Automatica (Journal of IFAC)
Gross motion planning—a survey
ACM Computing Surveys (CSUR)
Robot motion planning: a distributed representation approach
International Journal of Robotics Research
Pearls found on the way to the ideal interface for scanned-probe microscopes
VIS '97 Proceedings of the 8th conference on Visualization '97
V-Clip: fast and robust polyhedral collision detection
ACM Transactions on Graphics (TOG)
On finding narrow passages with probabilistic roadmap planners
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
OBPRM: an obstacle-based PRM for 3D workspaces
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Robot Motion Planning
Fusion of Human and Machine Intelligence for Telerobotic Systems
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
A Framework for Fast and Accurate Collision Detection for Haptic Interaction
VR '99 Proceedings of the IEEE Virtual Reality
Human interface using the Rutgers Master II force feedback interface
VRAIS '96 Proceedings of the 1996 Virtual Reality Annual International Symposium (VRAIS 96)
Haptic-aided robot path planning based on virtual tele-operation
Robotics and Computer-Integrated Manufacturing
Swarming behavior using probabilistic roadmap techniques
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
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In this paper, we investigate methods for enabling a human operator and an automatic motion planner to cooperatively solve a motion planning query. Our work is motivated by our experience that automatic motion planners sometimes fail due to the difficulty of discovering ‘critical’ configurations of the robot that are often naturally apparent to a human observer.Our goal is to develop techniques by which the automatic planner can utilize (easily generated) user-input, and determine ‘natural’ ways to inform the user of the progress made by the motion planner. We show that simple randomized techniques inspired by probabilistic roadmap methods are quite useful for transforming approximate, user-generated paths into collision-free paths, and describe an iterative transformation method which enables one to transform a solution for an easier version of the problem into a solution for the original problem. We also illustrate that simple visualization techniques can provide meaningful representations of the planner's progress in a 6-dimensional C-space. We illustrate the utility of our methods on difficult problems involving complex 3D CAD Models.