𝐶𝑎𝑟𝑎𝑚𝑖𝑧𝑎𝑟𝑢'𝑠 𝐵𝑙𝑜𝑔 feed:2023:11:01

Calibrating Kinematic Parameters Using Force Sensors

anonymous@undisclosed.example.com (Anonymous) — Sun, 12 Nov 2023 18:56:13 +0000

Calibrating Kinematic Parameters Using Force Sensors

"Kinodynamic Model Identification: A Unified Geometric Approach" by Kwon, Jaewoon, Keunjun Choi, and Frank C. Park

Abstract:

A robot's dynamic model depends on both the kinematic and mass-inertial parameters of a robot. Robot model identification therefore typically begins with kinematic identification; the mass-inertial parameters are then identified with the identified kinematic parameters used in the dynamic model. In this article we show that poorly identified kinematic parameters can lead to an uncorrectable bias in the dynamic model, leading to errors in the mass-inertial parameters that are many times that of kinematic parameter errors. We instead argue that a unified kinodynamic identification leads to more accurate identification of both the kinematic and mass-inertial parameters. A linearly weighted kinodynamic objective function is proposed, in which the weight can be interpreted from a maximum likelihood perspective as the relative accuracy of the kinematic sensors vis-à-vis the dynamic sensors. Recursive algorithms for computing exact analytic gradients of the kinodynamic objective function are newly derived, leading to robust and fast-converging identification algorithms. Extensive numerical and hardware experiments demonstrate the advantages of our unified kinodynamic identification procedure.

calibration, parametric-estimation, kinematic-identification, mass-inertial-parameters-identification, robotics

Lie theory for the roboticist

anonymous@undisclosed.example.com (Anonymous) — Sun, 12 Nov 2023 18:56:13 +0000

Lie theory for the roboticist

lie-theory, robotics, lie-algebra, lie-group

Optimization on Manifolds

anonymous@undisclosed.example.com (Anonymous) — Sun, 12 Nov 2023 18:56:13 +0000

Optimization on Manifolds

optimization, manifolds

Robot Design Optimization Using Meshes

anonymous@undisclosed.example.com (Anonymous) — Sun, 12 Nov 2023 18:56:14 +0000

Robot Design Optimization Using Meshes

"Physically Consistent Lie Group Mesh Models for Robot Design and Motion Co-Optimization" by Kwon, Jaewoon, Seunghyun Kim, and Frank C. Park.

Abstract:

With recent advances in rapid prototyping and mechatronics, the problem of simultaneous design and motion optimization, or the co-design problem, is becoming more and more relevant in robotics. For reasons of computational tractability, all existing methods use simplified approximations of a robot’s kinodynamic model. We empirically confirm that any model approximations that are not physically consistent, i.e., the kinodynamic model parameters do not accurately reflect the actual link shapes, can sometimes lead to large errors in the optimization. We then propose a physically consistent, Lie group-based mesh element model for robot links that (i) parametrizes a wide variety of link shapes, and (ii) provides closed-form analytic gradients for the mesh deformations. The latter property is key to developing efficient co-design optimization algorithms that significantly improve numerical convergence and stability. Our co-design methodology is validated through extensive numerical and hardware experiments involving 3D-printed serial and parallel robots.

robotics, lie-group, co-design-optimization