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 Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

2021 ◽  
Vol 101 (4) ◽  
Author(s):  
Michele Ginesi ◽  
Daniele Meli ◽  
Andrea Roberti ◽  
Nicola Sansonetto ◽  
Paolo Fiorini
Keyword(s):  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Potential Functions ◽  
Movement Primitives ◽  
Dynamic Movement ◽  
Dynamic Potential ◽  
Place Task ◽  
Dynamic Movement Primitives ◽  
Definition Of ◽  
New Formulation

AbstractObstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.

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.

Dynamic Movement Primitives: Volumetric Obstacle Avoidance

2019 ◽  
Author(s):  
Michele Ginesi ◽  
Daniele Meli ◽  
Andrea Calanca ◽  
Diego Dall'Alba ◽  
Nicola Sansonetto ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Movement Primitives ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

scholarly journals Composite dynamic movement primitives based on neural networks for human–robot skill transfer

2021 ◽  
Author(s):  
Weiyong Si ◽  
Ning Wang ◽  
Chenguang Yang
Keyword(s):  
Neural Networks ◽  
Obstacle Avoidance ◽  
Skill Acquisition ◽  
Robot Manipulator ◽  
Skill Learning ◽  
Skills Transfer ◽  
Movement Primitives ◽  
Dynamic Movement ◽  
Position And Orientation ◽  
Dynamic Movement Primitives

AbstractIn this paper, composite dynamic movement primitives (DMPs) based on radial basis function neural networks (RBFNNs) are investigated for robots’ skill learning from human demonstrations. The composite DMPs could encode the position and orientation manipulation skills simultaneously for human-to-robot skills transfer. As the robot manipulator is expected to perform tasks in unstructured and uncertain environments, it requires the manipulator to own the adaptive ability to adjust its behaviours to new situations and environments. Since the DMPs can adapt to uncertainties and perturbation, and spatial and temporal scaling, it has been successfully employed for various tasks, such as trajectory planning and obstacle avoidance. However, the existing skill model mainly focuses on position or orientation modelling separately; it is a common constraint in terms of position and orientation simultaneously in practice. Besides, the generalisation of the skill learning model based on DMPs is still hard to deal with dynamic tasks, e.g., reaching a moving target and obstacle avoidance. In this paper, we proposed a composite DMPs-based framework representing position and orientation simultaneously for robot skill acquisition and the neural networks technique is used to train the skill model. The effectiveness of the proposed approach is validated by simulation and experiments.

Dynamic movement primitives based cloud robotic skill learning for point and non-point obstacle avoidance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhenyu Lu ◽  
Ning Wang
Keyword(s):  
Obstacle Avoidance ◽  
Skill Learning ◽  
Unknown Parameters ◽  
Content Type ◽  
Learning Framework ◽  
Movement Primitives ◽  
Dynamic Movement ◽  
Forcing Function ◽  
Universal Space ◽  
Dynamic Movement Primitives

Purpose Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills changed by the environment, rebuild the DMPs model and propose a new DMPs-based skill learning framework removing the influence of the changing environment. Design/methodology/approach The authors proposed methods for two obstacle avoidance scenes: point obstacle and non-point obstacle. For the case with point obstacles, an accelerating term is added to the original DMPs function. The unknown parameters in this term are estimated by interactive identification and fitting step of the forcing function. Then a pure skill despising the influence of obstacles is achieved. Using identified parameters, the skill can be applied to new tasks with obstacles. For the non-point obstacle case, a space matching method is proposed by building a matching function from the universal space without obstacle to the space condensed by obstacles. Then the original trajectory will change along with transformation of the space to get a general trajectory for the new environment. Findings The proposed two methods are certified by two experiments, one of which is taken based on Omni joystick to record operator’s manipulation motions. Results show that the learned skills allow robots to execute tasks such as autonomous assembling in a new environment. Originality/value This is a new innovation for DMPs-based cloud robotic skill learning from multi-scene tasks and generalizing new skills following the changes of the environment.

scholarly journals Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance

2021 ◽  
Vol 11 (23) ◽  
pp. 11184
Author(s):  
Ang Li ◽  
Zhenze Liu ◽  
Wenrui Wang ◽  
Mingchao Zhu ◽  
Yanhui Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Obstacle Avoidance ◽  
Path Integrals ◽  
Additional Term ◽  
Robot Arm ◽  
Shape Parameters ◽  
Movement Primitives ◽  
Model Free ◽  
Dynamic Movement ◽  
Dynamic Movement Primitives

Dynamic movement primitives (DMPs) are a robust framework for movement generation from demonstrations. This framework can be extended by adding a perturbing term to achieve obstacle avoidance without sacrificing stability. The additional term is usually constructed based on potential functions. Although different potentials are adopted to improve the performance of obstacle avoidance, the profiles of potentials are rarely incorporated into reinforcement learning (RL) framework. In this contribution, we present a RL based method to learn not only the profiles of potentials but also the shape parameters of a motion. The algorithm employed is PI2 (Policy Improvement with Path Integrals), a model-free, sampling-based learning method. By using the PI2, the profiles of potentials and the parameters of the DMPs are learned simultaneously; therefore, we can optimize obstacle avoidance while completing specified tasks. We validate the presented method in simulations and with a redundant robot arm in experiments.

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
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