此数据集包含故障检测后机器人的力和扭矩测量。每个故障的特点是从故障检测后立即开始定期收集 15 个力/扭矩样本。
数据集robot-lp文件夹,文件夹里有五个文本文件:
lp1.txt: 方法把握位置失败 (27K)
lp2.txt: 部分传输失败(15K)
lp3.txt: 转移失败后的部分位置 (15K)
lp4.txt: 解草位置方法失败(34K)
lp5.txt: 部分运动失败(49K)
所有特征都是数值的,尽管它们仅具有整数值。每个特征表示故障检测后测量的力或扭矩:每个故障实例的特点是,从故障检测后立即开始,定期收集 15 个力/扭矩样本:每个故障实例的总观察窗口为 315 毫秒。
每个示例描述如下:
class
Fx1 Fy1 Fz1 Tx1 Ty1 Tz1
Fx2 Fy2 Fz2 Tx2 Ty2 Tz2
......
Fx15 Fy15 Fz15 Tx15 Ty15 Tz15
其中Fx1.Fx15是观察窗口中力Fx的演化,Fy、Fz和扭矩也是如此:共有90个功能。
每个数据集中的实例数
-- LP1: 88
-- LP2: 47
-- LP3: 47
-- LP4: 117
-- LP5: 164
类分布
-- LP1: 24% normal
19% collision
18% front collision
39% obstruction
-- LP2: 43% normal
13% front collision
15% back collision
11% collision to the right
19% collision to the left
-- LP3: 43% ok
19% slightly moved
32% moved
6% lost
-- LP4: 21% normal
62% collision
18% obstruction
-- LP5: 27% normal
16% bottom collision
13% bottom obstruction
29% collision in part
16% collision in tool
可用于根据数据进行故障检测。
http://kdd.ics.uci.edu/databases/robotfailure/robotfailure.html
Seabra Lopes, L. (1997) “Robot Learning at the Task Level: a Study in the Assembly Domain”, Ph.D. thesis, Universidade Nova de Lisboa, Portugal.
Seabra Lopes, L. and L.M. Camarinha-Matos (1998) Feature Transformation Strategies for a Robot Learning Problem, “Feature Extraction, Construction and Selection. A Data Mining Perspective”, H. Liu and H. Motoda (edrs.), Kluwer Academic Publishers.
Camarinha-Matos, L.M., L. Seabra Lopes, and J. Barata (1996) Integration and Learning in Supervision of Flexible Assembly Systems, “IEEE Transactions on Robotics and Automation”, 12 (2), 202-219.