9781041034407 - Trajectory Planning Using Dynamics and Power Models: A Heuristics Based Approach de Ordonez, Camilo; Harper, Mario; Boylan, Jonathan T.; Collins Jr., Emmanuel G. (17 resultados)

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Hardcover. Condición: Brand New. 152 pages. 8.50x5.43x8.50 inches. In Stock.

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Hardcover. Condición: new. Hardcover. This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A* framework.Drawing from optimal control's model predictive control framework, the book develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO), which generates graph trees through input sampling of a dynamic model, enabling A*-type algorithms to find optimal trajectories. The book covers various robotic platforms and tasks, including manipulators lifting heavy loads, mobile robots navigating steep hills, energy-efficient skid-steered movements, thermally informed space exploration planning, and climbing robots in obstacle-rich environments. It also explores methods for updating dynamic models for robust operation and provides sample code for applying SBMPO to additional problems.This resource is aimed at researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems. This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A* framework. It develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO). For researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A\* framework.Drawing from optimal control's model predictive control framework, the book develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO), which generates graph trees through input sampling of a dynamic model, enabling A\*-type algorithms to find optimal trajectories. The book covers various robotic platforms and tasks, including manipulators lifting heavy loads, mobile robots navigating steep hills, energy-efficient skid-steered movements, thermally informed space exploration planning, and climbing robots in obstacle-rich environments. It also explores methods for updating dynamic models for robust operation and provides sample code for applying SBMPO to additional problems.This resource is aimed at researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems. 144 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Camilo Ordonez received a B.S. in Electronics Engineering from Pontificia Bolivariana University in 2003. He obtained his M.S. and Ph.D. degrees in Mechanical Engineering from Florida State University in 2006 and 2010, respectively. Currently, he .

Hardcover. Condición: new. Hardcover. This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A* framework.Drawing from optimal control's model predictive control framework, the book develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO), which generates graph trees through input sampling of a dynamic model, enabling A*-type algorithms to find optimal trajectories. The book covers various robotic platforms and tasks, including manipulators lifting heavy loads, mobile robots navigating steep hills, energy-efficient skid-steered movements, thermally informed space exploration planning, and climbing robots in obstacle-rich environments. It also explores methods for updating dynamic models for robust operation and provides sample code for applying SBMPO to additional problems.This resource is aimed at researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems. This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A* framework. It develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO). For researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A\* framework.Drawing from optimal control's model predictive control framework, the book develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO), which generates graph trees through input sampling of a dynamic model, enabling A\*-type algorithms to find optimal trajectories. The book covers various robotic platforms and tasks, including manipulators lifting heavy loads, mobile robots navigating steep hills, energy-efficient skid-steered movements, thermally informed space exploration planning, and climbing robots in obstacle-rich environments. It also explores methods for updating dynamic models for robust operation and provides sample code for applying SBMPO to additional problems.This resource is aimed at researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems.

Hardcover. Condición: new. Hardcover. This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A* framework.Drawing from optimal control's model predictive control framework, the book develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO), which generates graph trees through input sampling of a dynamic model, enabling A*-type algorithms to find optimal trajectories. The book covers various robotic platforms and tasks, including manipulators lifting heavy loads, mobile robots navigating steep hills, energy-efficient skid-steered movements, thermally informed space exploration planning, and climbing robots in obstacle-rich environments. It also explores methods for updating dynamic models for robust operation and provides sample code for applying SBMPO to additional problems.This resource is aimed at researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems. This book shows how to plan trajectories (i.e. time-dependent paths) for autonomous robots using a dynamic model within the A* framework. It develops a paradigm called Sampling Based Model Predictive Optimization (SBMPO). For researchers, engineers, and advanced students in motion planning and control for robotic and autonomous systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.