Reaction Null Space of a multibody system with applications in robotics

Nenchev, Dragomir N.

doi:10.5194/ms-4-97-2013

Cited by 44 publications

(17 citation statements)

References 56 publications

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“…Recently, a new approach has been proposed. It is based on the optimal control of the manipulator global CoM [23,24]. The method is based on planning the displacements of the total CoM of the moving links.…”

Section: Introductionmentioning

confidence: 99%

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An Alternative Method for Shaking Force Balancing of the 3RRR PPM through Acceleration Control of the Center of Mass

et al. 2020

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The problem of shaking force balancing of robotic manipulators, which allows the elimination or substantial reduction of the variable force transmitted to the fixed frame, has been traditionally solved by optimal mass redistribution of the moving links. The resulting configurations have been achieved by adding counterweights, by adding auxiliary structures or, by modifying the form of the links from the early design phase. This leads to an increase in the mass of the elements of the mechanism, which in turn leads to an increment of the torque transmitted to the base (the shaking moment) and of the driving torque. Thus, a balancing method that avoids the increment in mass is very desirable. In this article, the reduction of the shaking force of robotic manipulators is proposed by the optimal trajectory planning of the common center of mass of the system, which is carried out by "bang-bang" profile. This allows a considerable reduction in shaking forces without requiring counterweights, additional structures, or changes in form. The method, already presented in the literature, is resumed in this case using a direct and easy to automate modeling technique based on fully Cartesian coordinates. This permits to express the common center of mass, the shaking force, and the shaking moment of the manipulator as simple analytic expressions. The suggested modeling procedure and balancing technique are illustrated through the balancing of the 3RRR planar parallel manipulator (PPM). Results from computer simulations are reported.

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Section: Introductionmentioning

confidence: 99%

“…The methods presented in References [23,24] use reference-point coordinates to calculate the position of the global CoM. Then, this position has to be expressed in terms of the degrees of freedom and some convenient relative coordinates.…”

Section: Introductionmentioning

confidence: 99%

An Alternative Method for Shaking Force Balancing of the 3RRR PPM through Acceleration Control of the Center of Mass

et al. 2020

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“…They provide a tractable planning method to avoid the pseudoinverse of the Jacobian matrix. Nenchev [10] summarizes results based on the application of Reaction Null-Space approach. Via the null-space and pseudo-inverse mapping of the coupling inertia, the joint space is decomposed into two complementary orthogonal subspaces which is useful for motion analysis and control of various unfixed-base systems.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for a Space-Based Manipulator System Based on Quantum Genetic Algorithm

Chen

Zhou

2017

Journal of Robotics

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In this study, by considering a space-based, n-joint manipulator system as research object, a kinematic and a dynamic model are constructed and the system’s nonholonomic property is discussed. In light of the nonholonomic property unique to space-based systems, a path planning method is introduced to ensure that when an end-effector moves to the desired position, a floating base achieves the expected pose. The trajectories of the joints are first parameterized using sinusoidal polynomial functions, and cost functions are defined by the pose deviation of the base and the positional error of the end-effector. At this stage, the path planning problem is converted into a target optimization problem, where the target is a function of the joints. We then adopt a quantum genetic algorithm (QGA) to solve this objective optimization problem to attain the optimized trajectories of the joints and then execute nonholonomic path planning. To test the proposed method, we carried out a simulation on a six-degree-of-freedom (DOF) space-based manipulator system (SBMS). The results showed that, compared to traditional genetic optimization algorithms, the QGA converges more rapidly and has a more accurate output.

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“…The nature of this approach (Briot et al 2012;Nenchev 2013) is based on the optimal control of the robot-manipulator center of masses. The nature of this approach (Briot et al 2012;Nenchev 2013) is based on the optimal control of the robot-manipulator center of masses.…”

Section: Introductionmentioning

confidence: 99%