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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Journal ArticleDOI
Research on manipulator motion planning for complex systems based on deep learning
TL;DR: This paper addresses the crucial challenges of the current manipulator planning approaches by utilizing the concept of deep learning and proposes a dynamic deep neural network enabled iterative manipulator motion planning framework for complex systems.
Book ChapterDOI
Robustness and Randomness
TL;DR: The paper investigates the possibility to use randomness at certain levels of reasoning to make geometric constructions more robust and discusses approaches based on exact arithmetic, interval arithmetic and probabilistic methods.
Proceedings ArticleDOI
Unmanned Leg-Wheel Vehicle Design and of Vertical Step Passability Performance Analysis
TL;DR: A new locomotion mechanism that combines legs and wheels is proposed, and a prototype mobile robot that adopts the mechanism is introduced, and actions for obstacle climbing is designed.
Advances in Reachability Analysis for Nonlinear Dynamic Systems
TL;DR: This dissertation provides new theoretical and numerical techniques for rigorously enclosing the set of solutions reachable by a given systems of nonlinear ODEs subject to uncertain initial conditions, parameters, and time-varying inputs.
Posted Content
Applications of Successive Convexification in Autonomous VehiclePlanning and Control
TL;DR: In this article, the authors present the first systematic application of successive convexification methods from the aerospace literature to the autonomous driving problems and show a simple application of logical state-trigger constraints in a continuous formulation of the optimization.