Modelling and Control of Robot Manipulators

By J. Rigelsford. 2000

1,175 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8ded110268fbb9a8b7f6854b8bc0ff4970b6d1b3

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In Control and Systems Engineering

Citations in the corpus

Planning algorithms (2006, methodology): “The standard approach in many robotics books [368, 858, 908, 994] is to introduce the kinematic chain formulations and DH parameters in the first couple of chapters, and then move on to topics that are crucial for controlling robot manipulators, including dynamics modeling, singularities, manipulabi”
Motor interference between Humans and Humanoid Robots: Effect of Biological and Artificial Motion (2005, methodology): “3 where the inverse kinematics is solved using Jacobian pseudo-inverse with null space optimization [24].”
A probabilistic approach to robot trajectory generation (2013, methodology): “…representations use time- or 1 Intelligent Autonomous Systems lab, Technische Universitat Darmstadt, Germany {paraschos,neumann}@ias.tu-darmstadt.de 2 Robot Learning Group, Max-Planck Institute for Intelligent Systems, Tuebingen, Germany mail@jan-peters.net phase-dependent policies [1], [6], [9].”
Sensor-assisted adaptive motor control under continuously varying context (2007, background): “However, since for the combination of end-effector and object, both the inertia tensor and the center of mass vary, we need to estimate 10 hidden context variables (Sciavicco and Siciliano, 2000).”
Minimum-time trajectory planning of mechanical manipulators under dynamic constraints (2002, methodology): “For a discussion on the presented cubic spline trajectory planning see the equivalent derivation of Sciavicco and Siciliano (2000).”
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation (2020, background): “Since then, task frames in the form of task spaces have been used extensively in robotics [6].”
Learning objective functions for manipulation (2013, background): “…to the Moore-Penrose inverse of J, and (I − J † J) is an orthogonal projector in the null-space of J. Note that this is a fairly standard way of solving the inverse kinematics problem [22], except that here we attempt to learn the cost function C(q) to be minimized in the null space of the task.”
Learning Forward Models for the Operational Space Control of Redundant Robots (2010, background): “In this section, we give some background information (Sciavicco and Siciliano, 2000) on joint to operational space mappings with a focus on the velocity level.”
Feedback Control of Dynamic Bipedal Robot Locomotion (2007, methodology): “Other user-oriented sources on the use of Lagrange’s method for the derivation of equations of motions for rigid-body mechanical systems from a roboticist’s perspective are [60,71,164,206,218].”
Learning, planning, and control for quadruped locomotion over challenging terrain (2011, background): “A very straightforward way to remove the complications associated with the floating base is to directly derive the equation of motion in the constraint space (Sciavicco and Siciliano 2000) and reduce the problem to a fixed-base problem.”
Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators (2003, methodology): “…overshoot, minimum energy, or minimum time, the actuator torque functions are calculated using dynamic equations of motion of the manipulator, obtained usually from the closed-form Lagrange–Euler formulation or the forward-backward recursive Newton–Euler formulation (Sciavicco and Siciliano, 2000).”
Learning variable impedance control (2011, methodology): “…by determining the configuration at a given point in the task, the apparent inertia at the end-effector is co-determined (Hogan 1985b; Khatib 1995; Sciavicco and Siciliano 2000b), and second by possibly purposefully using a reference trajectory which is far from the actually followed trajectory…”
Policy Gradient Methods for Robotics (2006, neutral): “, [43], [44].”
Multi-task Gaussian Process Learning of Robot Inverse Dynamics (2008, background): “It can be shown that the required torque for the j joint can be written as [4] τ j (x) = ∑J j=j y T jj′(x)π m j yjj′ : R 3J 7→ R(10), (1) where theyjj′ ’s are vector-valued functions of x, andπ m j ∈ R 10 is the vector of inertial parameters 1”
Process Control: Modeling, Design and Simulation (2003, background): “…of application of smart material systems: » the seismic protection of cultural heritage structures [1] » harvesting energy from structural vibration [2] » morphing aircraft [3] » lightweight actuators for aerospace applications such as adaptive optics [4] » noise and vibration cancellation [5] »…”
Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors (2008, methodology): “For the distal humerus, the SoR was defined by applying the Denavit– Hartenberg method [24] at the elbow FL-EX hinge joint (Fig.”