Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots

Author(s):  
Khurshid Aliev ◽  
Dario Antonelli
Author(s):  
Rui Liu ◽  
Jeremy Webb ◽  
Xiaoli Zhang

To effectively cooperate with a human, advanced manufacturing machines are expected to execute the industrial tasks according to human natural language (NL) instructions. However, NL instructions are not explicit enough to be understood and are not complete enough to be executed, leading to incorrected executions or even execution failure. To address these problems for better execution performance, we developed a Natural-Language-Instructed Task Execution (NL-Exe) method. In NL-Exe, semantic analysis is adopted to extract task-related knowledge, based on what human NL instructions are accurately understood. In addition, logic modeling is conducted to search the missing execution-related specifications, with which incomplete human instructions are repaired. By orally instructing a humanoid robot Baxter to perform industrial tasks “drill a hole” and “clean a spot”, we proved that NL-Exe could enable an advanced manufacturing machine to accurately understand human instructions, improving machine’s performance in industrial task execution.


2021 ◽  
Vol 101 (3) ◽  
Author(s):  
Korbinian Nottensteiner ◽  
Arne Sachtler ◽  
Alin Albu-Schäffer

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.


Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He

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.


2021 ◽  
Vol 121 (5) ◽  
pp. 1379-1388
Author(s):  
A. Mouthon ◽  
J. Ruffieux ◽  
W. Taube

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.


Author(s):  
Shashi Raj Pandey ◽  
Kitae Kim ◽  
Madyan Alsenwi ◽  
Yan Kyaw Tun ◽  
Choong Seon Hong
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