Identification of Effective Motion Primitives for Ground Vehicles
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Date
2020
Publication Type
Conference Paper
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Abstract
Understanding the kinematics of a ground robot is essential for efficient navigation. Based on the kinematic model of a robot, its full motion capabilities can be represented by theoretical motion primitives. However, depending on the environment and/or human preferences, not all of those theoretical motion primitives are desirable and/or achievable. This work presents a method to identify effective motion primitives (eMP) from continuous trajectories for autonomous ground robots. The pipeline efficiently performs segmentation, representation and reconstruction of the motion primitives, using initial human-driving behaviour as a guide to create a motion primitive library. Hence, this strategy incorporates how the environment affects the robot operation regarding accelerations, speed, braking, and steering behaviours.The method is thoroughly tested on an autonomous car-like electric vehicle, and the results show excellent generalisation of the theoretical motion primitive distribution to real vehicle. The experiments are carried out on large site with very diverse characteristics, illustrating the applicability of the method. © 2020 IEEE
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published
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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Pages / Article No.
2027 - 2034
Publisher
IEEE
Event
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (virtual)
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Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.