A localisation system for a high-speed land vehicle.
Presented at: UNSPECIFIED.
There has been a significant increase in the use of Autonomous Guided Vehicles (AGVs) in ports, mines and other primary industries. Many of these applications require vehicles which operate safely and efficiently in unstructured environments at speeds approaching those of human-controlled vehicles. Meeting these objectives is extremely difficult and arguably one of the most important requirements is an accurate and robust localisation system. In this paper we describe the development of a prototype, Kalman filter-based localisation system for a conventional road vehicle operating in an outdoor environment at speeds in excess of 15ms-1. Using sparsely placed beacons, vehicle position can be resolved to the order of a metre. Three main themes are addressed. The first is a quantitative methodology for sensor suite design. Sensors are classified according to their frequency responses and the suite is chosen to ensure a uniform response across the spectrum of vehicle manoeuvres. The second theme develops accurate, high-order nonlinear models of vehicle motion which incorporate kinematics, dynamics and slip due to tyre deformation. Each model is useful within a certain operating regime. Outside of this regime the model can fail (for example, some states become unobservable) and the third theme avoids this problem through the use multiple models algorithms which synergistically fuse the properties of several models. Through addressing these themes we have developed a navigation system which has been shown to be accurate and robust to different types of road surface and occasional sensor data loss. The theories and principles developed in this paper are being used to develop a navigation system for commercial mining vehicles.
|Type:||Conference item (UNSPECIFIED)|
|Title:||A localisation system for a high-speed land vehicle|
|Keywords:||Autonomous Guided Vehicle, Estimation, Kalman filter, Model fusion, Navigation, Process modelling, Sensor suite design, Unscented filter|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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