https://hal.inria.fr/hal-00801220/document

Terrain traversability analysis methods for unmanned ground vehicles: A survey

The DARPA PerceptOR program has implemented a rigorous evaluative test program which fosters the development of field relevant outdoor mobile robots. Autonomous ground vehicles were deployed on diverse test courses throughout the USA and quantitatively evaluated on such factors as autonomy level, waypoint acquisition, failure rate, speed, and communications bandwidth. Our efforts over the three year program have produced new approaches in planning, perception, localization, and control which have been driven by the quest for reliable operation in challenging environments. This paper focuses on some of the most unique aspects of the systems developed by the CMU PerceptOR team, the lessons learned during the effort, and the most immediate challenges that remain to be addressed.

Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments

We describe a core system for autonomous navigation in outdoor natural terrain. The system consists of three parts: a perception module that processes range images to identify untraversable regions of the terrain, a local map management module that maintains a representation of the environment in the vicinity of the vehicle, and a planning module that issues commands to the vehicle controller. We illustrate our approach by an experiment in which a vehicle travelled autonomously for one kilometer through unmapped cross-country terrain. >

/pdf/a-behavior-based-system-for-off-road-navigation-bxl2bqlmkb.pdf

A behavior-based system for off-road navigation

Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.

/pdf/learning-from-demonstration-for-autonomous-navigation-in-2jv5ykzvpt.pdf

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments.

The development of a software system for obstacle avoidance that controls a vehicle smoothly, continuously, and safely across natural terrain en route to a goal is discussed. The work, carried out as part of the Strategic Computing Initiative of the US Defense Advanced Research Projects Agency (DARPA), was performed on the Autonomous Land Vehicle at Martin Marietta's Denver facilities. The planning software uses digital maps to plan a preferred route, and then, as the vehicle traverses that route, sensors are used to obtain scene descriptions. Techniques necessary to detect and avoid obstacles in cross-country terrain, with minimum delay between sensing and acting, are used. The system architecture, the cross-country perception system, the planning system for obstacle avoidance, the simulation environment, and the experiments are described. Work to develop a fine-grained control architecture, internalized plans, multisensor perception, and color image segmentation is discussed. >

K.E. Olin

Papers

Autonomous cross-country navigation: an integrated perception and planning system