Path planning of a free-floating space robot based on the degree of controllability

Xinghong, Huang; Jia, Yingmin; Xu, Shengyuan

doi:10.1007/s11431-016-6069-3

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…The system's ability to plan trajectory has been enhanced. The concept of Degree of Controllability (DOC) for underdriven systems is presented in Literature [12] as a way to provide efficient path planning algorithms for kinematic non-redundant free-floating robot systems. This strategy avoids the inefficient operation problem caused by internal coupling, and it is confirmed by arithmetic examples that it avoids the internal coupling problem and improves the performance of the operation.…”

Section: Introductionmentioning

confidence: 99%

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Ni,

Cao,

Jiang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning network algorithm. The path recognition system is optimized by using the memory system and the decision module guided by the attention mechanism, which solves the problems of the existing DRL intelligent robot path recognition method that cannot store long-time memory and the training time is too long. The system in this paper is validated by conducting path recognition experiments under different road conditions and conducting dynamic obstacle avoidance test experiments. The system can maintain excellent noise immunity and a short path recognition time of 51 ms under the conditions of missing road conditions, circuitous road conditions and large curvature, and in the presence of multiple dynamic obstacles, the system can maintain a collision rate close to 0 while maintaining a very high success rate of 0.98, and the required path recognition time is much lower than that of other methods, with an average reward value of 0.4151. This paper’s system is highly accurate in recognizing intelligent robot paths and has high application value and capability.

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Section: Introductionmentioning

confidence: 99%

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Ni,

Cao,

Jiang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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“…As a kind of feasible sensor with advantages of low power consumption, strong adaptability and low cost [1], digital cameras have been widely applied in robotics to realize target detection and tracking. In a machine vision-based robot, the visual detection system plays a very important role in realizing diverse robotic functions, such as target detection and tracking [2], [3], autonomous navigation [4], [5], mutual positioning [6], path planning [7]- [9], visual servoing [10], robot-human interaction [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of the Visual Detection System for Amphibious Robots

He¹,

Zhang²,

Zhu³

et al. 2019

International Journal of Robotics and Automation

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Aiming at application requirements of our spherical amphibious robot, which has one type of propulsion system to be used both on land and in water, an improved tracking learning detection (TLD) algorithm is designed and implemented, so as to improve the robustness and environmental adaptability of its visual detection system. First, binary normalized gradient method is implemented to reduce the number of image candidate samples. Second, the compressive sensing algorithm is introduced to speed up the image feature calculation. Then, some common videos are applied to test the performance of the improved TLD algorithm, and the results show that the average frame rate of the improved TLD algorithm is increased. Finally, to evaluate the feasibility and effectiveness of the improved TLD algorithm in the visual system of our amphibious spherical robot, a series of experiments are carried out with illumination changes and in underwater environment. Comparison experimental results demonstrated that the improved TLD algorithm has better effectiveness, robustness and real-time performance than the traditional TLD algorithm.

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“…For the free-floating space manipulator, motion between the base and joints satisfies the first-order nonholonomic constraint originally because of momentum conservation without external force [26]. Moreover, according to existing researches about kinematic and dynamic modelling for underactuated manipulators, the second-order nonholonomic constraint also exists in accelerations among active joints, passive joints, and the base.…”

Section: Introductionmentioning

confidence: 99%

Kinematic and Dynamic Characteristics of the Free-Floating Space Manipulator with Free-Swinging Joint Failure

Jia

Yuan

Chen

et al. 2019

International Journal of Aerospace Engineering

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For the free-floating space manipulator with free-swinging joint failure, motions among its active joints, passive joints, free-floating base, and end-effector are coupled. It is significant to make clear all motion coupling relationships, which are defined as "kinematic coupling relationships" and "dynamic coupling relationships," inside the system. With the help of conservation of system momentum, the kinematic model is established, and velocity mapping relation between active joints and passive joints, velocity mapping relation between active joints and base, velocity mapping relation between active joints and end-effector. We establish the dynamic model based on the Lagrange equation, and the system inertia matrix is partitioned according to the distribution of active joints, passive joints, and the base. Then, kinematic and dynamic coupling relationships are explicitly derived, and coupling indexes are defined to depict coupling degree. Motions of a space manipulator with free-swinging joint failure simultaneously satisfy the first-order nonholonomic constraint (kinematic coupling relationships) and the second-order nonholonomic constraint (dynamic coupling relationships), and the manipulator can perform tasks through motion planning and control. Finally, simulation experiments are carried out to verify the existence and correctness of the first-order and second-order nonholonomic constraints and display task execution effects of the space manipulator. This research analyzes the kinematic and dynamic characteristics of the free-floating space manipulator with free-swinging joint failure for the first time. It is the theoretical basis of free-swinging joint failure treatment for a space manipulator. Passive joints Base Space manipulator End-effector − Figure 1: Kinematic model of the n-DOF space manipulator with free-swinging joint failure.3 International Journal of Aerospace Engineering R 3×n represents the contribution of θ to angular momentum L. J LM and J AM can be expressed as J LM = j LM1 , j LM2 ,⋯,j LMn and J AM = j AM1 , j AM2 ,⋯,j AMn , respectively, and we can find column vectors corresponding to passive joints and active joints to make up matrixesEquations (6) and (7) can be expressed in matrix form T P T ∈ R 3+p ×1 and the Jacobian matrix iswhere J eA v is the first three rows of J eA . The kinematic coupling index is the manipulability of J vePA . For the task to eliminate the base attitude deflection when regulating passive joints, we select the task velocity vector as v ωbP = θ T P , ω T 0 T ∈ R p+3 ×1 and the Jacobian matrix iswhere J bA ω is the last three row vectors of J bA . The kinematic coupling index is the manipulability of J ωbPA . To eliminate base attitude disturbance, we keep ω 0 ≡ 0 [28]. For general space tasks, we should select v task = v task1 , v task2 ,⋯,v task f T ∈ R f ×1 according to controlled passive units for current task requirements. f is the dimensionality of v task . Then, the corresponding row vectors from J t need to be taken out to construct the Jacobian matrix:...

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Path planning of a free-floating space robot based on the degree of controllability

Cited by 10 publications

References 18 publications

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Design and Implementation of the Visual Detection System for Amphibious Robots

Kinematic and Dynamic Characteristics of the Free-Floating Space Manipulator with Free-Swinging Joint Failure

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