Version 1
: Received: 8 February 2019 / Approved: 11 February 2019 / Online: 11 February 2019 (09:00:14 CET)
Version 2
: Received: 6 January 2020 / Approved: 7 January 2020 / Online: 7 January 2020 (05:25:17 CET)
How to cite:
Hoang, T. Estimation and Planning Differential Drive Robot Simultaneously - Technical Report. Preprints2019, 2019020084. https://doi.org/10.20944/preprints201902.0084.v1
Hoang, T. Estimation and Planning Differential Drive Robot Simultaneously - Technical Report. Preprints 2019, 2019020084. https://doi.org/10.20944/preprints201902.0084.v1
Hoang, T. Estimation and Planning Differential Drive Robot Simultaneously - Technical Report. Preprints2019, 2019020084. https://doi.org/10.20944/preprints201902.0084.v1
APA Style
Hoang, T. (2019). Estimation and Planning Differential Drive Robot Simultaneously - Technical Report. Preprints. https://doi.org/10.20944/preprints201902.0084.v1
Chicago/Turabian Style
Hoang, T. 2019 "Estimation and Planning Differential Drive Robot Simultaneously - Technical Report" Preprints. https://doi.org/10.20944/preprints201902.0084.v1
Abstract
Estimation and planning play a vital role in the construction of an autonomous navigation framework. However, these problems are often considered separately, while planning gives robot a free-collision path towards the desired goal, estimation algorithm presents the executed trajectory in the sense that it has to be closed to the ground truth path as much as possible. Recently, a unified probabilistic framework, which supports solving these problems simultaneously, dubbed STEAP has been proposed. Nevertheless, its current version is only designated for an omni wheels robot, which allows robot to move and turn in vertical direction. Differential drive robot, on the other hand, though limited to move along only one direction, has been used in various situations due to its flexibility and lower cost in hardware designing. Thus, in this extension, our aim is to control a differential drive robot via STEAP. Moreover, in a more complicated environment such as labyrinth or maze, the original STEAP sometimes fails to find a path. Indeed, this problem is mainly caused by the poor initialization and the non-linearity in optimizer constraints. In our implementation, instead of dealing with these constraints, we employ a global planner algorithm such as Dijkstra or RRT to treat STEAP as an effective local planner module that focus on following the global path. Consequently, the experimental results show that the extended STEAP not only able to navigate a differential drive robot but also in a more complicated and unstructured environment.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
