High precision trajectory planning on freeform surfaces for robotic manipulators

2017 ◽  
Author(s):  
Renan S. Freitas ◽  
Eduardo E. M. Soares ◽  
Ramon R. Costa ◽  
Breno B. Carvalho
Keyword(s):  
High Precision ◽  
Trajectory Planning ◽  
Robotic Manipulators ◽  
Freeform Surfaces

scholarly journals A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory

2021 ◽  
Vol 19 (1) ◽  
pp. 643-662
Author(s):  
Zhiqiang Wang ◽  
◽  
Jinzhu Peng ◽  
Shuai Ding
Keyword(s):  
Trajectory Planning ◽  
Optimization Algorithm ◽  
Robotic Manipulators ◽  
Planning Method ◽  
Robotic Systems ◽  
Control Points ◽  
Interpolation Algorithm ◽  
Tau Theory ◽  
Small Set ◽  
Bacteria Foraging Optimization Algorithm

<abstract><p>In this paper, a novel bio-inspired trajectory planning method is proposed for robotic systems based on an improved bacteria foraging optimization algorithm (IBFOA) and an improved intrinsic Tau jerk (named Tau-J*) guidance strategy. Besides, the adaptive factor and elite-preservation strategy are employed to facilitate the IBFOA, and an improved Tau-J* with higher-order of intrinsic guidance movement is used to avoid the nonzero initial and final jerk, so as to overcome the computational burden and unsmooth trajectory problems existing in the optimization algorithm and traditional interpolation algorithm. The IBFOA is utilized to determine a small set of optimal control points, and Tau-J* is then invoked to generate smooth trajectories between the control points. Finally, the results of simulation tests demonstrate the eminent stability, optimality, and rapidity capability of the proposed bio-inspired trajectory planning method.</p></abstract>

Analysis of Optimal Balancing for Robotic Manipulators in Repetitive Motions

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Amin Nikoobin ◽  
Mojtaba Moradi
Keyword(s):  
Performance Improvement ◽  
Trajectory Planning ◽  
Cost Reduction ◽  
Robotic Manipulators ◽  
Point Of View ◽  
Dynamic Equations ◽  
Potential Fields ◽  
Cost Functions ◽  
Closed Contour ◽  
Static Balancing

Balancing plays a major role in performance improvement of robotic manipulators. From an optimization point of view, some balancing parameters can be modified to decrease motion cost. Recently introduced, this concept is called optimal balancing: an umbrella term for static balancing and other balancing methods. In this method, the best combination of balancing and trajectory planning is sought. In this note, repetitive full cycle motion of robot manipulators including different subtasks is considered. The basic idea arises from the fact that, upon changing dynamic equations of a robotic manipulator or cost functions in subtasks, the entire cycle of motion must be reconsidered in an optimal balancing problem. The possibility of cost reduction for a closed contour in potential fields is shown by some simulations done for a PUMA-like robot. Also, the obtained results show 34.8% cost reduction compared to that of static balancing.

Optimum time-energy-jerk trajectory planning for serial robotic manipulators by reparameterized quintic NURBS curves

2020 ◽  
pp. 095440622096973
Author(s):  
Guanglei Wu ◽  
Wenkang Zhao ◽  
Xuping Zhang
Keyword(s):  
Trajectory Planning ◽  
Execution Time ◽  
Optimization Problem ◽  
Robotic Manipulators ◽  
Nurbs Curves ◽  
Planning Approach ◽  
Key Points ◽  
Joint Dynamics ◽  
Distributed Execution ◽  
Robot Performance

This paper deals with the trajectory planning for serial robotic manipulators passing through key points by minimizing execution time, energy consumption and joint jerks. Quintic NURBS curves are adopted to fit the trajectory, of which the trajectory is reparameterized with respect to time for generation of geometric path and motion laws, aiming at continuity of the robot velocity, acceleration and jerk. A trajectory planning approach for optimum robot performance is proposed by solving a multi-objective optimization problem to attain optimal curve parameters and distributed execution time along curve segments simultaneously. The proposed technique of trajectory planning is numerically illustrated with a robotic arm and evaluated by experimental measurements. The comparison of total execution time and joint dynamics with/without variables optimization shows the effectiveness of the proposed approach.

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