Tracked robots can traverse a wide range of terrains, but can be hard to control automatically in uneven terrain because of track slip. Improved robot control requires a real-time estimate of slip, but this can be difficult to obtain without good forward velocity measurements. This project showed that a slip estimate can be obtained using only a rate gyroscope, allowing for improved path following control.
This work has been used by the CSIR for a mine safety robot and in autonomous 3D mapping projects. Work is ongoing in this domain with a Masters student, Ditebogo Masha, looking to apply slip estimation to terrain classification and characterisation problems.
M. Burke, “Path-following control of a velocity constrained tracked vehicle incorporating adaptive slip estimation,” 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, 2012, pp. 97-102.
D. Masha, M. Burke, and B. Twala, “Slip estimation methods for proprioceptive terrain classification using tracked mobile robots.” Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), 2017. IEEE, 2017.
D. Masha, M. Burke, “Importance Sampling Forests for Location Invariant Proprioceptive Terrain Classification” Artificial Intelligence Research – First Southern African Conference for AI Research, SACAIR 2020, in Communications in Computer and Information Science, Springer, 2020.