Smooth trajectory generation and hybrid B-splines-Quaternions based tool path interpolation for a 3T1R parallel kinematic milling robot
- URL: http://arxiv.org/abs/2602.16758v1
- Date: Wed, 18 Feb 2026 11:57:52 GMT
- Title: Smooth trajectory generation and hybrid B-splines-Quaternions based tool path interpolation for a 3T1R parallel kinematic milling robot
- Abstract summary: This paper presents a smooth trajectory generation method for a four-degree-of-freedom parallel milling robot.<n>The proposed approach integrates B-spline and Quaternion kinematic techniques to manage decoupled position and orientation data points.<n>The method offers enhanced accuracy, reduced velocity fluctuations, and computational efficiency compared to conventional methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper presents a smooth trajectory generation method for a four-degree-of-freedom parallel kinematic milling robot. The proposed approach integrates B-spline and Quaternion interpolation techniques to manage decoupled position and orientation data points. The synchronization of orientation and arc-length-parameterized position data is achieved through the fitting of smooth piece-wise Bezier curves, which describe the non-linear relationship between path length and tool orientation, solved via sequential quadratic programming. By leveraging the convex hull properties of Bezier curves, the method ensures spatial and temporal separation constraints for multi-agent trajectory generation. Unit quaternions are employed for orientation interpolation, providing a robust and efficient representation that avoids gimbal lock and facilitates smooth, continuous rotation. Modifier polynomials are used for position interpolation. Temporal trajectories are optimized using minimum jerk, time-optimal piece-wise Bezier curves in two stages: task space followed by joint space, implemented on a low-cost microcontroller. Experimental results demonstrate that the proposed method offers enhanced accuracy, reduced velocity fluctuations, and computational efficiency compared to conventional interpolation methods.
Related papers
- Biconvex Optimization for Smooth Minimum-Time Trajectories around Convex Obstacles [49.315843050082925]
We present a bi approach for minimum-time motion planning convex obstacles that is guaranteed to converge.<n>We find that the proposed method produces high-quality trajectories with times to produce state-of-the-art decomposition of variables.
arXiv Detail & Related papers (2026-08-03T19:48:24Z) - Natural Functional Gradients for Smooth Trajectory Optimization [11.331190543795914]
We propose a trajectory optimization framework that performs geometry-aware updates directly in function space using natural functional gradients.<n>We derive a practical Monte-Carlo estimator of the natural functional gradient that requires only black-box trajectory evaluations.<n> Experiments on constrained robotic manipulation tasks demonstrate that the proposed method improves trajectory feasibility and produces smoother motions.
arXiv Detail & Related papers (2026-05-27T09:25:57Z) - Efficient Path Generation with Curvature Guarantees by Mollification [2.6381163133447836]
We present a method to regularize non-differentiable functions and generate feasible paths through mollification.<n>Specifically, we approximate an arbitrary path with a differentiable function that can converge to it with arbitrary precision.<n>The proposed approach is analytically more efficient than standard methods, enabling real-time implementation on microcontrollers.
arXiv Detail & Related papers (2025-12-15T10:48:42Z) - Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction [14.628742412460346]
This paper presents an end-to-end framework for reconstructing 3D parametric curves directly from multi-view edge maps.<n>We propose a novel bi-directional coupling mechanism between parametric curves and edge-oriented Gaussian components.<n>Our method significantly reduces the parameter count during training, achieving both higher efficiency and superior performance compared to existing approaches.
arXiv Detail & Related papers (2025-06-26T15:48:08Z) - Gaussian Primitives for Deformable Image Registration [9.184092856125067]
Experimental results on brain MRI, lung CT, and cardiac MRI datasets demonstrate that GaussianDIR outperforms existing DIR methods in both accuracy and efficiency.
As a training-free approach, it challenges the stereotype that iterative methods are inherently slow and transcend the limitations of poor generalization.
arXiv Detail & Related papers (2024-06-05T15:44:54Z) - SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid 3D Registration [77.13381026159111]
We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration.<n>The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
arXiv Detail & Related papers (2024-05-30T15:55:04Z) - An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation [10.05584976985694]
3D-2D constraints provided by line features have been widely used in Visual Odometry (VO) and Structure-from-Motion (SfM) systems.<n>We propose a novel three-view pose solver based on rotation-translation decoupled estimation.
arXiv Detail & Related papers (2024-03-18T10:21:05Z) - A 5-Point Minimal Solver for Event Camera Relative Motion Estimation [47.45081895021988]
We introduce a novel minimal 5-point solver that estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines.
Our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%.
arXiv Detail & Related papers (2023-09-29T08:30:18Z) - Manifold Interpolating Optimal-Transport Flows for Trajectory Inference [64.94020639760026]
We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow)
MIOFlow learns, continuous population dynamics from static snapshot samples taken at sporadic timepoints.
We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
arXiv Detail & Related papers (2022-06-29T22:19:03Z) - Hybrid Trilinear and Bilinear Programming for Aligning Partially
Overlapping Point Sets [85.71360365315128]
In many applications, we need algorithms which can align partially overlapping point sets are invariant to the corresponding corresponding RPM algorithm.
We first show that the objective is a cubic bound function. We then utilize the convex envelopes of trilinear and bilinear monomial transformations to derive its lower bound.
We next develop a branch-and-bound (BnB) algorithm which only branches over the transformation variables and runs efficiently.
arXiv Detail & Related papers (2021-01-19T04:24:23Z) - DiffPD: Differentiable Projective Dynamics with Contact [65.88720481593118]
We present DiffPD, an efficient differentiable soft-body simulator with implicit time integration.
We evaluate the performance of DiffPD and observe a speedup of 4-19 times compared to the standard Newton's method in various applications.
arXiv Detail & Related papers (2021-01-15T00:13:33Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.