The Navlab system for mobile robot navigation
The Navlab system for mobile robot navigation
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Sensor based motion planning: the hierarchical generalized Voronoi graph
Sensor based motion planning: the hierarchical generalized Voronoi graph
A game-theoretic framework for robot motion planning
A game-theoretic framework for robot motion planning
On motion planning in changing, partially predictable environments
International Journal of Robotics Research
On-line search in a simple polygon
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon
Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon
Optimal Selection of Uncertain Actions by Maximizing Expected Utility
Autonomous Robots
Mars Rover Autonomous Navigation
Autonomous Robots
Occlusions as a Guide for Planning the Next View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion and Perception Strategies for Outdoor Mobile Robot Navigation in Unknown Environments
The 4th International Symposium on Experimental Robotics IV
Robot Navigation for Automatic Model Construction Using Safe Regions
ISER '00 Experimental Robotics VII
Autonomous Rover Navigation on Unknown Terrains Functions and Integration
ISER '00 Experimental Robotics VII
A Sensor-Based Solution to the Next Best View Problem
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Theory and experiments in autonomous sensor-based motion planning with applications for flight planetary microrovers
Autonomous surface exploration for mobile robots
Autonomous surface exploration for mobile robots
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
International Journal of Computer Vision
Design and field experimentation of a prototype Lunar prospector
International Journal of Robotics Research
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Path planning with variable-fidelity terrain assessment
Robotics and Autonomous Systems
Hi-index | 0.00 |
The mobility sensors on a typical mobile robot vehicle have limited range. Therefore a navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge to compute paths are unable to anticipate obstacles sufficiently early and have no choice but to resort to an inefficient local obstacle avoidance behavior. To alleviate this problem, we present an opportunistic navigation and view planning strategy that incorporates look-ahead sensing of possible obstacle configurations. This planning strategy is based on a "what-if" analysis of hypothetical future configurations of the environment. Candidate sensing positions are evaluated based on their ability to observe anticipated obstacles. These sensing positions identified by this forward-simulation framework are used by the planner as intermediate waypoints. The validity of the strategy is supported by results from simulations as well as field experiments with a real robotic platform. These results show that significant reduction in path length can be achieved by using this framework.