Task Execution Based-on Human-Robot Communication and Pointing Gestures

2017 ◽  
pp. 37-46 ◽  
Author(s):  
Peiqing Yan ◽  
Bingwei He ◽  
Liwei Zhang ◽  
Jianwei Zhang
Keyword(s):  
Task Execution ◽  
Pointing Gestures ◽  
Robot Communication

SCREW KINEMATIC MODEL FOR ROBOT CALIBRATION WITH PC-ROBOT COMMUNICATION OVERVIEW

2017 ◽  
Author(s):  
Lucas Kato ◽  
Tiago Pinto ◽  
Henrique Simas ◽  
Daniel Martins
Keyword(s):  
Kinematic Model ◽  
Robot Calibration ◽  
Robot Communication

scholarly journals Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

2021 ◽  
Vol 101 (3) ◽  
Author(s):  
Korbinian Nottensteiner ◽  
Arne Sachtler ◽  
Alin Albu-Schäffer
Keyword(s):  
Autonomous Robots ◽  
Vision System ◽  
Dynamic Environments ◽  
Bayesian Framework ◽  
Experimental Results ◽  
Robotic Assembly ◽  
Tactile Sensing ◽  
Task Execution ◽  
Industrial Assembly ◽  
Assembly Tasks

AbstractRobotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

scholarly journals Modulation of intracortical inhibition during physically performed and mentally simulated balance tasks

2021 ◽  
Vol 121 (5) ◽  
pp. 1379-1388
Author(s):  
A. Mouthon ◽  
J. Ruffieux ◽  
W. Taube
Keyword(s):  
Motor Evoked Potentials ◽  
Action Observation ◽  
Short Interval ◽  
Intracortical Inhibition ◽  
Mental Simulation ◽  
Task Execution ◽  
Postural Tasks ◽  
Execution Condition ◽  
The One ◽  
Postural Task

Abstract Purpose Action observation (AO) during motor imagery (MI), so-called AO + MI, has been proposed as a new form of non-physical training, but the neural mechanisms involved remains largely unknown. Therefore, this study aimed to explore whether there were similarities in the modulation of short-interval intracortical inhibition (SICI) during execution and mental simulation of postural tasks, and if there was a difference in modulation of SICI between AO + MI and AO alone. Method 21 young adults (mean ± SD = 24 ± 6.3 years) were asked to either passively observe (AO) or imagine while observing (AO + MI) or physically perform a stable and an unstable standing task, while motor evoked potentials and SICI were assessed in the soleus muscle. Result SICI results showed a modulation by condition (F2,40 = 6.42, p = 0.009) with less SICI in the execution condition compared to the AO + MI (p = 0.009) and AO (p = 0.002) condition. Moreover, switching from the stable to the unstable stance condition reduced significantly SICI (F1,20 = 8.34, p = 0.009) during both, physically performed (− 38.5%; p = 0.03) and mentally simulated balance (− 10%, p < 0.001, AO + MI and AO taken together). Conclusion The data demonstrate that SICI is reduced when switching from a stable to a more unstable standing task during both real task execution and mental simulation. Therefore, our results strengthen and further support the existence of similarities between executed and mentally simulated actions by showing that not only corticospinal excitability is similarly modulated but also SICI. This proposes that the activity of the inhibitory cortical network during mental simulation of balance tasks resembles the one during physical postural task execution.

A Crowd-enabled Task Execution Approach in UAV Networks Towards Fog Computing

2021 ◽  
Author(s):  
Shashi Raj Pandey ◽  
Kitae Kim ◽  
Madyan Alsenwi ◽  
Yan Kyaw Tun ◽  
Choong Seon Hong
Keyword(s):  
Fog Computing ◽  
Task Execution
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