Name
Abstract | ||
|---|---|---|
This work introduces a novel control strategy to allow a class of mobile robots to robustly navigate in dynamics and potentially cluttered environments. The proposed approach combines a high-level motion planner and a low-level stabilizing feedback control law designed considering the nonlinear dynamic model of the vehicle. Taking advantage of a symbolic description of the vehicle dynamics and of the environment, the reference trajectories are sequences of elementary primitives which are obtained with a reduced computational cost. However, the resulting references may fail to be functionally controllable for the actual dynamical model of the vehicle. Accordingly, to obtain a desired tracking error, sufficient conditions are then derived by investigating the interconnection between the discrete time planner and the continuous time closed-loop nonlinear system. The effectiveness of the obtained results is demonstrated by considering, as application, a ground robot navigating in a cluttered environment. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CDC.2014.7039463 | Decision and Control |
Keywords | Field | DocType |
closed loop systems,continuous time systems,discrete time systems,feedback,mobile robots,path planning,robot dynamics,robust control,vehicle dynamics,continuous time closed-loop nonlinear system,discrete time planner,dynamic environments,high-level motion planner,low-level stabilizing feedback control law,mobile robots navigation,robust control strategy,sufficient conditions,tracking error,vehicle dynamic symbolic description,vehicle nonlinear dynamic model | Nonlinear system,Computer science,Control theory,Control engineering,Vehicle dynamics,Discrete time and continuous time,Interconnection,Robust control,Robot,Mobile robot,Tracking error | Conference |
ISSN | Citations | PageRank |
0743-1546 | 0 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Furci | 1 | 29 | 3.62 |
| Roberto Naldi | 2 | 221 | 23.68 |
| Andrea Paoli | 3 | 212 | 16.73 |
| Lorenzo Marconi | 4 | 845 | 93.46 |