Hierarchical Task-Priority Control for Human-Robot Co-manipulation

2020 ◽  
pp. 125-138
Author(s):  
Jonathan Cacace ◽  
Fabio Ruggiero ◽  
Vincenzo Lippiello
Keyword(s):  
Task Priority

scholarly journals Stability of the Tracking Problem with Task-Priority Inverse Kinematics

2018 ◽  
Vol 51 (22) ◽  
pp. 121-125 ◽  
Author(s):  
Mathias Hauan Arbo ◽  
Jan Tommy Gravdahl
Keyword(s):  
Inverse Kinematics ◽  
Tracking Problem ◽  
Task Priority

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

Multi-task Planning with the Consideration of Task Priority

2020 ◽  
pp. 56-63
Author(s):  
Renkang Ke ◽  
Qibo Sun ◽  
Jinglin Li ◽  
Ao Zhou ◽  
Shangguang Wang
Keyword(s):  
Task Planning ◽  
Task Priority

Task Priority Control for the HPX Runtime System

2020 ◽  
Author(s):  
Suhang Jiang ◽  
Mulya Agung ◽  
Ryusuke Egawa ◽  
Hiroyuki Takizawa
Keyword(s):  
Runtime System ◽  
Task Priority

scholarly journals Adaptive Cost-Based Task Scheduling in Cloud Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Mohammed A. S. Mosleh ◽  
G. Radhamani ◽  
Mohamed A. G. Hazber ◽  
Syed Hamid Hasan
Keyword(s):  
Task Scheduling ◽  
Completion Time ◽  
Virtual Machines ◽  
Minimum Cost ◽  
Arrival Rate ◽  
Data Access ◽  
Access Time ◽  
Access Path ◽  
Task Priority ◽  
The Cost

Task execution in cloud computing requires obtaining stored data from remote data centers. Though this storage process reduces the memory constraints of the user’s computer, the time deadline is a serious concern. In this paper, Adaptive Cost-based Task Scheduling (ACTS) is proposed to provide data access to the virtual machines (VMs) within the deadline without increasing the cost. ACTS considers the data access completion time for selecting the cost effective path to access the data. To allocate data access paths, the data access completion time is computed by considering the mean and variance of the network service time and the arrival rate of network input/output requests. Then the task priority is assigned to the removed tasks based data access time. Finally, the cost of data paths are analyzed and allocated based on the task priority. Minimum cost path is allocated to the low priority tasks and fast access path are allocated to high priority tasks as to meet the time deadline. Thus efficient task scheduling can be achieved by using ACTS. The experimental results conducted in terms of execution time, computation cost, communication cost, bandwidth, and CPU utilization prove that the proposed algorithm provides better performance than the state-of-the-art methods.

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