A Dual Quaternion based RRT* Path Planning Approach for Satellite Rendezvous and Docking
- URL: http://arxiv.org/abs/2512.17680v1
- Date: Fri, 19 Dec 2025 15:17:46 GMT
- Title: A Dual Quaternion based RRT* Path Planning Approach for Satellite Rendezvous and Docking
- Abstract summary: This paper proposes a sampling-based motion planner that employs a dual quaternion representation to generate smooth, collision-free pose trajectories for satellite rendezvous and docking under keep-out zone constraints.<n>The proposed planner integrates the dual quaternion algebra directly into an RRT* framework, thereby enabling natural screw motion in SE(3).<n>A comparison with a standard RRT* using separate translation and quaternion steering highlights the enhanced pose continuity and obstacle avoidance of the proposed method.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper proposes a sampling-based motion planner that employs a dual quaternion representation to generate smooth, collision-free six-degree-of-freedom pose trajectories for satellite rendezvous and docking under keep-out zone constraints. The proposed planner integrates the dual quaternion algebra directly into an RRT* framework, thereby enabling natural screw motion interpolation in SE(3). The dual quaternion-based RRT* has been implemented in Python and demonstrated on a representative multi-obstacle scenario. A comparison with a standard RRT* using separate translation and quaternion steering highlights the enhanced pose continuity and obstacle avoidance of the proposed method. The present approach is purely kinematic in nature and does not take into account relative orbital dynamics. Consequently, the resulting path provides a preliminary estimate for a subsequent optimisation-based trajectory planner, which will refine the motion with dynamic constraints for the purpose of practical satellite rendezvous and docking missions.
Related papers
- ASTRA: Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment [86.35109211408489]
We propose ASTRA, a framework that introduces 2D motion trajectories as explicit, texture-agnostic supervision for asynchronous dynamic reconstruction.<n>ASTRA preserves high-frequency spatial details and sustains strong robustness even under severe asynchrony with up to 25-frame offsets, achieving approximately 1.4 dB PSNR improvement, reducing temporal-offset MAE by 54.0%, and nearly quadrupling the synchronization success rate.
arXiv Detail & Related papers (2026-08-03T10:06:45Z) - AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
arXiv Detail & Related papers (2026-07-13T16:00:41Z) - AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust [1.7394635819080972]
Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority.<n>Prior geometric control approaches extend differential flatness through Hopf fibration-based attitude representations to support bidirectional thrust.<n>We propose a learning-based framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions.
arXiv Detail & Related papers (2026-05-23T00:02:22Z) - RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework [49.531001563853984]
RAD-2 is a unified generator-discriminator framework for closed-loop planning.<n>It produces diverse trajectory candidates, while an RL-optimized discriminator reranks these candidates according to their long-term driving quality.<n>It reduces the collision rate by 56% compared with strong diffusion-based planners.
arXiv Detail & Related papers (2026-04-16T17:59:44Z) - Smooth trajectory generation and hybrid B-splines-Quaternions based tool path interpolation for a 3T1R parallel kinematic milling robot [0.0]
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.
arXiv Detail & Related papers (2026-02-18T11:57:52Z) - Solving Geodesic Equations with Composite Bernstein Polynomials for Trajectory Planning [0.0]
Trajectories are planned over a cost surface that encodes obstacles as continuous fields rather than discrete boundaries.<n>Regions near obstacles are assigned higher costs, naturally encouraging the trajectory to maintain a safe distance while still allowing efficient routing through constrained spaces.<n>The method applies to both two- and three-dimensional environments and is suitable for ground, aerial, underwater, and space systems.
arXiv Detail & Related papers (2026-02-10T23:33:15Z) - Optimal Trajectory Planning for Orbital Robot Rendezvous and Docking [2.666234597824067]
Approaching a tumbling target safely is a critical challenge in space debris removal missions utilizing robotic manipulators onboard servicing satellites.<n>We propose a trajectory planning method based on nonlinear optimization for a close-range rendezvous to bring a free-floating, rotating debris object into the manipulator's workspace.<n>The proposed method introduces a dynamic keep-out sphere that adapts depending on the approach conditions, allowing for closer and safer access to the target.
arXiv Detail & Related papers (2025-12-26T06:11:54Z) - Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
arXiv Detail & Related papers (2025-09-18T03:51:06Z) - QuadKAN: KAN-Enhanced Quadruped Motion Control via End-to-End Reinforcement Learning [2.26464274357758]
We address vision-guided quadruped motion control with reinforcement learning (RL)<n>We propose QuadKAN, a cross-modal policy instantiated with Kolmogorov-Arnold Networks (KANs)<n>We show that QuadKAN achieves consistently higher returns, greater distances, and fewer collisions than state-of-the-art (SOTA) baselines.
arXiv Detail & Related papers (2025-08-26T16:05:32Z) - DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
We present DeSiRe-GS, a self-supervised gaussian splatting representation.<n>It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
arXiv Detail & Related papers (2024-11-18T05:49:16Z) - A Generic Framework for Clustering Vehicle Motion Trajectories [4.125187280299247]
We propose an effective non-parametric trajectory clustering framework consisting of five stages.
We investigate and evaluate the proposed framework on a challenging real-world dataset consisting of annotated trajectories.
We extend the framework to validate the augmentation of the real dataset with synthetic data generated by a Generative Adversarial Network (GAN)
arXiv Detail & Related papers (2020-09-25T21:46:37Z) - Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial
Vehicles (MAVs) [61.94975011711275]
We propose a geometrically based motion planning technique textquotedblleft RRT*textquotedblright; for this purpose.
In the proposed technique, we modified original RRT* introducing an adaptive search space and a steering function.
We have tested the proposed technique in various simulated environments.
arXiv Detail & Related papers (2020-08-29T09:55:49Z) - First Steps: Latent-Space Control with Semantic Constraints for
Quadruped Locomotion [73.37945453998134]
Traditional approaches to quadruped control employ simplified, hand-derived models.
This significantly reduces the capability of the robot since its effective kinematic range is curtailed.
In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space.
A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators.
We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
arXiv Detail & Related papers (2020-07-03T07:04:18Z) - Quaternion Equivariant Capsule Networks for 3D Point Clouds [58.566467950463306]
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations.
We connect dynamic routing between capsules to the well-known Weiszfeld algorithm.
Based on our operator, we build a capsule network that disentangles geometry from pose.
arXiv Detail & Related papers (2019-12-27T13:51:17Z)
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.