Latest Papers

ASME Journal of Mechanisms and Robotics

  • Dynamics of Mobile Manipulators Using Dual Quaternion Algebra
    on September 14, 2022 at 12:00 am

    AbstractThis article presents two approaches to obtain the dynamical equations of mobile manipulators using dual quaternion algebra. The first one is based on a general recursive Newton–Euler formulation and uses twists and wrenches, which are propagated through high-level algebraic operations and works for any type of joints and arbitrary parameterizations. The second approach is based on Gauss’s Principle of Least Constraint (GPLC) and includes arbitrary equality constraints. In addition to showing the connections of GPLC with Gibbs–Appell and Kane’s equations, we use it to model a nonholonomic mobile manipulator. Our current formulations are more general than their counterparts in the state of the art, although GPLC is more computationally expensive, and simulation results show that they are as accurate as the classic recursive Newton–Euler algorithm.

Symbolic Differentiation Algorithm for Inverse Dynamics of Serial Robots With Flexible Joints


A new symbolic differentiation algorithm is proposed in this paper to automatically generate the inverse dynamics of flexible-joint robots in symbolic form, and results obtained can be used in real-time applications. The proposed method with O(n) computational complexity is developed based on the recursive Newton–Euler algorithm, the chain rule of differentiation, and the computer algebra system. The input of the proposed algorithm consists of symbolic matrices describing the kinematic and dynamic parameters of the robot. The output is the inverse dynamics solution written in portable and optimized code (C-code/Matlab-code). An exemplary, numerical simulation for inverse dynamics of the Kuka LWR4 robot with seven flexible joints is conducted using matlab, in which the computational time per cycle of inverse dynamics is about 0.02 ms. The numerical example provides very good matching results versus existing methods, while requiring much less computation time and complexity.
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