scholarly journals A Novel Human-Like Control Framework for Mobile Medical Service Robot

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Zhang ◽  
Jiehao Li ◽  
Wen Qi ◽  
Xuanyi Zhou ◽  
Yingbai Hu ◽  
...  
Keyword(s):  
Medical Service ◽  
Gaussian Mixture ◽  
Service Robot ◽  
Kinect Sensor ◽  
Dynamic Movement ◽  
Medical Supplies ◽  
Control Framework ◽  
Learning Technique ◽  
Dynamic Movement Primitives ◽  
Novel Coronavirus

Recently, as a highly infectious disease of novel coronavirus (COVID-19) has swept the globe, more and more patients need to be isolated in the rooms of the hospitals, so how to deliver the meals or drugs to these infectious patients is the urgent work. It is a reliable and effective method to transport medical supplies or meals to patients using robots, but how to teach the robot to the destination and to enter the door like a human is an exciting task. In this paper, a novel human-like control framework for the mobile medical service robot is considered, where a Kinect sensor is used to manage human activity recognition to generate a designed teaching trajectory. Meanwhile, the learning technique of dynamic movement primitives (DMP) with the Gaussian mixture model (GMM) is applied to transfer the skill from humans to robots. A neural-based model predictive tracking controller is implemented to follow the teaching trajectory. Finally, some demonstrations are carried out in a hospital room to illustrate the superiority and effectiveness of the developed framework.

scholarly journals Learning, Generalization, and Obstacle Avoidance with Dynamic Movement Primitives and Dynamic Potential Fields

2019 ◽  
Vol 9 (8) ◽  
pp. 1535 ◽  
Author(s):  
Mingshan Chi ◽  
Yufeng Yao ◽  
Yaxin Liu ◽  
Ming Zhong
Keyword(s):  
Obstacle Avoidance ◽  
Home Environment ◽  
The Elderly ◽  
Service Robot ◽  
Motion Path ◽  
Dynamic Motion ◽  
Dynamic Movement ◽  
Dynamic Potential ◽  
Dynamic Movement Primitives ◽  
Learning Generalization

In order to offer simple and convenient assistance for the elderly and disabled to take care of themselves, we propose a general learning and generalization approach for a service robot to accomplish specified tasks autonomously in an unstructured home environment. This approach firstly learns the required tasks by learning from demonstration (LfD) and represents the learned tasks with dynamic motion primitives (DMPs), so as to easily generalize them to a new environment only with little modification. Furthermore, we integrate dynamic potential field (DPF) with the above DMPs model to realize the autonomous obstacle avoidance function of a service robot. This approach is validated on the wheelchair mounted robotic arm (WMRA) by performing serial experiments of placing a cup on the table with an obstacle or without obstacle on its motion path.

scholarly journals An Extended DMPs framework for Decoupled Quaternions Learning and generalization

2021 ◽  
Author(s):  
Zhiwei Liao ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Xuesong Mei
Keyword(s):  
Riemannian Manifolds ◽  
Gaussian Mixture ◽  
Joint Space ◽  
Robot Learning ◽  
Learning From Demonstration ◽  
End Effector ◽  
Mixture Regression ◽  
Cartesian Space ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

Abstract Dynamic Movement Primitives (DMPs) as a robust and efficient framework has been studied widely for robot learning from demonstration. Classical DMPs framework mainly focuses on the movement learning in Cartesian or joint space, and can't properly represent end-effector orientation. In this paper, we present an Extended DMPs framework (EDMPs) both in Cartesian space and Riemannian manifolds for Quaternion-based orientations learning and generalization. Gaussian Mixture Model and Gaussian Mixture Regression are adopted as the initialization phase of EDMPs to handle multi-demonstrations and obtain their mean and covariance. Additionally, some evaluation indicators including reachability and similarity are defined to characterize the learning and generalization abilities of EDMPs. Finally, the quaternion-based orientations are successfully transferred from human to the robot, and a real-world experiment is conducted to verify the effectiveness of the proposed method. The experimental results reveal that the presented approach can learn and generalize multi-space parameters under multi-demonstrations.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals The Generalization of Robot Skills Based on Dynamic Movement Primitives

2020 ◽  
Vol 53 (5) ◽  
pp. 265-270
Author(s):  
Xian Li ◽  
Chenguang Yang ◽  
Ying Feng
Keyword(s):  
Movement Primitives ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

Learning from demonstration using improved dynamic movement primitives

2021 ◽  
Author(s):  
Tiantian Wang ◽  
Liang Yan ◽  
Gang Wang ◽  
Xiaoshan Gao ◽  
Nannan Du ◽  
...  
Keyword(s):  
Learning From Demonstration ◽  
Movement Primitives ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

scholarly journals An Adaptive Imitation Learning Framework for Robotic Complex Contact-Rich Insertion Tasks

2022 ◽  
Vol 8 ◽  
Author(s):  
Yan Wang ◽  
Cristian C. Beltran-Hernandez ◽  
Weiwei Wan ◽  
Kensuke Harada
Keyword(s):  
Force Control ◽  
Robotic Manipulation ◽  
Imitation Learning ◽  
Specific Class ◽  
High Quality ◽  
Single Task ◽  
Learning Framework ◽  
Dynamic Movement ◽  
Real Hardware ◽  
Dynamic Movement Primitives

Complex contact-rich insertion is a ubiquitous robotic manipulation skill and usually involves nonlinear and low-clearance insertion trajectories as well as varying force requirements. A hybrid trajectory and force learning framework can be utilized to generate high-quality trajectories by imitation learning and find suitable force control policies efficiently by reinforcement learning. However, with the mentioned approach, many human demonstrations are necessary to learn several tasks even when those tasks require topologically similar trajectories. Therefore, to reduce human repetitive teaching efforts for new tasks, we present an adaptive imitation framework for robot manipulation. The main contribution of this work is the development of a framework that introduces dynamic movement primitives into a hybrid trajectory and force learning framework to learn a specific class of complex contact-rich insertion tasks based on the trajectory profile of a single task instance belonging to the task class. Through experimental evaluations, we validate that the proposed framework is sample efficient, safer, and generalizes better at learning complex contact-rich insertion tasks on both simulation environments and on real hardware.

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