Robot trajectory generation using modified hidden Markov model and Lloyd's algorithm in joint space

Garrido, Javier; Yu, Wen; Li, Xiaoou

doi:10.1016/j.engappai.2016.03.006

Cited by 20 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this approach requires learning the target joint configuration. For upper-limb exoskeletons, the generation of reference trajectories is grounded in learning models, the most common of which are neural networks (NNs) [8,11,54], hidden Markov models (HMM) [55], dynamic-motion-primitive (DMP) models [45], and Gaussian mixture models [14,56]. Concretely, the idea behind LbD is to extract an adequate control law from demonstrations of human motion during ADL tasks.…”

Section: Approaches Based On Learning By Demonstration (Lbd)mentioning

confidence: 99%

Motion Planning of Upper-Limb Exoskeleton Robots: A Review

2020

View full text Add to dashboard Cite

(1) Background: Motion planning is an important part of exoskeleton control that improves the wearer’s safety and comfort. However, its usage introduces the problem of trajectory planning. The objective of trajectory planning is to generate the reference input for the motion-control system. This review explores the methods of trajectory planning for exoskeleton control. In order to reduce the number of surveyed papers, this review focuses on the upper limbs, which require refined three-dimensional motion planning. (2) Methods: A systematic search covering the last 20 years was conducted in Ei Compendex, Inspect-IET, Web of Science, PubMed, ProQuest, and Science-Direct. The search strategy was to use and combine terms “trajectory planning”, “upper limb”, and ”exoskeleton” as high-level keywords. “Trajectory planning” and “motion planning” were also combined with the following keywords: “rehabilitation”, “humanlike motion“, “upper extremity“, “inverse kinematic“, and “learning machine “. (3) Results: A total of 67 relevant papers were discovered. Results were then classified into two main categories of methods to plan trajectory: (i) Approaches based on Cartesian motion planning, and inverse kinematics using polynomial-interpolation or optimization-based methods such as minimum-jerk, minimum-torque-change, and inertia-like models; and (ii) approaches based on “learning by demonstration” using machine-learning techniques such as supervised learning based on neural networks, and learning methods based on hidden Markov models, Gaussian mixture models, and dynamic motion primitives. (4) Conclusions: Various methods have been proposed to plan the trajectories for upper-limb exoskeleton robots, but most of them plan the trajectory offline. The review approach is general and could be extended to lower limbs. Trajectory planning has the advantage of extending the applicability of therapy robots to home usage (assistive exoskeletons); it also makes it possible to mitigate the shortages of medical caregivers and therapists, and therapy costs. In this paper, we also discuss challenges associated with trajectory planning: kinematic redundancy and incompatibility, and the trajectory-optimization problem. Commonly, methods based on the computation of swivel angles and other methods rely on the relationship (e.g., coordinated or synergistic) between the degrees of freedom used to resolve kinematic redundancy for exoskeletons. Moreover, two general solutions, namely, the self-tracing configuration of the joint axis and the alignment-free configuration of the joint axis, which add the appropriate number of extra degrees of freedom to the mechanism, were employed to improve the kinematic incompatibility between human and exoskeleton. Future work will focus on online trajectory planning and optimal control. This will be done because very few online methods were found in the scope of this study.

show abstract

Section: Approaches Based On Learning By Demonstration (Lbd)mentioning

confidence: 99%

Motion Planning of Upper-Limb Exoskeleton Robots: A Review

2020

View full text Add to dashboard Cite

show abstract

“…Moreover, the analytical expressions for forward kinematic solutions of most parallel mechanisms are hard to obtain and only can find the numerical solutions. Another method is to perform the trajectory planning in operating space; it is intuitive to avoid obstacles and easy to track the end-effector position and posture [18,19], but the problem of kinematic singularity is difficult to address using such a method. To facilitate analyzing the dynamic performances of a hybrid manipulator, the trajectory planning problem is handled in the space of the output angle of joint moving platform (OAJ) [33].…”

Section: Trajectory Optimization Modellingmentioning

confidence: 99%

“…In the later iterations, the population gradually tends toward the region of the optimum, and the difference between the individuals is small, hence the exploration is selected to keep the excellent individuals to improve the search effective. The APCM operator can be expressed as (18) where max and min are maximum and minimum value of the individuals in contemporary population, max − min denotes the magnitude of decision space.…”

Section: Adaptive Precision-controllable Mutationmentioning

confidence: 99%

A Multi-objective Trajectory Planning Method Based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

Wang¹,

Li²,

Shuai³

et al. 2020

Preprint

View full text Add to dashboard Cite

A multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introducing three genetic operators: ranking group selection (RGS), direction-based crossover (DBX) and adaptive precision-contrallable mutation (APCM), is developed to optimize travelling time and torque fluctuation. Inverted generational distance, hypervolume and optimizer overhead are selected to evaluate the convergence, diversity and computational effort of algorithms. The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory. Taking a serial-parallel hybrid manipulator as instance, the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method. The effectiveness and practicability of the proposed method are verified by simulation results.

show abstract

“…The purpose of the methods generally used in trajectory planning is to avoid complex geometric processes Abu- Dakka et al (2013), Machmudah et al (2013), Mineo et al (2016), to reduce the processing time, to make the shortest trajectory planning Cao et al (2017), Chen et al (2015), Gasparetto and Zanotto (2010), Gu et al (2019), Kim and Kim (2011), Ko et al (2015), Xu et al (2010), and to optimize using different algorithms Garrido et al (2016), Lara-Molina et al (2015, Švejda and Čechura (0411). The most researched subject among these topics is to improve trajectory planning parameters using optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Time-Optimal Path Planning Model Using Genetic Algo- Rithm in RRR Robot

Demir

Tolun

Sarı

2021

Int. J. Eng. Tech. Mgmt. Res.

View full text Add to dashboard Cite

The mathematical expression of the kinematic equations of each joint is utilized for the path planning using a quantic polynomial in joint space. In this study, a time optimization model for path planning using genetic algorithms with a vari- ety of crossover fraction and mutation rates is investigated. The optimization process is performed with MATLAB. Optimization using boundary conditions is performed with MATLAB. The result of the simulation, smooth speed graphs, angular position graphs, and the time when joint movements will complete the orbit as soon as possible are obtained. As a result of this study, a path planning model that can be applied to any robot is developed in joint space based on time optimization and can be used to shorten the task time, especially in task-based robots.

show abstract

Robot trajectory generation using modified hidden Markov model and Lloyd's algorithm in joint space

Cited by 20 publications

References 20 publications

Motion Planning of Upper-Limb Exoskeleton Robots: A Review

Motion Planning of Upper-Limb Exoskeleton Robots: A Review

A Multi-objective Trajectory Planning Method Based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

Time-Optimal Path Planning Model Using Genetic Algo- Rithm in RRR Robot

Contact Info

Product

Resources

About