and outdoor motion planning. The first is characterized by the fact that many vehicles cannot move in any direction at any time (car-like robots). Therefore, kinematic constrains need to be taken into account when planning a new path. Outoor motion planning focuses on the problem that has to be faced when a robot is going to work in scenarios with non-flat ground, with different floor types (grass, sand, etc.). In this case the path computed should take into account the capabilities of the robot to properly model the environment. In order to solve these problems we are using the Fast Marching Square method, which has proved to be robust and efficient in the recent past when applied to other robot motion planning subproblems.Publicad

Fast Marching in motion planning for rhombic like vehicles operating in ITER

Abstract — Rhombic like vehicles are characterized for high maneuverability in cluttered environments. This type of vehicles will be used on remote handling operations of maintenance in the International Thermonuclear Experimental Reactor (ITER). Previous work was done in motion planning using Constrained Delaunay Triangulation for rhombic like vehicles operating in ITER. This paper shows that the integration of Fast Marching Square improves the motion planning methodology, decreasing also the computational effort, which can be applied not only in ITER but also in other complex and cluttered environments. Simulated results are presented comparing the initial and the improved motion planning. I

13th Annual Review of Progress in Applied Computational Electromagnetics at the Naval Postgraduate School, Monterey, CA, March 17-21, 1997, Conference Proceedings Volumes I & II

Includes Volumes 1 &