Evaluation of Skid-Steering Kinematic Models for Subarctic Environments

• Published in: 2020 17th Conference on Computer and Robot Vision (CRV)
• Main Authors: Baril, Dominic, Grondin, Vincent, Deschenes, Simon-Pierre, Laconte, Johann, Vaidis, Maxime, Kubelka, Vladimir, Gallant, Andre, Giguere, Philippe, Pomerleau, Francois
• Other Authors: Université Laval Québec (ULaval)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2020
• Subjects: mobile robots; skid-steering vehicles; robot kinematics; winter; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; Arctic; Subarctic
• Online Access: https://hal.inrae.fr/hal-04589438; https://hal.inrae.fr/hal-04589438/document; https://hal.inrae.fr/hal-04589438/file/2004.05131v1.pdf; https://doi.org/10.1109/CRV50864.2020.00034

International audience In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-theart odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lightweight platforms. In this paper, we focus on how these models perform when deployed on a large and heavy (590 kg) SSMR. We collected more than 2 km of data on both snow and concrete. We compare the ideal differential-drive, extended differential-drive, radius-ofcurvature-based, and full linear kinematic models commonly deployed for SSMRs. Each of the models is fine-tuned by searching their optimal parameters on both snow and concrete. We then discuss the relationship between the parameters, the model tuning, and the final accuracy of the models.