【免费】MITCheetah3四足机器人开源控制算法论文.pdf_mit机器狗

需积分: 0 16 浏览量更新于2023-04-28 评论 1 收藏 4.77MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

资源详情

资源评论

资源推荐

Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control

Jared Di Carlo

, Patrick M. Wensing

, Benjamin Katz

, Gerardo Bledt

1,3

, and Sangbae Kim

Abstract— This paper presents an implementation of model

predictive control (MPC) to determine ground reaction forces

for a torque-controlled quadruped robot. The robot dynamics

are simpliﬁed to formulate the problem as convex optimization

while still capturing the full 3D nature of the system. With

the simpliﬁed model, ground reaction force planning problems

are formulated for prediction horizons of up to 0.5 seconds,

and are solved to optimality in under 1 ms at a rate of 20-30

Hz. Despite using a simpliﬁed model, the robot is capable of

robust locomotion at a variety of speeds. Experimental results

demonstrate control of gaits including stand, trot, ﬂying-trot,

pronk, bound, pace, a 3-legged gait, and a full 3D gallop. The

robot achieved forward speeds of up to 3 m/s, lateral speeds up

to 1 m/s, and angular speeds up to 180 deg/sec. Our approach

is general enough to perform all these behaviors with the same

set of gains and weights.

I. INTRODUCTION

Control of highly dynamic legged robots is a challenging

problem due to the underactuation of the body during many

gaits and due to constraints placed on ground reaction forces.

As an example, during dynamic gaits

such as bounding or

galloping, the body of the robot is always underactuated.

Additionally, ground reaction forces must always remain in

a friction cone to avoid slipping. Current solutions for highly

dynamic locomotion include heuristic controllers for hopping

and bounding [1], which are effective, but difﬁcult to tune;

two-dimensional planar simpliﬁcations [2], which are only

applicable for gaits without lateral or roll dynamics; and

evolutionary optimization for galloping [3], which cannot

currently be solved fast enough for online use. Recent

results on hardware include execution of bounding limit

cycles discovered ofﬂine with HyQ [4] and learned pronking,

trotting, and bounding gaits on StarlETH [5].

Predictive control can stabilize these dynamic gaits by

anticipating periods of ﬂight or underactuation, but is dif-

ﬁcult to solve due to the nonlinear dynamics of legged

robots and the large number of states and control inputs.

Nonlinear optimization has been shown to be effective for

predictive control of hopping robots [6], humanoids [7], [8],

and quadrupeds [9], with [9] demonstrating the utility of

heuristics to regularize the optimization. Another common

approach is to use both a high-level planner, such as in [10],

[11] and a lower level controller to track the plan. More

Authors are with the

Department of Electrical Engineering and

Computer Science at the Massachusetts Institute of Technology,

Cambridge, MA, 02139, USA; the

Department of Aerospace and

Mechanical Engineering at the University of Notre Dame, Notre

Dame, IN, 46556; and the

Department of Mechanical Engineer-

ing at the Massachusetts Institute of Technology, Cambridge, MA,

02139, USA. email: dicarloj@mit.edu, pwensing@nd.edu,

benkatz@mit.edu, gbledt@mit.edu, sangbae@mit.edu

In this paper, the term dynamic gaits is used to refer to gaits with

signiﬁcant periods of ﬂight or underactuation.

Fig. 1. The MIT Cheetah 3 Robot galloping at 2.5 m/s

recently, the experimental results in [12] show that whole-

body nonlinear MPC can be used to stabilize trotting and

jumping.

The stabilization of the quadruped robot HyQ using con-

vex optimization discussed in [13] demonstrates the utility

of convex optimization, but the approach cannot be imme-

diately extended to dynamic gaits due to the quasi-static

simpliﬁcations made to the robot model. Similarly, in bipedal

locomotion, convex optimization has been used to ﬁnd the

best forces to satisfy instantaneous dynamics requirements

[14] and to plan footsteps with the linear inverted pendulum

model [15] but the latter approach does not include orienta-

tion in the predictive model.

While galloping is well studied in the ﬁeld of biology

[16], [17], surprisingly few hardware implementations of

galloping exist. The ﬁrst robot to demonstrate galloping

was the underactuated quadruped robot Scout II [18], which

reached 1.3 m/s, but had limited control of yaw. The MIT

Cheetah 1 robot [19] achieved high-speed galloping, but

was constrained to a plane. To the best of our knowledge,

the only previous implementation of a fully 3D gallop with

yaw control is on the hydraulically actuated WildCat robot

[20], developed by Boston Dynamics. Unfortunately, no

speciﬁc details about WildCat or its control system have been

published.

The main contribution of this paper is a predictive con-

troller which stabilizes a large number of gaits, including

those with complex orientation dynamics. On hardware,

we achieved a maximum yaw rate of 180 deg/sec and a

maximum linear velocity of 3.0 m/s during a fully 3D gallop,

which we believe to be the fastest gallop of an electrically

actuated robot, and the fastest angular velocity of any legged

robot similar in scale to Cheetah 3. Our controller can be

formulated as a single convex optimization problem which

considers a 3D, 12 DoF model of the robot. The solution of

This work was supported by the National Science Foundation [NSF-IIS-

1350879] and the Air Force Ofﬁce of Scientiﬁc Research [FA2386-17-1-

4661]

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Madrid, Spain, October 1-5, 2018

本内容试读结束，登录后可阅读更多

下载后可阅读完整内容，剩余7页未读，立即下载

rzss8

粉丝: 0
资源: 7

会员权益专享

MIT Cheetah 3四足机器人开源控制算法论文.pdf

评论0

会员权益专享

最新资源

MIT Cheetah 3四足机器人开源控制算法论文.pdf

评论0

ST电机库开源版本，5.2.0

简单状态机控制步进电机

cheetan-开源

mit cheetah 四足电机控制命令can 速度控制

mit cheetah源码解析

mit cheetah 代码

mit cheetah spine 原理图

mit cheetah 电机图纸

dynamic locomotion in the mit cheetah 3 through convex model-predictive cont

MIT Cheetah mini

cheetah开源电机驱动控制解析

mini cheetah mpc

Cheetah mini的仿真控制

mit-cheetah-note

mit mini cheetah机器狗电机can驱动方法

minicheetah硬件

有没有最近几年的平衡小车国内外研究现状

cheetah3硬件

猎豹优化算法（CO）和菲克定律优化算法（FLA）的共同点

cheetah spi 写入指令

会员权益专享

最新资源