Sensor-based algorithm for collision-free avoidance of mobile robots in complex dynamic environments

Dimitri Leca 1 Viviane Cadenat 1 Thierry Sentenac 2
1 LAAS-RAP - Équipe Robotique, Action et Perception
LAAS - Laboratoire d'analyse et d'architecture des systèmes
2 ICA - Institut Clément Ader
Abstract : This paper deals with the problem of navigation of unmanned vehicles through poorly known environments cluttered with static and dynamic obstacles. The robot is equipped with a LiDAR able to provide a scan of the surroundings and with classical dedicated localization sensors (odometry, IMU). The proposed navigation strategy relies on: (i) a new concept called Enhanced Laser Scan (ELS), which is built from the current laser scan by adding virtual points along the predicted trajectory of the obstacles; (ii) two sensor-based controllers allowing respectively to reach the goal and to avoid obstacles. These controllers relying on the richer information provided by the ESL, they will be able to anticipate and safely avoid both static and moving obstacles; (iii) a high-level decision process allowing a better choice of the sense-of-motion (SOM) around the obstacle and its reassessment if needed.
Keywords : Spirals Vehicle dynamics Navigation Robot sensing systems Robot kinematics Control systems
Document type :
Conference papers
Complete list of metadatas
https://hal-mines-albi.archives-ouvertes.fr/hal-02334205
Contributor : Imt Mines Albi Ecole Nationale Supérieure Des Mines d'Albi-Carmaux <>
Submitted on : Friday, October 25, 2019 - 6:44:39 PM
Last modification on : Friday, November 15, 2019 - 2:43:20 PM

Identifiers

Collections

MINES-ALBI | UNIV-TLSE3 | INSA-TOULOUSE | ICA | ICA-ALBI | UT1-CAPITOLE | UNIV-TLSE2 | INSA-GROUPE | LAAS | LAAS-RAP | LAAS-ROBOTIQUE

Citation

Dimitri Leca, Viviane Cadenat, Thierry Sentenac. Sensor-based algorithm for collision-free avoidance of mobile robots in complex dynamic environments. 2019 European Conference on Mobile Robots (ECMR), Sep 2019, Prague, Czech Republic. p.1-6, ⟨10.1109/ECMR.2019.8870344⟩. ⟨hal-02334205⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

38

Contact

CCSD