Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement.
Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement.
复制标题
DOI：
10.3389/frobt.2021.637301
复制
发表时间：
2021
影响因子：
3.4
通讯作者：
Killpack MD
中科院分区：
文献类型：
--
作者：
Rupert L;Duggan T;Killpack MD
本文提出了将长度传感器的软连续体机器人关节以及一种新的配置估计方法，大大减少配置估计误差的方法。用于放置传感器沿着的长度的关节的方法包括一个单一的关节长度传感器，传感器线端到端，传感器重叠，根据启发式，和传感器放置的优化，我们在本文中描述。配置估计的方法包括将传感器长度直接与关节角度的一段相关，使用覆盖关节段的重叠传感器的相等权重，以及使用连续体关节上的所有传感器的加权线性组合。使用稳健线性回归确定线性组合方法的权重。使用运动学仿真，我们表明，放置三个或更多的重叠传感器和估计配置与传感器的线性组合导致的中位误差的0.026%的最大运动范围或更小。这是一个超过500倍的改进相比，使用一个单一的传感器来估计关节配置。这个误差是在80个不同长度和运动范围的模拟机器人上计算的。我们还发现，完全优化的传感器布局执行仅略优于根据启发式传感器的位置。这表明使用传感器的线性组合（使用线性回归找到权重）比重叠传感器的放置更重要。此外，使用启发式显着简化了这些技术的应用时，设计硬件。
This paper presents methods for placing length sensors on a soft continuum robot joint as well as a novel configuration estimation method that drastically minimizes configuration estimation error. The methods utilized for placing sensors along the length of the joint include a single joint length sensor, sensors lined end-to-end, sensors that overlap according to a heuristic, and sensors that are placed by an optimization that we describe in this paper. The methods of configuration estimation include directly relating sensor length to a segment of the joint's angle, using an equal weighting of overlapping sensors that cover a joint segment, and using a weighted linear combination of all sensors on the continuum joint. The weights for the linear combination method are determined using robust linear regression. Using a kinematic simulation we show that placing three or more overlapping sensors and estimating the configuration with a linear combination of sensors resulted in a median error of 0.026% of the max range of motion or less. This is over a 500 times improvement as compared to using a single sensor to estimate the joint configuration. This error was computed across 80 simulated robots of different lengths and ranges of motion. We also found that the fully optimized sensor placement performed only marginally better than the placement of sensors according to the heuristic. This suggests that the use of a linear combination of sensors, with weights found using linear regression is more important than the placement of the overlapping sensors. Further, using the heuristic significantly simplifies the application of these techniques when designing for hardware.
登录
查看更多内容
影响因子：
4.3
作者：
Clark, Emily;Brunton, Steven L.;Kutz, J. Nathan
通讯作者：
Kutz, J. Nathan
影响因子：
3.5
作者：
Felt, Wyatt;Telleria, Maria J.;Remy, C. David
通讯作者：
Remy, C. David
影响因子：
6.4
作者：
Hyatt, Phillip;Kraus, Dustan;Killpack, Marc D.
通讯作者：
Killpack, Marc D.
影响因子：
6.4
作者：
Gerboni, Giada;Diodato, Alessandro;Menciassi, Arianna
通讯作者：
Menciassi, Arianna
影响因子：
5.2
作者：
Anderson PL;Mahoney AW;Webster RJ 3rd
通讯作者：
Webster RJ 3rd