Online Motion Planning for Two Space Rigid Bodies with Rolling Constraints

doi:10.16183/j.cnki.jsjtu.2019.261

Abstract

Abstract:

The rolling restraint system between two space rigid bodies is a typical non-holonomic system. The incomplete characteristics can be used to simplify the mechanical structure and improve the reliability of the system. Aimed at the problems that the state variables of the pure rolling constraint non-holonomic system are difficult to control, the existing control methods are limited to specific models, and there is a lack of online control research, a solution method suitable for the online motion planning of the general rolling constraint system is established based on the rolling constraint first-order motion model. First, the offline motion planning is achieved by using the collocation method to obtain the reference trajectory. Then, the sequential action control (SAC) algorithm is used in real-time control combined with the rolling optimization framework to realize the online motion planning of the rolling system. The algorithm is applied to the real-time motion planning of the ball-plane rolling model and the rolling model between two spheres. The simulation results show that the method has a practical application value in broadening the control of the spherical robot and the operation of the dexterous manipulator.

Key words: rolling constraints, non-holonomic system, motion planning, direct collocation method, sequential action control (SAC)

CLC Number:

TP242

REN Shufeng, YANG Dan, YU Haidong, Wang Hao. Online Motion Planning for Two Space Rigid Bodies with Rolling Constraints[J]. Journal of Shanghai Jiao Tong University, 2021, 55(8): 1009-1017.

Figures/Tables 11

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Tab.1

Parameters for offline planning of rolling contact model

参数名	参数值
配点数	41
控制变量约束	$ω x$ ≤30, $ω y$ ≤30
Q	diag([100 100 10 10 1])
R	diag([0.1 0.1])
P₁	diag([20 20 10 10 1])

Tab.1

Tab.2

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参数名	参数值
传感器采样频率	f_s=50 Hz,t_s=0.02 s
预测区间/控制时长初值	T_p=0.5 s,Δt_init=0.1
目标函数下降上限系数	γ=-5
线搜索参数	ω=0.55,k_max=6
Q	diag([1000 1000 50 50 1])
R	diag([0.1 0.1])