Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Hybrid intelligent path planning for articulated rovers in rough terrain
Fuzzy Sets and Systems
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This paper presents a new, computationally inexpensive, terrain roughness assessment technique. The technique, targeted for high speed Unmanned Ground Vehicles (UGVs), considers no previous knowledge about the terrain but a priori knowledge about the vehicle's characteristics such as damping ratio, natural frequency and wheel configuration. The approach aims to allow fast, smooth and safe transition between different terrains (e.g. pavement, grass, sand, etc.) with no additional computation effort to detect the terrain transitions and cope with the associated complexities. This technique although developed for UGVs is applicable to current commercial vehicles as a driving assistant system which will increase the safety of motor vehicles (not only UGVs). The terrain profile is considered as a frequency band limited random variable. Accordingly, terrain roughness is described in terms of a set of stochastic parameters. This paper derives the required resolution of the terrain perception system as function of the vehicle's characteristics needed by the UGV to make effective maneuvers. A simulation of a vehicle changing its speed according to the perceived terrain roughness illustrates the implementation of the proposed technique. The results also show how the vehicle is able to autonomously traverse at high speed unknown terrains containing different roughness regions.