A robust approach to high‐speed navigation for unrehearsed desert terrain

Abstract

This article presents a robust approach to navigating at high speed across desert terrain. A central theme of this approach is the combination of simple ideas and components to build a capable and robust system. A pair of robots were developed, which completed a 212 km Grand Challenge desert race in approximately 7 h. A pathcentric navigation system uses a combination of LIDAR and RADAR based perception sensors to traverse trails and avoid obstacles at speeds up to 15 m/s. The onboard navigation system leverages a human-based preplanning system to improve reliability and robustness. The robots have been extensively tested, traversing over 3500 km of desert trails prior to completing the challenge. This article describes the mechanisms, algorithms, and testing methods used to achieve this performance.

Contextual Sequence Prediction with Application to Control Library Optimization

“…The sub-sequence of trajectories is usually computed offline [11,9]. Such methods are widely used in modern, autonomous ground robots including the two highest placing teams for DARPA Urban Challenge and Grand Challenge [28,17,27,26], LAGR [12], UPI [1], and Perceptor [14] programs. We use CONSEQOPT to maximize this function and generate trajectory sequences taking the current environment features.…”

Odin: Team VictorTango's entry in the DARPA Urban Challenge

“…With traditional hybrid architectures, deliberative components are usually kept at a high level, whereas the more reactive, behavior-based, components are used at a low level for direct actuator control (Konolige & Myers, 1998;Rosenblatt, 1995). With the rapid growth of computing technology, however, there has been a reemergence of deliberative methods for low-level motion planning (Thrun et al, 2006;Urmson et al, 2006). Search-based approaches provide the important traits of predictability and optimality, which are useful from an engineering point of view (Russel & Norvig, 2003).…”

Learning to Drive Among Obstacles

“…When clearance is not available, the algorithm plans at slower speeds [9]. Sandstorm and H1ghlander, robots developed for desert racing, have driven extreme routes at speeds up to 15 meters per second by planning in a series of grids along the original path and smoothing the result [10].…”