scholarly journals MOTION PLANNING OF ASSISTIVE EXOSKELETON IN HUMAN’S LOCOMOTION REHABILITATION

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
DUONG MIEN KA
Keyword(s):  
Motion Planning ◽  
Control Algorithm ◽  
Science And Technology ◽  
Motion Path ◽  
Balance Problem ◽  
Walking Pattern ◽  
Locomotion Pattern

Researches on rehabilitation exoskeleton system have bee n implementing in recent decades and have been achieving many advantages. However, most of reseaches have focused on more simplified systems such as rehabilitation exoskeleton for one or two joints or for one paralysed leg with the purpose of recovering human’s locomotion pattern. Researches on rehabilitation exoskeleton using for whole two paralysed legs are limited because of the complexity of balance issue for a combined human – exoskeleton system. Therefore, crutches are used to prevent the human from falling during human’s walking in recent researches. In order to abandon the crutches and help to recover human’s nomal walking pattern, the balance problem of combined human-exoskeleton system must be considered in control algorithm. In this paper, we build a motion path for a combined human-exoskeleton ensuring that the combined human-exoskeleton system can move in a balanced area. Our proposed paths are validated in the control algorithm for the HUALEX exoskeleton system in University of Electronic Science and Technology of China (UESTC).

Probabilistic Roadmaps Based Motion Planning of a 4 Degree of Freedom Robot

2012 ◽  
Vol 241-244 ◽  
pp. 1922-1930
Author(s):  
Yu Tian Liu
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Shortest Path ◽  
Degrees Of Freedom ◽  
Learning Phase ◽  
Degree Of Freedom ◽  
Motion Path ◽  
Probabilistic Roadmaps ◽  
Two Phases ◽  
4 Degrees Of Freedom

In this paper, we used a probabilistic roadmaps(PRM) method to plan a motion path for a 4 degrees of freedom(DOF) robot in static workspace. This methods includes two phases: a learning phase and a query phase. In learning phase, a roadmap is constructed and stored as a graph , in which stores all of the random collision-free configurations in free configuration space denoted by and keeps all of the edges corresponding to feasible paths between these configurations. In query phase, the algorithm tries to connect any given initial and goal configuration to the nodes in the graph. And then the Dijkstra's algorithm searches for a shortest path to concatenate these two nodes. The experiment result demonstrates that this method applying to this 4 degrees of freedom robot works well.

An optimal singularity-free motion planning method for a 6-DOF parallel manipulator

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shiqi Li ◽  
Dong Chen ◽  
Junfeng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Planning ◽  
Parallel Manipulator ◽  
Parallel Manipulators ◽  
Free Motion ◽  
Motion Path ◽  
Swarm Optimization ◽  
End Effector ◽  
Content Type ◽  
Multi Objective

Purpose This paper aims to present a method of optimal singularity-free motion planning under multiple objectives and multiple constrains for the 6-DOF parallel manipulator, which is used as an execution mechanism for the automated docking of components. Design/methodology/approach First, the distribution characteristics of the Jacobian matrix determinant in local workspace are studied based on the kinematics and a sufficient and necessary condition of singularity-free path planning in local workspace is proposed. Then, a singularity-free motion path of the end-effector is generated by a fifth-order B-spline curve and the corresponding trajectories of each actuator are obtained via the inverse kinematics. Finally, several objective functions are defined to evaluate the motion path and an improved multi-objective particle swarm optimization algorithm-based on the Pareto archive evolution is developed to obtain the optimal singularity-free motion trajectories. Findings If the initial pose and the target pose of the end-effector are both singularity-free, a singularity-free motion path can be planned in the local workspace as long as all the values of each pose elements in their own directions are monotonous. The improved multi-objective particle swarm optimization (IMPSO) algorithm is effective and efficient in the optimization of multi-objective motion planning model. The optimal singularity-free motion path of the end-effector is verified in the component docking test. The docking result is that the movable component is in alignment with the fixed one, which proves the feasibility and practicability of the proposed motion path method to some extent. Originality/value The proposed method has a certain novelty value in kinematic multi-objective motion planning of parallel manipulators; it also increases the application prospect of parallel manipulators and provides attractive solutions to component assembly for those organizations with limited cost and that want to find an option that is effective to be implemented.

Motion planning for walking pattern generation of humanoid

2007 ◽  
Author(s):  
Kensuke Harada ◽  
Shizuko Hattori ◽  
Hirohisa Hirukawa ◽  
Mitsuharu Morisawa ◽  
Shuuji Kajita ◽  
...  
Keyword(s):  
Motion Planning ◽  
Pattern Generation ◽  
Walking Pattern ◽  
Walking Pattern Generation

Attitude Control of Spacecraft Multibody System by Optimal Nonholonomic Motion Planning

2011 ◽  
Vol 383-390 ◽  
pp. 4257-4261
Author(s):  
Yun Ping Liu
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Attitude Control ◽  
Control Algorithm ◽  
Multibody System ◽  
Attitude Motion ◽  
Planning Problem ◽  
Motion Equations ◽  
Modified Newton Method ◽  
Nonholonomic Motion Planning

The optimal nonholonomic motion planning for spacecraft multibody system is researched. The attitude motion equations of spacecraft multibody system take on nonholonomic constraint without outside force. The control of system can be converted to the nonholonomic motion planning problem for a driftless system. Based on modified-Newton method, the optimal control algorithm of controlling spacecraft to desired attitude is accepted by optimal control algorithm and Ritz approximation theory. Quaternion is used to describe the kinematics equation, which avoids the singularity and has the benefit of small calculation and high precision. At last, numerical simulations of spacecraft with two reaction fly wheels has proved of the approach to be effective.

scholarly journals How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4012
Author(s):  
Milad Karimshoushtari ◽  
Carlo Novara ◽  
Fabio Tango
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Urban Areas ◽  
Control Algorithm ◽  
Transportation Systems ◽  
Imitation Learning ◽  
Planning And Control ◽  
Computation Efficiency ◽  
And Control

Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach.

scholarly journals Ship Trajectory Tracking Control System Design Based on Sliding Mode Control Algorithm

2018 ◽  
Vol 25 (3) ◽  
pp. 26-34 ◽  
Author(s):  
Yong Liu ◽  
Renxiang Bu ◽  
Xiaori Gao
Keyword(s):  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Guidance System ◽  
Motion Path ◽  
Sliding Mode Controller ◽  
Trajectory Tracking Control ◽  
Mode Control

Abstract The paper reports the design and tests of the planar autopilot navigation system in the three-degree-of-freedom (3-DOF) plane (surge, sway and yaw) for a ship. The aim of the tests was to check the improved maneuverability of the ship in open waters using the improved nonlinear control algorithm, developed based on the sliding mode control theory for the ship-trajectory tracking problem of under-actuated ships with static constraints, actuator saturation, and parametric uncertainties. With the integration of the simple increment feedback control law, the dynamic control strategy was developed to fulfill the under-actuated tracking and stabilization objectives. In addition, the LOS (line of sight) guidance system was applied to control the motion path, whereas the sliding mode controller was used to emulate the rudder angle and propeller rotational speed control. Firstly, simulation tests were performed to verify the validity of the basic model and the tracking control algorithm. Subsequently, full scale maneuverability tests were done with a novel container ship, equipped with trajectory tracking control and sliding mode controller algorithm, to check the dynamic stability performance of the ship. The results of the theoretical and numerical simulation on a training ship verify the invariability and excellent robustness of the proposed controller, which: effectively eliminates system chattering, solves the problem of lateral drift of the ship, and maintains the following of the trajectory while simultaneously achieving global stability and robustness.

scholarly journals Motion Planning and Reactive Control Algorithms for Object Manipulation in Uncertain Conditions

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 76
Author(s):  
Marco Costanzo ◽  
Giuseppe De Maria ◽  
Gaetano Lettera ◽  
Ciro Natale ◽  
Salvatore Pirozzi
Keyword(s):  
Motion Planning ◽  
Motion Control ◽  
Control Algorithm ◽  
State Of The Art ◽  
Object Manipulation ◽  
Control Algorithms ◽  
Reactive Control ◽  
Actual Object ◽  
Depth Images ◽  
Grasp Force

This work proposes the application of several smart strategies for object manipulation tasks. A real-time flexible motion planning method was developed to be adapted to typical in-store logistics scenarios. The solution combines and optimizes some state-of-the-art techniques to solve object recognition and localization problems with a new hybrid pipeline. The algorithm guarantees good robustness and accuracy for object detection through depth images. A standard planner plans collision-free trajectories throughout the whole task while a proposed reactive motion control is active. Distributed proximity sensors were adopted to locally modify the planned trajectory when unexpected or misplaced obstacles intervene in the scene. To implement a robust grasping phase, a novel slipping control algorithm was used. It dynamically computes the grasp force by adapting it to the actual object physical properties so as to prevent slipping. Experimental results carried out in a typical supermarket scenario demonstrate the effectiveness of the presented methods.

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