Trajectory Planning Using Dynamics and Power Models: A Heuristics Based Approach
暫譯: 基於啟發式方法的動力學與功率模型軌跡規劃

Ordonez, Camilo, Harper, Mario, Boylan, Jonathan T.

Trajectory Planning Using Dynamics and Power Models: A Heuristics Based Approach
  • 出版商: CRC
  • 出版日期: 2025-07-11
  • 售價: $2,350
  • 貴賓價: 9.5 折 $2,233
  • 語言: 英文
  • 頁數: 128
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1041034407
  • ISBN-13: 9781041034407

    • 尚未上市，無法訂購

商品描述

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.

商品描述(中文翻譯)

這本書展示了如何在 A* 框架內使用動態模型為自主機器人規劃軌跡（即時間依賴的路徑）。

本書借鑒了最優控制的模型預測控制框架，發展出一種稱為基於取樣的模型預測優化（Sampling Based Model Predictive Optimization, SBMPO）的範式，通過對動態模型的輸入取樣生成圖樹，使 A* 類算法能夠找到最優軌跡。本書涵蓋了各種機器人平台和任務，包括操控器搬運重物、移動機器人導航陡坡、節能的滑行轉向運動、熱信息驅動的空間探索規劃，以及在障礙物豐富環境中的攀爬機器人。它還探討了更新動態模型以實現穩健運行的方法，並提供了將 SBMPO 應用於其他問題的示例代碼。

本資源旨在為機器人和自主系統的運動規劃與控制領域的研究人員、工程師和高級學生提供參考。

作者簡介

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 is a faculty member in the department of mechanical engineering at the FAMU-FSU College of Engineering. He is part of the Center for Intelligent Systems, Controls, and Robotics (CISCOR) and the Energy and Sustainability Center. His research interests include dynamic modeling of legged and wheeled vehicles, terrain identification, and motion planning.

Mario Harper is a professor of Computer Science at Utah State University and the director of the Decision-making, Intelligence, Robotics, Electrification, and Transportation (DIRECT) Lab. With expertise spanning Artificial Intelligence, Machine Learning, Robotics, and Finance, Dr. Harper has contributed to many projects involving satellites, Mars rovers, military systems, and electrified transportation. His research integrates AI with electrification, space robotics, and intelligent systems, with a focus on practical applications in extreme environments. He received a B.S. in Physics and Economics from Utah State University, as well as an M.S. in Finance and Computational Science and a Ph.D. in Computer Science, both from Florida State University.

Jonathan Tyler Boylan earned a Bachelor's degree in Mechanical Engineering with a Minor in Computer Science from Florida State University in 2023. He is currently pursuing a Master's degree in Robotics at Florida State University, where he conducts research in the Scansorial and Terrestrial Robotics and Integrated Design (STRIDe) Lab at the FAMU-FSU College of Engineering. His work focuses on advancing decisionmaking algorithms for autonomous robotic systems, including autonomous ground vehicles (AGVs), quadrupedal robots, and other platforms. His research interests span dynamic modeling, motion planning, computer vision, and robotic control, aiming to bridge theoretical insights with practical innovations in autonomous robotics.

Emmanuel Collins currently serves as Dean of the J.B. Speed School of Engineering at the University of Louisville. He has had a long career as a researcher in the fields of controls and robotics. Upon graduating with his Ph.D. in Aeronautics and Astronautics from Purdue University, he was employed at Harris Corporation where he worked in the emerging field of flexible space structure control. He made major contributions to the development and demonstration of effective robust vibration control algorithms, culminating in an Honorary Superior Accomplishment Award from NASA. As a professor, he has made contributions to a variety of areas, including robust control, robust fault detection, proprioceptive terrain classification for robots, intelligence for both mobile robot planning and manipulator motion planning, and nonlinear adaptive control. He has always focused on developing or interpreting state-of-the-art optimization algorithms and applying them to real-world problems. This remains one of his passions.

作者簡介(中文翻譯)

卡米洛·奧爾多涅斯於2003年獲得波利瓦爾大學電子工程學士學位。他於2006年和2010年分別在佛羅里達州立大學獲得機械工程碩士和博士學位。目前，他是FAMU-FSU工程學院機械工程系的教職員。他是智能系統、控制與機器人中心（CISCOR）及能源與可持續發展中心的成員。他的研究興趣包括腿式和輪式車輛的動態建模、地形識別和運動規劃。

馬里奧·哈珀是猶他州立大學的計算機科學教授，也是決策、智能、機器人、電氣化和交通（DIRECT）實驗室的主任。哈珀博士在人工智能、機器學習、機器人技術和金融領域擁有廣泛的專業知識，參與了許多涉及衛星、火星探測器、軍事系統和電氣化交通的項目。他的研究將人工智能與電氣化、空間機器人和智能系統相結合，專注於極端環境中的實際應用。他獲得了猶他州立大學的物理學和經濟學學士學位，以及佛羅里達州立大學的金融和計算科學碩士學位及計算機科學博士學位。

喬納森·泰勒·博伊蘭於2023年獲得佛羅里達州立大學機械工程學士學位，並輔修計算機科學。他目前正在佛羅里達州立大學攻讀機器人學碩士學位，並在FAMU-FSU工程學院的攀爬和地面機器人及綜合設計（STRIDe）實驗室進行研究。他的工作專注於推進自主機器人系統的決策算法，包括自主地面車輛（AGVs）、四足機器人和其他平台。他的研究興趣涵蓋動態建模、運動規劃、計算機視覺和機器人控制，旨在將理論見解與自主機器人的實際創新相結合。

伊曼紐爾·柯林斯目前擔任路易斯維爾大學J.B. Speed工程學院院長。他在控制和機器人領域擁有長期的研究生涯。畢業於普渡大學航空航天學博士後，他在哈里斯公司工作，專注於靈活空間結構控制的新興領域。他對有效的穩健振動控制算法的開發和演示做出了重大貢獻，並因此獲得了NASA的榮譽卓越成就獎。作為教授，他在多個領域做出了貢獻，包括穩健控制、穩健故障檢測、機器人的本體感知地形分類、移動機器人規劃和操控運動規劃的智能，以及非線性自適應控制。他始終專注於開發或解釋最先進的優化算法並將其應用於現實世界的問題，這仍然是他的熱情所在。

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