Fast Motion of Path Tracking Task with Constant Hand Speed Using Redundant Manipulator

2015 ◽  
Vol 27 (1) ◽  
pp. 74-82 ◽  
Author(s):  
Kousuke Okabe ◽  
◽  
Yasumichi Aiyama
Keyword(s):  
Motion Planning ◽  
State Space ◽  
Path Tracking ◽  
Tracking Task ◽  
Joint Torque ◽  
Redundant Manipulator ◽  
Fast Motion ◽  
Joint Velocity ◽  
Driving Torque ◽  
Joint Acceleration

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/09.jpg"" width=""300"" />Limits and a motion in state space</div> We propose a motion planning method for accelerating path-tracking task with constant hand speed using redundant manipulator. Tracking-speed should be increased to satisfy the joint torque and joint velocity limits. We propose a method using the state space of redundant manipulators to analyze these limits and to operate motion planning. This state space has a position on a trajectory, redundant pose, and redundant velocity axes. These limits are projected on state space. We consider, motion planning is path-finding problem on state space at a constant hand speed. To plan a faster motion, a constant hand speed is examined by finding a path with limits in state space. We use computer simulation to test and evaluate our proposed method on 3-link planar manipulator. Results demonstrated motion faster than a motion minimizing joint acceleration norm and a motion minimizing driving torque norm. We also verified a problem when our method was applied to an actual manipulator. </span>

Analyzing and Reducing Energy Usage in a Humanoid Robot During Standing Up and Sitting Down Tasks

2016 ◽  
Vol 13 (04) ◽  
pp. 1650014 ◽  
Author(s):  
Ercan Elibol ◽  
Juan Calderon ◽  
Martin Llofriu ◽  
Wilfrido Moreno ◽  
Alfredo Weitzenfeld
Keyword(s):  
Humanoid Robot ◽  
Center Of Pressure ◽  
Center Of Mass ◽  
Electrical Power ◽  
Joint Torque ◽  
Q Learning ◽  
Current Usage ◽  
Standing Up ◽  
Joint Velocity ◽  
Joint Acceleration

The aim of this paper is to reduce the energy consumption of a humanoid by analyzing electrical power as input to the robot and mechanical power as output. The analysis considers motor dynamics during standing up and sitting down tasks. The motion tasks of the humanoid are described in terms of joint position, joint velocity, joint acceleration, joint torque, center of mass (CoM) and center of pressure (CoP). To reduce the complexity of the robot analysis, the humanoid is modeled as a planar robot with four links and three joints. The humanoid robot learns to reduce the overall motion torque by applying Q-Learning in a simulated model. The resulting motions are evaluated on a physical NAO humanoid robot during standing up and sitting down tasks and then contrasted to a pre-programmed task in the NAO. The stand up and sit down motions are analyzed for individual joint current usage, power demand, torque, angular velocity, acceleration, CoM and CoP locations. The overall result is improved energy efficiency between 25–30% when compared to the pre-programmed NAO stand up and sit down motion task.

Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

Robotica ◽  
2019 ◽  
Vol 38 (6) ◽  
pp. 983-999
Author(s):  
Zhaoli Jia ◽  
Siyuan Chen ◽  
Zhijun Zhang ◽  
Nan Zhong ◽  
Pengchao Zhang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Joint Angle ◽  
Weighting Factor ◽  
Quadratic Program ◽  
Redundant Manipulators ◽  
Repetitive Motion ◽  
Acceleration Level ◽  
Primal Dual ◽  
Joint Velocity ◽  
Joint Acceleration

SUMMARYIn order to solve joint-angle drift problem of dual redundant manipulators at acceleration-level, an acceleration-level tri-criteria optimization motion planning (ALTC-OMP) scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor. This scheme can resolve the joint-angle drift problem of dual redundant manipulators which will arise in single criteria or bi-criteria scheme. In addition, the proposed scheme considers joint-velocity joint-acceleration physical limits. The proposed scheme can not only guarantee joint-velocity and joint-acceleration within their physical limits, but also ensure that final joint-velocity and joint-acceleration are near to zero. This scheme is realized by dual redundant manipulators which consist of left and right manipulators. In order to ensure the coordinated operation of manipulators, two motion planning problems are reformulated as two general quadratic program (QP) problems and further unified into one standard QP problem, which is solved by a simplified linear-variational-inequalities-based primal-dual neural network at the acceleration-level. Computer-simulation results based on dual PUMA560 redundant manipulators further demonstrate the effectiveness and feasibility of the proposed ALTC-OMP scheme to resolve joint-angle drift problem arising in the dual redundant manipulators.

scholarly journals InverseMuscleNET: Alternative Machine Learning Solution to Static Optimization and Inverse Muscle Modeling

2021 ◽  
Vol 15 ◽  
Author(s):  
Ali Nasr ◽  
Keaton A. Inkol ◽  
Sydney Bell ◽  
John McPhee
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Muscle Activation ◽  
Joint Angle ◽  
Learning Model ◽  
Joint Torque ◽  
Static Optimization ◽  
Machine Learning Model ◽  
Joint Velocity ◽  
Joint Acceleration

InverseMuscleNET, a machine learning model, is proposed as an alternative to static optimization for resolving the redundancy issue in inverse muscle models. A recurrent neural network (RNN) was optimally configured, trained, and tested to estimate the pattern of muscle activation signals. Five biomechanical variables (joint angle, joint velocity, joint acceleration, joint torque, and activation torque) were used as inputs to the RNN. A set of surface electromyography (EMG) signals, experimentally measured around the shoulder joint for flexion/extension, were used to train and validate the RNN model. The obtained machine learning model yields a normalized regression in the range of 88–91% between experimental data and estimated muscle activation. A sequential backward selection algorithm was used as a sensitivity analysis to discover the less dominant inputs. The order of most essential signals to least dominant ones was as follows: joint angle, activation torque, joint torque, joint velocity, and joint acceleration. The RNN model required 0.06 s of the previous biomechanical input signals and 0.01 s of the predicted feedback EMG signals, demonstrating the dynamic temporal relationships of the muscle activation profiles. The proposed approach permits a fast and direct estimation ability instead of iterative solutions for the inverse muscle model. It raises the possibility of integrating such a model in a real-time device for functional rehabilitation and sports evaluation devices with real-time estimation and tracking. This method provides clinicians with a means of estimating EMG activity without an invasive electrode setup.

scholarly journals MuscleNET: Mapping Electromyography to kinematic and dynamic biomechanical variables by machine learning

2021 ◽  
Author(s):  
Ali Nasr ◽  
Sydney Marie Bell ◽  
Jiayuan He ◽  
Rachel l Whittaker ◽  
Clark R Dickerson ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Joint Angle ◽  
Joint Torque ◽  
Regression Error ◽  
Joint Velocity ◽  
Muscle Models ◽  
Joint Acceleration ◽  
Semg Signals

Objective: This paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque. Approach: The regression models, collectively known as MuscleNET, take one of four forms: ANN (Forward Artificial Neural Network), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), and RCNN (Recurrent Convolutional Neural Network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model's input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models' inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data. Main results: Results were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals. Significance: All forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.

Bi-criteria minimization with MWVN–INAM type for motion planning and control of redundant robot manipulators

Robotica ◽  
2018 ◽  
Vol 36 (5) ◽  
pp. 655-675 ◽  
Author(s):  
Dongsheng Guo ◽  
Kene Li ◽  
Bolin Liao
Keyword(s):  
Motion Planning ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Redundancy Resolution ◽  
Redundant Robot ◽  
Planning And Control ◽  
New Type ◽  
Joint Velocity ◽  
And Control ◽  
Joint Acceleration

SUMMARYThis study proposes and investigates a new type of bi-criteria minimization (BCM) for the motion planning and control of redundant robot manipulators to address the discontinuity problem in the infinity-norm acceleration minimization (INAM) scheme and to guarantee the final joint velocity of motion to be approximate to zero. This new type is based on the combination of minimum weighted velocity norm (MWVN) and INAM criteria, and thus is called the MWVN–INAM–BCM scheme. In formulating such a scheme, joint-angle, joint-velocity, and joint-acceleration limits are incorporated. The proposed MWVN–INAM–BCM scheme is reformulated as a quadratic programming problem solved at the joint-acceleration level. Simulation results based on the PUMA560 robot manipulator validate the efficacy and applicability of the proposed MWVN–INAM–BCM scheme in robotic redundancy resolution. In addition, the physical realizability of the proposed scheme is verified in practical application based on a six-link planar robot manipulator.

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