Motion planning of a redundant manipulator for the purpose of speed-up of the hands constant speed tasks

2013 ◽  
Author(s):  
Kousuke Okabe ◽  
Yasumichi Aiyama
Keyword(s):  
Motion Planning ◽  
Constant Speed ◽  
Redundant Manipulator ◽  
Speed Up

How to speed up to be in time: Action-judgment dissociations in children and adults

2004 ◽  
Vol 63 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Friedrich Wilkening ◽  
Claudia Martin
Keyword(s):  
Rating Scale ◽  
Constant Speed ◽  
Linear Condition ◽  
Speed Up ◽  
Nonlinear Condition ◽  
Speed Change ◽  
Catch Up ◽  
Normative Rule ◽  
Judgment Condition ◽  
Time Linear

Children 6 and 10 years of age and adults were asked how fast a toy car had to be to catch up with another car, the latter moving with a constant speed throughout. The speed change was required either after half of the time (linear condition) or half of the distance (nonlinear condition), and responses were given either on a rating scale (judgment condition) or by actually producing the motion (action condition). In the linear condition, the data patterns for both judgments and actions were in accordance with the normative rule at all ages. This was not true for the nonlinear condition, where children’s and adults’ judgment and also children’s action patterns were linear, and only adults’ action patterns were in line with the nonlinearity principle. Discussing the reasons for the misconceptions and for the action-judgment dissociations, a claim is made for a new view on the development of children’s concepts of time and speed.

OPTIMAL MOTION PLANNING OF A PLANAR PARALLEL MANIPULATOR WITH KINEMATICALLY REDUNDANT DEGREES OF FREEDOM

2016 ◽  
Vol 40 (3) ◽  
pp. 383-397 ◽  
Author(s):  
Bahman Nouri Rahmat Abadi ◽  
Sajjad Taghvaei ◽  
Ramin Vatankhah
Keyword(s):  
Motion Planning ◽  
Feedback Linearization ◽  
Degrees Of Freedom ◽  
Jacobian Matrix ◽  
Redundant Manipulator ◽  
Optimal Motion ◽  
Planar Parallel Manipulator ◽  
Optimal Motion Planning ◽  
Planning Algorithm ◽  
Actuation Method

In this paper, an optimal motion planning algorithm and dynamic modeling of a planar kinematically redundant manipulator are considered. Kinematics of the manipulator is studied, Jacobian matrix is obtained and the dynamic equations are derived using D’Alembert’s principle. Also, a novel actuation method is introduced and applied to the 3-PRPR planar redundant manipulator. In this approach, the velocity of actuators is determined in such a way to minimize the 2-norm of the velocity vector, subjected to the derived kinematic relations as constraints. Having the optimal motion planning, the motion is controlled via a feedback linearization controller. The motion of the manipulator is simulated and the effectiveness of the proposed actuation strategy and the designed controller is investigated.

scholarly journals Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1890 ◽  
Author(s):  
Zijian Hu ◽  
Kaifang Wan ◽  
Xiaoguang Gao ◽  
Yiwei Zhai ◽  
Qianglong Wang
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Predictive Control ◽  
Experimental Testing ◽  
The Novel ◽  
Simulation Environment ◽  
Unknown Environments ◽  
Autonomous Motion ◽  
Policy Gradient ◽  
Speed Up

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.

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>

Optimal Base Placement and Motion Planning for Mobile Manipulators

2012 ◽  
Author(s):  
Bin Du ◽  
Jing Zhao ◽  
Chunyu Song
Keyword(s):  
Motion Planning ◽  
Great Influence ◽  
Initial Point ◽  
Mobile Manipulator ◽  
Target Point ◽  
Mobile Manipulators ◽  
Redundant Manipulator ◽  
Self Motion ◽  
The Given ◽  
Manipulation Capability

A mobile manipulator typically consists of a mobile platform and a robotic manipulator mounted on the platform. The base placement of the platform has a great influence on whether the manipulator can perform a given task. In view of the issue, a new approach to optimize the base placement for a specified task is proposed in this paper. Firstly, the workspace of a redundant manipulator is investigated. The manipulation capability of the redundant manipulator is maximized based on the manipulability index through the joint self-motion of the redundant manipulator. Then the maximum manipulation capability in the specified work point is determined. Next, the relative manipulability index (RMI) is defined for analyzing manipulation capability of the manipulator in its workspace, and the global manipulability map (GMM) is presented based on the above measure. Moreover, the optimal base placement related to the given task is obtained, and the motion planning is implemented by an improved rapidly-exploring random tree (RRT) algorithm with the RMI, which can enhance the manipulation capability from the initial point to the target point. Finally, the feasibility of the proposed algorithm is illustrated with numerical simulations and experiments on the mobile manipulator.

Parallel computation of configuration space

Robotica ◽  
1996 ◽  
Vol 14 (2) ◽  
pp. 205-212 ◽  
Author(s):  
J. Solano González ◽  
D.I. Jonest
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Parallel Computer ◽  
Computer Language ◽  
Parallel Tasks ◽  
Planning Methods ◽  
Network Topologies ◽  
Speed Up ◽  
Computationally Intensive ◽  
Proper Account

SUMMARYMany motion planning methods use Configuration Space to represent a robot manipulator's range of motion and the obstacles which exist in its environment. The Cartesian to Configuration Space mapping is computationally intensive and this paper describes how the execution time can be decreased by using parallel processing. The natural tree structure of the algorithm is exploited to partition the computation into parallel tasks. An implementation programmed in the occam2 parallel computer language running on a network of INMOS transputers is described. The benefits of dynamically scheduling the tasks onto the processors are explained and verified by means of measured execution times on various processor network topologies. It is concluded that excellent speed-up and efficiency can be achieved provided that proper account is taken of the variable task lengths in the computation.

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