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>

Novel Trajectory Planning Method Based on Double Quaternion and Tau Theory

2021 ◽  
pp. 482-491
Author(s):  
Zhiqiang Wang ◽  
Jinzhu Peng ◽  
Mengchao Dong ◽  
Shouan Song ◽  
Shuai Ding ◽  
...  
Keyword(s):  
Trajectory Planning ◽  
Planning Method ◽  
Tau Theory

A double flora bacteria foraging optimization algorithm

2014 ◽  
Vol 31 (1) ◽  
pp. 43 ◽  
Author(s):  
Jianguo Jiang ◽  
Jiawei Zhou ◽  
Yingchun Zheng ◽  
Runsheng Zhou
Keyword(s):  
Optimization Algorithm ◽  
Bacteria Foraging Optimization Algorithm

scholarly journals Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan Fang ◽  
Lan Yang ◽  
Tianqi Wang ◽  
Shoucai Jing
Keyword(s):  
Fuel Consumption ◽  
Trajectory Planning ◽  
Signalized Intersections ◽  
Planning Method ◽  
Signalized Intersection ◽  
Driver Model ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Traffic Lights ◽  
Trigonometric Model

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

A trajectory planning method of vertebral plate grinding of spine surgery robot

2021 ◽  
Author(s):  
Heqiang Tian ◽  
Jingbo Pan ◽  
Yu Gao ◽  
Bin Tian ◽  
Debao Meng ◽  
...  
Keyword(s):  
Spine Surgery ◽  
Trajectory Planning ◽  
Planning Method
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