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G. Heinzinger

Bio: G. Heinzinger is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Trajectory & Approximation algorithm. The author has an hindex of 5, co-authored 8 publications receiving 340 citations.

Papers
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Proceedings ArticleDOI
13 May 1990
TL;DR: This is the first algorithm to guarantee bounds on the closeness of an approximation to a time-optimal trajectory and the running time and space required are polynomial in the desired accuracy of the approximation.
Abstract: An algorithm is presented for generating near-time-optimal trajectories for an open-kinematic-chain manipulator moving in a cluttered workspace. This is the first algorithm to guarantee bounds on the closeness of an approximation to a time-optimal trajectory. The running time and space required are polynomial in the desired accuracy of the approximation. The user may also specify tolerances by which the trajectories must clear obstacles in the workspace to allow modeling of control errors. >

44 citations

Proceedings Article
01 Feb 1991
TL;DR: A study is made of a fundamental problem in dextrous manipulation by a robot hand: the motion of two rigid bodies rolling relative to one another, and it is shown that the path-finding problem is equivalent to a nonlinear control problem.

20 citations

Proceedings ArticleDOI
14 May 1989
TL;DR: Simple configuration-independent bounds on the dynamics of a robot manipulator are developed and are useful in dynamics interpolation bounding the Coriolis and centrifugal forces, suboptimal control, and path planning with dynamical constraints.
Abstract: The authors develop simple configuration-independent bounds on the dynamics of a robot manipulator. In particular, the inertia and gravity tensors and their derivatives of all orders for open-kinematic-chain manipulators. The bounds are useful in dynamics interpolation bounding the Coriolis and centrifugal forces (since these depend on the derivatives of the inertia tensor), suboptimal control, and path planning with dynamical constraints. These bounds can be used for verifying that trajectories satisfy actuator constraints. The bounds on the derivatives of the inertia tensor bound Coriolis and centrifugal forces and determine whether or not they can be ignored along a trajectory. The bounds on the derivatives also permit reliable interpolation of feedforward terms of dynamic compensation in control. In addition, they may also be used to simplify convergence proofs in nonlinear control schemes where detailed estimates of the dynamics are required, and they are useful in a variety of planning and optimization problems to prove the correctness of algorithms. >

8 citations

Proceedings Article
01 Jan 1989

7 citations


Cited by
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MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

6,340 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design), where the task is to determine control inputs to drive a robot from an unknown position to an unknown target.
Abstract: This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an ...

2,993 citations

Book
20 May 2005
TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
Abstract: A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.

1,811 citations

Proceedings ArticleDOI
10 May 1999
TL;DR: A state-space perspective on the kinodynamic planning problem is presented, and a randomized path planning technique that computes collision-free kinodynamic trajectories for high degree-of-freedom problems is introduced.
Abstract: The paper presents a state-space perspective on the kinodynamic planning problem, and introduces a randomized path planning technique that computes collision-free kinodynamic trajectories for high degree-of-freedom problems. By using a state space formulation, the kinodynamic planning problem is treated as a 2n-dimensional nonholonomic planning problem, derived from an n-dimensional configuration space. The state space serves the same role as the configuration space for basic path planning. The bases for the approach is the construction of a tree that attempts to rapidly and uniformly explore the state space, offering benefits that are similar to those obtained by successful randomized planning methods, but applies to a much broader class of problems. Some preliminary results are discussed for an implementation that determines the kinodynamic trajectories for hovercrafts and satellites in cluttered environments resulting in state spaces of up to twelve dimensions.

1,414 citations

Book
01 Jan 2005
TL;DR: In this article, a comprehensive set of modeling, analysis and design techniques for a class of simple mechanical control systems is presented, that is, systems whose Lagrangian is kinetic energy minus potential energy.
Abstract: This talk will outline a comprehensive set of modeling, analysis and design techniques for a class of mechanical systems. We concern ourselves with simple mechanical control systems, that is, systems whose Lagrangian is kinetic energy minus potential energy. Example devices include robotic manipulators, aerospace and underwater vehicles, and mechanisms that locomote exploiting nonholonomic constraints. Borrowing techniques from nonlinear control and geometric mechanics, we propose a coordinateinvariant control theory for this class of systems. First, we take a Riemannian geometric approach to modeling systems dened on smooth manifolds, subject to nonholonomic constraints, external forces and control forces. We also model mechanical systems on groups and symmetries. Second, we analyze some control-theoretic properties of this class of systems, including controllability, averaged response to oscillatory controls, and kinematic reductions. Finally, we exploit the modeling and analysis results to tackle control design problems. Starting from controllability and kinematic reduction assumptions we propose some algorithms for generating and tracking trajectories.

848 citations