RAVON

Lore & Background

RAVON was developed at the Robotics Research Lab at the University of Kaiserslautern as a platform to explore behavior-based control strategies for autonomous off-road navigation. The vehicle uses a 3D laser scanner for environmental perception and employs a multi-layer control system with behavior-based components on two layers. It also incorporates a form of short-term memory to avoid collisions with obstacles observed some time earlier.

The robot was experimentally tested at the 2nd Military European Land Robot Trial in 2008. Its technical specifications include a length of 2.35 m, width of 1.4 m, height of 1.2 m, and a weight of 750 kg. It is powered by eight spiral cell batteries (12 V, 55 Ah each), providing an operation time of about 4 hours. The drive system is four-wheel drive with four independent electric motors, and steering is achieved via front and rear wheel steering using linear motors. The vehicle has a floor clearance of 0.3 m and can climb slopes of up to 100% at 7 km/h.

Reader's Guide

RAVON's significance lies in its role as a testbed for behavior-based control strategies in rough terrain, a domain critical for military and exploration robotics. Its multi-layer control system, combining behavior-based components with short-term memory for obstacle avoidance, demonstrates a practical approach to autonomous navigation in unstructured environments. The vehicle's experimental testing at the 2008 Military European Land Robot Trial underscores its relevance to military applications, where robust off-road mobility and adaptive navigation are essential. The use of a 3D laser scanner and four onboard computers highlights the computational demands of real-time perception and control. RAVON's design, with four-wheel drive and independent electric motors, along with front and rear steering, provides the maneuverability needed for challenging terrain. Its legacy is as a platform that advanced the understanding of how behavior-based architectures can be layered and integrated with memory mechanisms to improve autonomous navigation in outdoor settings, influencing subsequent research in field robotics.