BookDOI
Robot Motion Planning and Control
Jean-Paul Laumond
- Iss: 229
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TLDR
Guidelines in nonholonomic motion planning for mobile robots and collision detection algorithms for motion planning are presented.Abstract:
Guidelines in nonholonomic motion planning for mobile robots.- Geometry of nonholonomic systems.- Optimal trajectories for nonholonomic mobile robots.- Feedback control of a nonholonomic car-like robot.- Probabilistic path planning.- Collision detection algorithms for motion planning.read more
Citations
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Proceedings ArticleDOI
Non-contact manipulation for automated protein crystal harvesting using a rolling microrobot
TL;DR: The micro-agent, the actuation system and the visual control system to achieve this automated procedure to create a vortex that enables the robot to trap and transport even delicate objects in a non-contact manner to a pre-defined position.
Journal ArticleDOI
PD Controller for Manipulator with Kinetic Energy Term
TL;DR: It is shown that utilizing the EQV vector one can determine directly the kinetic energy of the manipulator and at the same time realize PD control in its joint space and this energy-based strategy gives an interesting insight into position control.
Journal ArticleDOI
NRR: a nonholonomic random replanner for navigation of car-like robots in unknown environments
Ellips Masehian,Hossein Kakahaji +1 more
TL;DR: A new sensor-based approach called nonholonomic random replanner (NRR) is presented for motion planning of car-like mobile robots and its effectiveness and efficiency were tested by running several simulations and the resulting runtimes and path lengths were compared to the basic RRT method.
Proceedings ArticleDOI
A Path Planning Method for Assistant Parallel Car-Parking
TL;DR: A scheme of a two-arc parking path is proposed, with the start position given but the goal to be yielded, and the optimized results by MATLAB Optimization Toolbox.
Modeling and control techniques for a class of mobile-robot error recovery problems
TL;DR: In this article, the authors propose recovery strategies by exploiting the structure inherent to the robot's constrained mobility and environmental interaction in the locomotion error, where the robot can choose between multiple locomotion modes depending on the circumstance, ultimately contributing to robust mobility.