Optimal Trajectory Generation for Energy Consumption Minimization and Moving Obstacle Avoidance of SURENA III Robot’s Arm

In this paper, trajectory generation for the 4 DOF arm of SURENA III humanoid robot with the purpose of optimizing energy and avoiding a moving obstacle is presented. For this purpose, first, kinematic equations for a seven DOF manipulator are derived. Then, using the Lagrange method, an explicit dynamics model for the arm is developed. In the next step, in order to generate the desired trajectory for the arm, two different methods are utilized. In the first method, each joint motion is presented by a quadratic polynomial. In the second one, the end effector’s path has been considered as 3 polynomial functions. Also, a known moving spherical obstacle with a linear path and constant velocity is considered in robot workspace. The main goal of optimization is to reduce the consumed energy by the arm in a movement between two known points in a specified time frame to avoid the moving obstacle. Initial and final velocities of the arm are set as zero. To this end, the optimization is carried out using Genetic Algorithm. Finally, in order to obtain the most reliable solutions for trajectory generation, many optimizations with various parameters are conducted and the results are presented and discussed.

[1]  Valder Steffen,et al.  OPTIMIZATION OF THE TRAJECTORY PLANNING OF ROBOT MANIPULATORS TAKING INTO ACCOUNT THE DYNAMICS OF THE SYSTEM , 1998 .

[2]  Serdar Kucuk,et al.  Energy minimization for 3-RRR fully planar parallel manipulator using particle swarm optimization , 2013 .

[3]  M. Kawato,et al.  Trajectory formation of arm movement by cascade neural network model based on minimum torque-change criterion , 1990, Biological Cybernetics.

[4]  Atsuo Kawamura,et al.  Trajectory planning of redundant manipulators for minimum energy consumption without matrix inversion , 1997, Proceedings of International Conference on Robotics and Automation.

[5]  Hui Dong,et al.  Obstacle Avoidance Path Planning of Planar Redundant Manipulators Using Workspace Density , 2015 .

[6]  S. F. P. Saramago,et al.  OPTIMAL TRAJECTORY PLANNING OF ROBOT MANIPULATORS IN THE PRESENCE OF MOVING OBSTACLES , 2000 .

[7]  Ian D. Walker,et al.  Minimum effort inverse kinematics for redundant manipulators , 1997, IEEE Trans. Robotics Autom..

[8]  Sezimaria F. P. Saramago,et al.  Dynamic optimization for the trajectory planning of robot manipulators in the presence of obstacles , 1999 .

[9]  Hao Shen,et al.  Dynamic optimization of a robot manipulator based on GA , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[10]  Han Ding,et al.  Dynamic optimization of redundant manipulators in worst case using recurrent neural networks , 2000 .

[11]  Shugen Ma,et al.  A new formulation technique for local torque optimization of redundant manipulators , 1996, IEEE Trans. Ind. Electron..

[12]  Tien C. Hsia,et al.  Joint trajectory generation for redundant robots in an environment with obstacles , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[13]  Yury Stepanenko,et al.  Iterative dynamic programming: an approach to minimum energy trajectory planning for robotic manipulators , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[14]  Shuzhi Sam Ge,et al.  A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Zheng H. Zhu,et al.  Dynamic robot manipulator trajectory planning for obstacle avoidance , 1999 .

[16]  Naoyuki Kubota,et al.  Collision avoidance planning of a robot manipulator by using genetic algorithm. A consideration for the problem in which moving obstacles and/or several robots are included in the workspace , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[17]  Yunong Zhang,et al.  Obstacle avoidance for kinematically redundant manipulators using a dual neural network , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Yunong Zhang,et al.  Minimum-Energy Redundancy Resolution of Robot Manipulators Unified by Quadratic Programming and its Online Solution , 2007, 2007 International Conference on Mechatronics and Automation.