Autonomous terrain characterisation and modelling for dynamic control of unmanned vehicles

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Abstract
An often-ignored aspect of unmanned cross-country vehicles is the dynamic response of the vehicle on dffer- ent terrain. We discuss techniques to predict the dynamic vehicle response to various natural obstacles. This method can then be used to adjust the vehicle dynamics to optimize pegormanee (e.g. speed) while ensuring that the vehicle is not damaged. This capability opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoid- ing them, thereby resulting in more effective achievement of objectives. Robust obstacle negotiation and vehicle dynamics prediction requires several key technologies that will be discussed in this paper. We detect and seg- ment (label) obstacles using a novel 30 obstacle algo- rithm. The material of each labelled obstacle (rock, vege- tation, etc.) is then determined using a texture or color classification scheme. Terrain load-bearing surface mod- els are then constructed using vertical springs to model the compressibility and traversability of each obstacle in front of the vehicle. The terrain model is then combined with the vehicle suspension model to yield an estimate of the maximum safe velocity, and predict the vehicle dy- namics as the vehicle follows a path. This end-to-end obstacle negotiation system is envisioned to be usejsrl in optimized path planning and vehicle navigation in ter- rain conditions cluttered with vegetation, bushes, rocks, etc. Results on natural terrain with various natural mate- rials are presented

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