Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
An autonomous guided vehicle for cargo handling applications
International Journal of Robotics Research
An intelligent, predictive control approach to the high-speed cross-country autonomous navigation problem
A Dynamical Model of Visually-Guided Steering, Obstacle Avoidance, and Route Selection
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
The TerraMax autonomous vehicle: Field Reports
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Tradeoffs Between Directed and Autonomous Driving on the Mars Exploration Rovers
International Journal of Robotics Research
Comprehensive Automation for Specialty Crops: Year 1 results and lessons learned
Intelligent Service Robotics
Robot Navigation in a Decentralized Landmark-Free Sensor Network
Journal of Intelligent and Robotic Systems
A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments
Journal of Intelligent and Robotic Systems
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Here we consider the problem of a robot that must follow a previously designated path outdoors. While the nominal path, a series of closely spaced via points, is provided with an assurance that it will lead to the destination, we can't be guaranteed that it will be obstacle free. We present an efficient system capable of both following the path as well as being perceptive and agile enough to avoid obstacles in its way. We present a system that detects obstacles using laser ranging, as well as a layered system that continuously tracks the path, avoiding obstacles and replanning the route when necessary. The distinction of this system is that compared to the state of the art, it is minimal in sensing and computation while achieving high speeds. In this paper, we present an algorithm that is based on models of obstacle avoidance by humans and show variations of the model to deal with practical considerations. We show how the parameters of this model are automatically learned from observation of human operation and discuss limitations of the model. We then show how these models can be extended by adding online route planning and a formulation that allows for operation at varying speeds. We present experimental results from an autonomous vehicle that has operated several hundred kilometers to validate the methodology.