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
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Journal ArticleDOI
Mechatronic design of adjustable serial manipulators with decoupled dynamics taking into account the changing payload
TL;DR: In this article, a new mechatronic design approach based on the opposite motion of manipulator links and the optimal command design is proposed to simplify the control by reducing the effects of complicated manipulator dynamics.
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V-Lab®– A Distributed Intelligent Discrete-Event Environment for Autonomous Agents Simulationi
Mo Jamshidi,Shahab Sheikh-Bahaei,J. Kitzinger,P. Sridhar,S. Beatty,S. Xia,Yan Wang,T. Song,U. Dole,Jingyu Liu,Edward Tunstel,M. Akbarzadeh,Paolo Lino,A. El-Osery,M. Fathi,X. Hu,B. P Zeigler +16 more
TL;DR: IDEVS is an element of a virtual laboratory, called V-Lab®, which is based on distributed multi-physics, multi-dynamic modeling techniques for multiple platforms, and a theme example for amultiagent simulation of a number of robotic agents with a slew of dynamic models and multiple computer work stations.
Journal ArticleDOI
An approach integrating planning and image-based visual servo control for road following and moving obstacles avoidance
Ramses Reyes,Rafael Murrieta-Cid +1 more
TL;DR: An approach that integrates planning and image based visual servo control for road following and moving obstacle avoidance and represents a robot's general plan or strategy in the form of a finite state machine or automaton is proposed.
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Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
TL;DR: In this paper , a data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers is proposed, which includes a Multi-Relation Graph Convolutional Network, a Long Short Term Memory Network, and attention layers.
Proceedings ArticleDOI
On Maximizing Lateral Clearance of an Autonomous Vehicle in Urban Environments
TL;DR: This work first presents a traditional MPC controller, which is then extended to encode the clearance maximization goal by manipulating its cost function and constraints, and provides insights on the additional information needed to achieve such goal.