The Experts below are selected from a list of 35475 Experts worldwide ranked by ideXlab platform
Lydia E. Kavraki - One of the best experts on this subject based on the ideXlab platform.
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Teaching Robot Motion Planning
2020Co-Authors: Mark Moll, Janice D. Bordeaux, Lydia E. KavrakiAbstract:Robot Motion Planning is a fairly intuitive and engaging topic, yet it is difficult to teach. The material is taught in undergraduate and graduate robotics classes in computer science, electrical engineering, mechanical engineering and aeronautical engineering, but at an abstract level. Deep learning could be achieved by having students implement and test different Motion Planning strategies. However, a full implementation of Motion Planning algorithms by undergraduates is practically impossible in the context of a single class, even by students proficient in programming. By helping undergraduates grasp Motion Planning concepts in series of courses designed for increasing advanced levels, we can open the field to young and enthusiastic talent. This cannot be done by asking students to implement Motion Planning algorithms from scratch or access thousands of lines of code and just figure out how things work. We present an ongoing project to develop microworld software and a modeling curriculum that supports undergraduate acquisition of Motion Planning knowledge and tool use by computer science and engineering students.
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the open Motion Planning library
IEEE Robotics & Automation Magazine, 2012Co-Authors: Ioan A Sucan, Mark Moll, Lydia E. KavrakiAbstract:The open Motion Planning library (OMPL) is a new library for sampling-based Motion Planning, which contains implementations of many state-of-the-art Planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex Motion Planning problems with minimal input. OMPL facilitates the addition of new Motion Planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based Motion Planning. The library is also available for use through Robot Operating System (ROS).
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Motion Planning with Complex Goals
IEEE Robotics & Automation Magazine, 2011Co-Authors: Amit Bhatia, Lydia E. Kavraki, Matthew R. Maly, Moshe Y. VardiAbstract:This article describes approach for solving Motion Planning problems for mobile robots involving temporal goals. Traditional Motion Planning for mobile robotic systems involves the construction of a Motion plan that takes the system from an initial state to a set of goal states while avoiding collisions with obstacles at all times. The Motion plan is also required to respect the dynamics of the system that are typically described by a set of differential equations. A wide variety of techniques have been pro posed over the last two decades to solve such problems [1], [2].
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kinodynamic Motion Planning by interior exterior cell exploration
WAFR, 2009Co-Authors: Ioan A Sucan, Lydia E. KavrakiAbstract:Over the last two decades, Motion Planning [4, 15, 17] has grown from a field that considered basic geometric problems to a field that addresses Planning for complex robots with kinematic and dynamic constraints [5]. Applications of Motion Planning have also expanded to fields such as graphics and computational biology [16].
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Decomposition-based Motion Planning: a framework for real-time Motion Planning in high-dimensional configuration spaces
Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001Co-Authors: Oliver Brock, Lydia E. KavrakiAbstract:Research in Motion Planning has been striving to develop faster Planning algorithms in order to be able to address a wider range of applications. In this paper a novel real-time Motion Planning framework, called decomposition-based Motion Planning, is proposed. It is particularly well suited for Planning problems that arise in service and field robotics. It decomposes the original Planning problem into simpler sub-problems, whose successive solution empirically results in a large reduction of the overall complexity. A particular implementation of decomposition-based Planning is proposed. Experiments with an eleven degree-of-freedom mobile manipulator are presented.
Steven M. Lavalle - One of the best experts on this subject based on the ideXlab platform.
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Motion Planning
IEEE Robotics & Automation Magazine, 2011Co-Authors: Steven M. LavalleAbstract:Here, we give the Part II of the two-part tutorial. Part I emphasized the basic problem formulation, mathematical concepts, and the most common solutions. The goal of Part II is to help you understand current robotics challenges from a Motion Planning perspective.
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Current Issues in Sampling-Based Motion Planning
Springer Tracts in Advanced Robotics, 2005Co-Authors: S.r. Lindemann, Steven M. LavalleAbstract:In this paper, we discuss the field of sampling-based Motion Planning. In contrast to methods that construct boundary representations of configuration space obstacles, samplingbased methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful Motion Planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how sampling-based Motion Planning has developed. We then discuss a variety of important issues for sampling-based Motion Planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in sampling-based Motion Planning.
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A game-theoretic framework for robot Motion Planning
1996Co-Authors: Steven M. LavalleAbstract:The primary contribution of this dissertation is the presentation of a dynamic game-theoretic framework that is used as an analytical tool and unifying perspective for a wide class of problems in robot Motion Planning. The framework provides a precise mathematical characterization that can incorporate any of the essential features of decision theory, stochastic optimal control, and traditional multiplayer games. The determination of strategies that optimize some precise performance functionals is central to these subjects, and is of fundamental value for many types of Motion Planning problems. The basic Motion Planning problem is to compute a collision-free trajectory for the robot, given perfect sensing, an exact representation of the environment, and completely predictable execution. The best-known algorithms have exponential complexity, and most extensions to the basic problem are provably intractable. The techniques in this dissertation characterize several extensions to the basic Motion Planning problem, and lead to computational techniques that provide practical, approximate solutions. A general perspective on Motion Planning is also provided by relating the similarities between various extensions to the basic problem within a common mathematical framework. Modeling, analysis, algorithms, and computed examples are presented for each of three problems: (1) Motion Planning under uncertainty in sensing and control; (2) Motion Planning under environment uncertainties; and (3) multiple-robot Motion Planning. Traditional approaches to the first problem are often based on a methodology known as preimage Planning, which involves worst-case analysis. In this context, a general method for determining feedback strategies is developed by blending ideas from stochastic optimal control and dynamic game theory with traditional preimage Planning concepts. This generalizes classical preimages to performance preimages and preimage plans to Motion strategies with information feedback. For the second problem, robot strategies are analyzed and determined for situations in which the environment of the robot is changing, but not completely predictable. Several new applications are identified for this context. The changing environment is treated in a flexible manner by combining traditional configuration space concepts with stochastic optimal control concepts. For the third problem, dynamic game-theoretic and multiobjective optimization concepts are applied to Motion Planning for multiple robots. This allows the synthesis of Motion plans that simultaneously optimize an independent performance criterion for each robot. Several versions of the formulation are considered: fixed-path coordination, coordination on independent configuration-space roadmaps, and centralized Planning.
Mariusz Janiak - One of the best experts on this subject based on the ideXlab platform.
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Motion Planning of the Underactuated Manipulators with Friction in Constrained State Space Motion Planning of the Underactuated Manipulators with Friction in Constrained State Space Motion Planning of the Underactuated Manipulators with Friction in C
2020Co-Authors: Adam Ratajczak, Mariusz JaniakAbstract:This paper addresses the constrained Motion Planning problem for passive joint manipulators with friction. Constraints are imposed on a system state space vector. The dynamics of underactuated manipulators are described by a control‐ane system with a drift term. In order to solve the constrained Motion Planning problem the imbalanced Jacobian algorithm derived from an endogenous configuration space approach is used. The state space constraints are included into the system representation of the manipulator dynamics. The extended system is subject to regularisation because of the Jacobian singularities, then the unconstrained Motion Planning problem is solved for the regularised system. The solution of the Motion Planning problem for this system is equivalent to the solution of the constrained Motion Planning problem for an original system. Performance of the imbalanced Jacobian algorithm has been demonstrated with series of simulation for the two kinds of manipulators with and without friction.
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constrained Motion Planning of nonholonomic systems
Systems & Control Letters, 2011Co-Authors: Mariusz Janiak, Krzysztof TchońAbstract:This paper addresses the constrained Motion Planning problem for nonholonomic systems represented by driftless control systems with output. The problem consists in defining a control function driving the system output to a desirable point at a given time instant, whereas state and control variables remain over the control horizon within prescribed bounds. The state and control constraints are handled by extending the control system with a pair of state equations driven by the violation of constraints, and adding regularizing perturbations. For the regularized system a Jacobian Motion Planning algorithm is designed, called imbalanced. Solutions of example constrained Motion Planning problems for the rolling ball illustrate the theoretical concepts.
Eiichi Yoshida - One of the best experts on this subject based on the ideXlab platform.
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Whole-Body Motion Planning
Humanoid Robotics: A Reference, 2019Co-Authors: Eiichi Yoshida, Fumio Kanehiro, Jean-paul LaumondAbstract:This chapter addresses whole-body Motion Planning for humanoid robots. Taking advantage of recent progress of Motion Planning techniques for many degree of freedom (DOF) systems, early work in humanoid Motion Planning started with a two-stage approach that utilizes kinematic and geometric Motion Planning to plan a rough path that is later transformed into a whole-body Motion including locoMotion with a dynamic biped walking pattern generator. Subsequent progress beyond this functional decomposition is to exploit all the DOFs for the desired task. Whole-body Motion Planning was then tackled by integrating generalized inverse kinematics that allows achieving the specified tasks by taking into account such constraints as balance, foot positions, or joint limits at the same time. Some applications are presented such as reactive Planning in changing cluttered environments, whole-body manipulation of bulky objects, and footstep Planning by variable kinematic modeling of footholds. The effectiveness of the proposed methods has been validated through experiments with the human-size humanoid platform HRP-2.
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IROS - Quotient-Space Motion Planning
2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018Co-Authors: Andreas Orthey, Adrien Escande, Eiichi YoshidaAbstract:A Motion Planning algorithm computes the Motion of a robot by computing a path through its configuration space. To improve the runtime of Motion Planning algorithms, we propose to nest robots in each other, creating a nested quotient-space decomposition of the configuration space. Based on this decomposition we define a new roadmap-based Motion Planning algorithm called the Quotient-space roadMap Planner (QMP). The algorithm starts growing a graph on the lowest dimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open Motion Planning library (OMPL).
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Quotient-Space Motion Planning
arXiv: Robotics, 2018Co-Authors: Andreas Orthey, Adrien Escande, Eiichi YoshidaAbstract:A Motion Planning algorithm computes the Motion of a robot by computing a path through its configuration space. To improve the runtime of Motion Planning algorithms, we propose to nest robots in each other, creating a nested quotient-space decomposition of the configuration space. Based on this decomposition we define a new roadmap-based Motion Planning algorithm called the Quotient-space roadMap Planner (QMP). The algorithm starts growing a graph on the lowest dimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open Motion Planning library (OMPL).
Krzysztof Tchoń - One of the best experts on this subject based on the ideXlab platform.
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Motion Planning of Nonholonomic Systems with Dynamics
Computational Kinematics, 2020Co-Authors: Krzysztof Tchoń, Janusz Jakubiak, Łukasz MałekAbstract:In the framework of control theory the Motion Planning problem of a robotic system amounts to determining a control function that steers the system from an initial state to a prescribed desirable state in such a way that the resulting state or output trajectory stays within an admissible region, free from obstacles. Basically, Motion Planning algorithms are devised to solve the problem without obstacles, and then suitable obstacle avoidance mechanisms are added. In this paper we shall concentrate on Motion Planning algorithms without obstacles for nonholonomic robotic systems. A comprehensive overview of approaches to the Motion Planning problem for the holonomic and the nonholonomic kinematics is contained in [7].
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Lagrangian Jacobian Motion Planning: A Parametric Approach
Journal of Intelligent and Robotic Systems, 2017Co-Authors: Ida Góral, Krzysztof TchońAbstract:This paper addresses the Motion Planning problem of nonholonomic robotic systems. The system's kinematics are described by a driftless control system with output. It is assumed that the control functions are represented in a parametric form, as truncated orthogonal series. A new Motion Planning algorithm is proposed based on the solution of a Lagrange-type optimisation problem stated in the linear approximation of the parametrised system. Performance of the algorithm is illustrated by numeric computations for a Motion Planning problem of the rolling ball.
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Dynamics and Motion Planning of Trident Snake Robot
Journal of Intelligent and Robotic Systems, 2013Co-Authors: Zuzanna Pietrowska, Krzysztof TchońAbstract:The trident snake robot is a mechanical device that serves as a demanding testbed for Motion Planning and control algorithms of constrained non-holonomic systems. This paper provides the equations of Motion and addresses the Motion Planning problem of the trident snake with dynamics, equipped with either active joints (undulatory locoMotion) or active wheels (wheeled locoMotion). Thanks to a partial feedback linearization of the dynamics model, the Motion Planning problem basically reduces to a constrained kinematic Motion Planning. Two kinds of constraints have been taken into account, ensuring the regularity of the feedback and the collision avoidance between the robot's arms and body. Following the guidelines of the endogenous configuration space approach, two Jacobian Motion Planning algorithms have been designed: the singularity robust Jacobian algorithm and the imbalanced Jacobian algorithm. Performance of these algorithms have been illustrated by computer simulations.
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constrained Motion Planning of nonholonomic systems
Systems & Control Letters, 2011Co-Authors: Mariusz Janiak, Krzysztof TchońAbstract:This paper addresses the constrained Motion Planning problem for nonholonomic systems represented by driftless control systems with output. The problem consists in defining a control function driving the system output to a desirable point at a given time instant, whereas state and control variables remain over the control horizon within prescribed bounds. The state and control constraints are handled by extending the control system with a pair of state equations driven by the violation of constraints, and adding regularizing perturbations. For the regularized system a Jacobian Motion Planning algorithm is designed, called imbalanced. Solutions of example constrained Motion Planning problems for the rolling ball illustrate the theoretical concepts.
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Motion Planning in velocity affine mechanical systems
International Journal of Control, 2010Co-Authors: Janusz Jakubiak, Krzysztof Tchoń, Wladyslaw MagieraAbstract:We address the Motion Planning problem in specific mechanical systems whose linear and angular velocities depend affinely on control. The configuration space of these systems encompasses the rotation group, and the Motion Planning involves the system orientation. Derivation of the Motion Planning algorithm for velocity affine systems has been inspired by the continuation method. Performance of this algorithm is illustrated with examples of the kinematics of a serial nonholonomic manipulator, the plate-ball kinematics and the attitude control of a rigid body.
