18,872 research outputs found
Efficient dynamic simulation for multiple chain robotic mechanisms
An efficient O(mN) algorithm for dynamic simulation of simple closed-chain robotic mechanisms is presented, where m is the number of chains, and N is the number of degrees of freedom for each chain. It is based on computation of the operational space inertia matrix (6 x 6) for each chain as seen by the body, load, or object. Also, computation of the chain dynamics, when opened at one end, is required, and the most efficient algorithm is used for this purpose. Parallel implementation of the dynamics for each chain results in an O(N) + O(log sub 2 m+1) algorithm
Parallel algorithms for computation of the manipulator inertia matrix
The development of an O(log2N) parallel algorithm for the manipulator inertia matrix is presented. It is based on the most efficient serial algorithm which uses the composite rigid body method. Recursive doubling is used to reformulate the linear recurrence equations which are required to compute the diagonal elements of the matrix. It results in O(log2N) levels of computation. Computation of the off-diagonal elements involves N linear recurrences of varying-size and a new method, which avoids redundant computation of position and orientation transforms for the manipulator, is developed. The O(log2N) algorithm is presented in both equation and graphic forms which clearly show the parallelism inherent in the algorithm
A spatial operator algebra for manipulator modeling and control
A recently developed spatial operator algebra, useful for modeling, control, and trajectory design of manipulators is discussed. The elements of this algebra are linear operators whose domain and range spaces consist of forces, moments, velocities, and accelerations. The effect of these operators is equivalent to a spatial recursion along the span of a manipulator. Inversion of operators can be efficiently obtained via techniques of recursive filtering and smoothing. The operator algebra provides a high level framework for describing the dynamic and kinematic behavior of a manipulator and control and trajectory design algorithms. The interpretation of expressions within the algebraic framework leads to enhanced conceptual and physical understanding of manipulator dynamics and kinematics. Furthermore, implementable recursive algorithms can be immediately derived from the abstract operator expressions by inspection. Thus, the transition from an abstract problem formulation and solution to the detailed mechanizaton of specific algorithms is greatly simplified. The analytical formulation of the operator algebra, as well as its implementation in the Ada programming language are discussed
Safe Robotic Grasping: Minimum Impact-Force Grasp Selection
This paper addresses the problem of selecting from a choice of possible
grasps, so that impact forces will be minimised if a collision occurs while the
robot is moving the grasped object along a post-grasp trajectory. Such
considerations are important for safety in human-robot interaction, where even
a certified "human-safe" (e.g. compliant) arm may become hazardous once it
grasps and begins moving an object, which may have significant mass, sharp
edges or other dangers. Additionally, minimising collision forces is critical
to preserving the longevity of robots which operate in uncertain and hazardous
environments, e.g. robots deployed for nuclear decommissioning, where removing
a damaged robot from a contaminated zone for repairs may be extremely difficult
and costly. Also, unwanted collisions between a robot and critical
infrastructure (e.g. pipework) in such high-consequence environments can be
disastrous. In this paper, we investigate how the safety of the post-grasp
motion can be considered during the pre-grasp approach phase, so that the
selected grasp is optimal in terms applying minimum impact forces if a
collision occurs during a desired post-grasp manipulation. We build on the
methods of augmented robot-object dynamics models and "effective mass" and
propose a method for combining these concepts with modern grasp and trajectory
planners, to enable the robot to achieve a grasp which maximises the safety of
the post-grasp trajectory, by minimising potential collision forces. We
demonstrate the effectiveness of our approach through several experiments with
both simulated and real robots.Comment: To be appeared in IEEE/RAS IROS 201
A reduced-order strategy for 4D-Var data assimilation
This paper presents a reduced-order approach for four-dimensional variational
data assimilation, based on a prior EO F analysis of a model trajectory. This
method implies two main advantages: a natural model-based definition of a mul
tivariate background error covariance matrix , and an important
decrease of the computational burden o f the method, due to the drastic
reduction of the dimension of the control space. % An illustration of the
feasibility and the effectiveness of this method is given in the academic
framework of twin experiments for a model of the equatorial Pacific ocean. It
is shown that the multivariate aspect of brings additional
information which substantially improves the identification procedure. Moreover
the computational cost can be decreased by one order of magnitude with regard
to the full-space 4D-Var method
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