Peiyi Chen (11 results)

Condition: New.

Paperback. Condition: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock.

Taschenbuch. Condition: Neu. Motion Planning using Kinodynamically Feasible Motion Primitives | An Online Algorithm for Navigating through Unknown 3D Environments | Peiyi Chen | Taschenbuch | 112 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846545522 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Soft cover. Condition: Fine. 215 p. : ill. ; 30 cm. Text in Chinese and English. catalogue prepared to accompany the exhibition at Kaohsiung Museum of Fine Arts in Taiwan in the summer of 2001. Illustrated in black & white.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Autonomous unmanned vehicles require the ability to maneuver within their environments safely and efficiently. The problem of generating these traversable, collision-free paths is known as motion planning. While much work has been done in this field, the majority of the existing motion planning algorithms either fail to consider the kinodynamic constraints of the vehicle or is too computationally intensive to be performed online. The presented motion planning algorithm mitigates these issues by using kinodynamically feasible motion primitives to enforce vehicle constraints, while exploiting the computational efficiency and scalability of probabilistic roadmap planners. The result is an online motion planner that is capable of generating safe, traversable paths to any specified destination in an unknown 3D environment. This approach has been experimentally verified using quadrotor helicopters and skid-steer ground rovers. 112 pp. Englisch.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chen PeiyiPeiyi Chen has developed planning algorithms and control strategies for a a variety of autonomous unmanned vehicles, including quadrotor helicopters and skid-steer ground rovers. He is a member of the Waterloo Autonomous Ve.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Autonomous unmanned vehicles require the ability to maneuver within their environments safely and efficiently. The problem of generating these traversable, collision-free paths is known as motion planning. While much work has been done in this field, the majority of the existing motion planning algorithms either fail to consider the kinodynamic constraints of the vehicle or is too computationally intensive to be performed online. The presented motion planning algorithm mitigates these issues by using kinodynamically feasible motion primitives to enforce vehicle constraints, while exploiting the computational efficiency and scalability of probabilistic roadmap planners. The result is an online motion planner that is capable of generating safe, traversable paths to any specified destination in an unknown 3D environment. This approach has been experimentally verified using quadrotor helicopters and skid-steer ground rovers.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Autonomous unmanned vehicles require the ability to maneuver within their environments safely and efficiently. The problem of generating these traversable, collision-free paths is known as motion planning. While much work has been done in this field, the majority of the existing motion planning algorithms either fail to consider the kinodynamic constraints of the vehicle or is too computationally intensive to be performed online. The presented motion planning algorithm mitigates these issues by using kinodynamically feasible motion primitives to enforce vehicle constraints, while exploiting the computational efficiency and scalability of probabilistic roadmap planners. The result is an online motion planner that is capable of generating safe, traversable paths to any specified destination in an unknown 3D environment. This approach has been experimentally verified using quadrotor helicopters and skid-steer ground rovers.