With Proximity Servoing towards Safe Human-Robot-Interaction

Ding, Yitao; Wilhelm, Felix; Faulhammer, Leonhard; Thomas, Ulrike

doi:10.1109/iros40897.2019.8968438

Cited by 21 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In both cases, there is a small dead spot near the sensing element. Typical O-ToFtechnology used, e. g. [20], [21], can work up to a distance of 4 m, depending on the component used, but this extended range has the cost of having a relatively large dead spot of 10 cm in front of the sensing element. Similarly, the O-Tri-Type sensor used in [84], [85] has a detection range of 1.5 m and a dead spot of 20 cm.…”

Section: ) Discussion On Optical Sensorsmentioning

confidence: 99%

“…Collision avoidance on a mobile robot based on capacitive proximity sensing for a variety of materials was shown in [17]. Covering robot links with modular single-ended capacitive proximity (C-SE) skins for collision avoidance and HRI is proposed in [18], [19], [20], [21]. These works show the potential for these technologies, especially for HRI.…”

Section: A Capacitive Sensingmentioning

confidence: 99%

“…However, for example in [19], it is discussed that C-SE is not suitable enough to detect insulating materials for the intended application of collision avoidance. As a solution, in [20], [21], a combination with time-of-flight (O-ToF) sensing (see Sec. III-B4) is proposed to compensate for the shortcomings of C-SE sensors.…”

Section: A Capacitive Sensingmentioning

confidence: 99%

“…9). In [43], [20], Ding et al show their developments of a multi-modal proximity sensor, the proximity sensing cuffs, featuring capacitive and O-ToF technology for material recognition and collision avoidance.…”

Section: ) Reflected Light Intensity-type Sensors (O-rli) Usingmentioning

confidence: 99%

See 3 more Smart Citations

Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications

et al. 2022

View full text Add to dashboard Cite

Section: ) Discussion On Optical Sensorsmentioning

confidence: 99%

Section: A Capacitive Sensingmentioning

confidence: 99%

Section: A Capacitive Sensingmentioning

confidence: 99%

Section: ) Reflected Light Intensity-type Sensors (O-rli) Usingmentioning

confidence: 99%

See 2 more Smart Citations

Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications

et al. 2022

View full text Add to dashboard Cite

“…In [28] it is used for manipulability optimization control to enable Robot-Assisted Minimally Invasive Surgery. Other applications include the optimization of redundant manipulators using dynamic neural networks [8], solving the inverse kinematics of redundant robots constructed according to the DLR manipulator scheme [4], or realizing proximity servoing for manipulators with distance sensors [3]. Another field of application is planning of optimal movements for mobile platforms equipped with a manipulator, as shown in [21] and [1].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Mobility of Robotic Arms in Collision-free Motion Planning

Kaden

Thomas

2021

J Intell Robot Syst

Self Cite

View full text Add to dashboard Cite

A major task in motion planning is to find paths that have a high ability to react to external influences while ensuring a collision-free operation at any time. This flexibility is even more important in human-robot collaboration since unforeseen events can occur anytime. Such ability can be described as mobility, which is composed of two characteristics. First, the ability to manipulate, and second, the distance to joint limits. This mobility needs to be optimized while generating collision-free motions so that there is always the flexibility of the robot to evade dynamic obstacles in the future execution of generated paths. For this purpose, we present a Rapidly-exploring Random Tree (RRT), which applies additional costs and sampling methods to increase mobility. Additionally, we present two methods for the optimization of a generated path. Our first approach utilizes the built-in capabilities of the RRT*. The second method optimize the path with the stochastic trajectory optimization for motion planning (STOMP) approach with Gaussian Mixture Models. Moreover, we evaluate the algorithms in complex simulation and real environments and demonstrate an enhancement of mobility.

show abstract