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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.

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Citations
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Book ChapterDOI

Reachable Sets for Simple Models of Car Motion

TL;DR: In this article, the authors considered the problem of finding a minimum length curve between two points in the plane, provided that the curvature radius of the curve should not be less than a given quantity and the tangent to the curve had a given direction at the initial point.
Journal ArticleDOI

Path following of car-like vehicles using dynamic inversion

TL;DR: A new feedforward/feedback control strategy where the feedforward is determined by a dynamic generator based on exact dynamic inversion over the nominal vehicle model and the feedback is mainly issued by correcting terms proportional to the tangential and normal errors determined with respect to the vehicle’s ideal trajectory is provided.
Journal ArticleDOI

Lyapunov and Minimum-Time Path Planning for Drones

TL;DR: In this article, a control strategy based upon a Lyapunov-LaSalle stabilization method was proposed for both high altitude long endurance (HALE) and medium altitude long-endurance (MALE) types of UAVs.
Proceedings ArticleDOI

Dynamic obstacles avoidance based on image-based dynamic window approach for human-vehicle interaction

TL;DR: The performance of the proposed human-vehicle interaction methodology, based on autonomous embedded functionalities, is simulated and verified in different bypass scenarios.
Proceedings ArticleDOI

A safe flight algorithm for unmanned aerial vehicles

TL;DR: In this paper, the authors present two algorithms that guide the forward motion of UAVs and illustrate how this safe flight algorithm can be coupled with other path planning algorithms to keep the UAV "safe".