Results in Combined Route Traversal and Collision Avoidance

Roth, Stephan; Hamner, Bradley; Singh, Sanjiv; Hwangbo, Myung

doi:10.1007/978-3-540-33453-8_41

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, we know that our system can plan complicated paths to arrive at a difficult goal without risking a collision. A similar reactive algorithm in 2D was previously deployed on a ground patrol robot (Roth et al 2005). The local planning for both the 3D and 2D obstacle avoidance is related to models of human navigation among discrete obstacles .…”

Section: Collision Avoidancementioning

confidence: 99%

Flying Fast and Low Among Obstacles: Methodology and Experiments

Scherer

Singh

Chamberlain

et al. 2008

The International Journal of Robotics Research

Self Cite

163

View full text Add to dashboard Cite

Safe autonomous flight is essential for widespread acceptance of aircraft that must fly close to the ground. We have developed a methodology of collision avoidance that can be used in three dimensions in much the same way as autonomous ground vehicles that navigate over unexplored terrain. Safe navigation is accomplished by a combination of online environmental sensing, path planning and collision avoidance. Here we outline our methodology and report results with an autonomous helicopter that operates at low elevations in uncharted environments some of which are densely populated with obstacles such as buildings, trees and wires. We have recently completed over 700 successful runs in which the helicopter traveled between coarsely specified waypoints separated by hundreds of meters, at speeds up to 10 meters/sec at elevations of 5-11 meters above ground level. The helicopter safely avoids large objects like buildings and trees but also wires as thin as 6 mm. We believe this represents the first time an air vehicle has traveled this fast so close to obstacles. The collision avoidance method learns to avoid obstacles by observing the performance of a human operator.

show abstract

Section: Collision Avoidancementioning

confidence: 99%

Flying Fast and Low Among Obstacles: Methodology and Experiments

Scherer

Singh

Chamberlain

et al. 2008

The International Journal of Robotics Research

Self Cite

163

View full text Add to dashboard Cite

show abstract

Experimental Analysis of Overhead Data Processing To Support Long Range Navigation

Silver

Sofman

Vandapel

et al. 2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-Long range navigation by unmanned ground vehicles continues to challenge the robotics community. Efficient navigation requires not only intelligent on-board perception and planning systems, but also the effective use of prior knowledge of the vehicle's environment. This paper describes a system for supporting unmanned ground vehicle navigation through the use of heterogeneous overhead data. Semantic information is obtained through supervised classification, and vehicle mobility is predicted from available geometric data. This approach is demonstrated and validated through over 50 kilometers of autonomous traversal through complex natural environments.

show abstract

Learning to Drive Among Obstacles

Hamner

Scherer

Singh

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

Abstract-This paper reports on an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters, and then show how these parameters can be automatically estimated by observation of a human driver. We present results from experiments with a vehicle (both real and simulated) that avoids obstacles while following a prescribed path at speeds up to 4 m/sec. We compare the proposed method with another method based on Principal Component Analysis, a commonly used learning technique. We find that the proposed method generalizes well and is capable of learning from a small number of examples.

show abstract

Results in Combined Route Traversal and Collision Avoidance

Cited by 4 publications

References 8 publications

Flying Fast and Low Among Obstacles: Methodology and Experiments

Flying Fast and Low Among Obstacles: Methodology and Experiments

Experimental Analysis of Overhead Data Processing To Support Long Range Navigation

Learning to Drive Among Obstacles

Contact Info

Product

Resources

About