Name
Abstract | ||
|---|---|---|
Path deformation is a technique that was intro- duced to generate robot motion wherein a path, that has been computed beforehand, is continuously deformed on-line in response to unforeseen obstacles. In an effort to improve path deformation, this paper presents a trajectory deformation scheme. The main idea is that by incorporating the time dimen- sion and hence information on the obstacles' future behaviour, quite a number of situations where path deformation would fail can be handled. The trajectory represented as a space-time curve is subject to deformation forces both external (to avoid collision with the obstacles) and internal (to maintain trajectory feasibility and connectivity). The trajectory deformation scheme has been tested successfully on a planar robot with double integrator dynamics and a car-like vehicle. Index Terms— Autonomous navigation; Motion deformation; Collision avoidance; Dynamic environments. I. I NTRODUCTION Where to move next?is a key question for an autonomous robotic system. This fundamental issue has been largely addressed in the past forty years. Many motion determination strategies have been proposed (see (1) for a review). They can broadly be classified into deliberative versus reactive strate- gies: deliberative strategies aim at computing a complete motion all the way to the goal, whereas reactive strategies determine the motion to execute during the next few time- steps only. Deliberative strategies have to solve a motion planning problem. They require a model of the environment as complete as possible and their intrinsic complexity is such that it may preclude their application in dynamic environments. Reactive strategies on the other hand can operate on-line using local sensor information: they can be used in any kind of environment whether unknown, changing or dynamic, but convergence towards the goal is difficult to guarantee. To bridge the gap between deliberative and reactive ap- proaches, a complementary approach has been proposed based upon motion deformation. The principle is simple: a complete motion to the goal is computed first using a priori information. It is then passed on to the robotic system for execution. During the course of the execution, the still-to - be-executed part of the motion is continuously deformed in response to sensor information acquired on-line, thus accounting for the incompleteness and inaccuracies of the a priori world model. Deformation usually results from the application of constraints both external (imposed by the obstacles) and internal (to maintain motion feasibility an d connectivity). Provided that the motion connectivity can be maintained, convergence towards the goal is achieved. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IROS.2008.4650639 | Nice |
Keywords | Field | DocType |
motion control,path planning,position control,robots,autonomous navigation,path deformation,robot motion,space-time curve,trajectory deformation,Autonomous navigation,Collision avoidance,Dynamic environments,Motion deformation | Motion control,Control theory,Computer science,Vehicle dynamics,Artificial intelligence,Deformation (mechanics),Trajectory,Motion planning,Computer vision,Double integrator,Simulation,Collision,Robot | Conference |
ISBN | Citations | PageRank |
978-1-4244-2057-5 | 4 | 0.45 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vivien Delsart | 1 | 21 | 2.41 |
| Thierry Fraichard | 2 | 866 | 70.04 |