Manipulability servoing in null space for bilateral motion control of redundant manipulators

2011 ◽  
Author(s):  
Tomoyuki Shimono ◽  
Nobuyuki Togashi ◽  
Naoki Motoi ◽  
Atsuo Kawamura
Keyword(s):  
Motion Control ◽  
Null Space ◽  
Redundant Manipulators

scholarly journals Motion Planning and Control of Redundant Manipulators for Dynamical Obstacle Avoidance

2021 ◽  
Author(s):  
Giacomo Palmieri ◽  
Cecilia Scoccia ◽  
Matteo-Claudio Palpacelli ◽  
Massimo Callegari
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Motion Control ◽  
Null Space ◽  
Computational Effort ◽  
Control Law ◽  
Redundant Manipulators ◽  
Planning And Control ◽  
On Line ◽  
And Control

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles, detected before the robot starts to move; it is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; it is realized by means of a velocity control law based on the null space method for redundancy control. A term of the control law takes into account the speed of the obstacles as well as their position. In order to test the algorithms, a set of simulations are presented: the robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system.

scholarly journals Motion Planning and Control of Redundant Manipulators for Dynamical Obstacle Avoidance

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 121
Author(s):  
Giacomo Palmieri ◽  
Cecilia Scoccia
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Motion Control ◽  
Null Space ◽  
Maximum Speed ◽  
Control Law ◽  
Redundant Manipulators ◽  
Planning And Control ◽  
On Line ◽  
And Control

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: the redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of 90%, with an increase of less than 15% of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately 50% and 80%, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system.

Gradient Optimization of Inverse Dynamics for Robotic Manipulator Motion Planning Using Combined Optimal Control

2017 ◽  
Author(s):  
Shangdong Gong ◽  
Redwan Alqasemi ◽  
Rajiv Dubey
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Inverse Dynamics ◽  
Null Space ◽  
Optimal Solution ◽  
Human Motion ◽  
Optimization Techniques ◽  
Redundant Manipulators ◽  
Robot Arm ◽  
Gradient Optimization

Motion planning of redundant manipulators is an active and widely studied area of research. The inverse kinematics problem can be solved using various optimization methods within the null space to avoid joint limits, obstacle constraints, as well as minimize the velocity or maximize the manipulability measure. However, the relation between the torques of the joints and their respective positions can complicate inverse dynamics of redundant systems. It also makes it challenging to optimize cost functions, such as total torque or kinematic energy. In addition, the functional gradient optimization techniques do not achieve an optimal solution for the goal configuration. We present a study on motion planning using optimal control as a pre-process to find optimal pose at the goal position based on the external forces and gravity compensation, and generate a trajectory with optimized torques using the gradient information of the torque function. As a result, we reach an optimal trajectory that can minimize the torque and takes dynamics into consideration. We demonstrate the motion planning for a planar 3-DOF redundant robotic arm and show the results of the optimized trajectory motion. In the simulation, the torque generated by an external force on the end-effector as well as by the motion of every link is made into an integral over the squared torque norm. This technique is expected to take the torque of every joint into consideration and generate better motion that maintains the torques or kinematic energy of the arm in the safe zone. In future work, the trajectories of the redundant manipulators will be optimized to generate more natural motion as in humanoid arm motion. Similar to the human motion strategy, the robot arm is expected to be able to lift weights held by hands, the configuration of the arm is changed along from the initial configuration to a goal configuration. Furthermore, along with weighted least norm (WLN) solutions, the optimization framework will be more adaptive to the dynamic environment. In this paper, we present the development of our methodology, a simulated test and discussion of the results.

scholarly journals A Synthetic Algorithm for Tracking a Moving Object in a Multiple-Dynamic Obstacles Environment Based on Kinematically Planar Redundant Manipulators

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Hongzhe Jin ◽  
Hui Zhang ◽  
Zhangxing Liu ◽  
Decai Yang ◽  
Dongyang Bie ◽  
...  
Keyword(s):  
Real Time ◽  
Null Space ◽  
Inverse Kinematic ◽  
Moving Object ◽  
Redundant Manipulators ◽  
Joint Angles ◽  
The Real ◽  
Time Path ◽  
Planar Manipulators ◽  
Spline Filter

This paper presents a synthetic algorithm for tracking a moving object in a multiple-dynamic obstacles environment based on kinematically planar manipulators. By observing the motions of the object and obstacles, Spline filter associated with polynomial fitting is utilized to predict their moving paths for a period of time in the future. Several feasible paths for the manipulator in Cartesian space can be planned according to the predicted moving paths and the defined feasibility criterion. The shortest one among these feasible paths is selected as the optimized path. Then the real-time path along the optimized path is planned for the manipulator to track the moving object in real-time. To improve the convergence rate of tracking, a virtual controller based on PD controller is designed to adaptively adjust the real-time path. In the process of tracking, the null space of inverse kinematic and the local rotation coordinate method (LRCM) are utilized for the arms and the end-effector to avoid obstacles, respectively. Finally, the moving object in a multiple-dynamic obstacles environment is thus tracked via real-time updating the joint angles of manipulator according to the iterative method. Simulation results show that the proposed algorithm is feasible to track a moving object in a multiple-dynamic obstacles environment.

Hierarchical tracking task control in redundant manipulators with compliance control in the null-space

Mechatronics ◽  
2018 ◽  
Vol 55 ◽  
pp. 171-179 ◽  
Author(s):  
Abbas Karami ◽  
Hamid Sadeghian ◽  
Mehid Keshmiri ◽  
Giuseppe Oriolo
Keyword(s):  
Null Space ◽  
Tracking Task ◽  
Redundant Manipulators ◽  
Compliance Control ◽  
Task Control

scholarly journals Cognitive social zones for improving the pedestrian collision avoidance with mobile robots

2019 ◽  
Vol 42 (2) ◽  
pp. 07-14 ◽  
Author(s):  
Daniel Herrera ◽  
Javier Gimenez ◽  
Matias Monllor ◽  
Flavio Roberti ◽  
Ricardo Carelli
Keyword(s):  
Motion Control ◽  
Social Space ◽  
Learning Algorithm ◽  
Null Space ◽  
Social Cues ◽  
Potential Fields ◽  
Learning Stage ◽  
The Social ◽  
Holonomic Mobile Robot ◽  
Experimental Inference

Social behaviors are crucial to improve the acceptance of a robot in human-shared environments. One of themost important social cues is undoubtedly the social space. This human mechanism acts like a repulsive field to guaranteecomfortable interactions. Its modeling has been widely studied in social robotics, but its experimental inference has beenweakly mentioned. Thereby, this paper proposes a novel algorithm to infer the dimensions of an elliptical social zone froma points-cloud around the robot. The approach consists of identifying how the humans avoid a robot during navigationin shared scenarios, and later use this experience to represent humans obstacles like elliptical potential fields with thepreviously identified dimensions. Thus, the algorithm starts with a first-learning stage where the robot navigates withoutavoiding humans, i.e. the humans are in charge of avoiding the robots while developing their tasks. During this period,the robot generates a points-cloud with 2D laser measures from its own framework to define the human-presence zonesaround itself but prioritizing its closest surroundings. Later, the inferred social zone is incorporated to a null-space-based(NSB) control for a non-holonomic mobile robot, which consists of both trajectory tracking and pedestrian collisionavoidance. Finally, the performance of the learning algorithm and the motion control is verified through experimentation.

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