Iterative learning of human partner's desired trajectory for proactive human-robot collaboration

A period-varying iterative learning control scheme is proposed for a robotic manipulator to learn a target trajectory that is planned by a human partner but unknown to the robot, which is a typical scenario in many applications. The proposed method updates the robot’s reference trajectory in an iterative manner to minimize the interaction force applied by the human. Although a repetitive human–robot collaboration task is considered, the task period is subject to uncertainty introduced by the human. To address this issue, a novel learning mechanism is proposed to achieve the control objective. Theoretical analysis is performed to prove the performance of the learning algorithm and robot controller. Selective simulations and experiments on a robotic arm are carried out to show the effectiveness of the proposed method in human–robot collaboration

(S)-tert-Butyl 3-(3-phenyl-1,2,4-oxa­diazol-5-yl)piperidine-1-carboxyl­ate

The title compound, C18H23N3O3, crystallized with two independent mol­ecules (A and B) in the asymmetric unit. The phenyl ring and the 1,2,4-oxadiazole ring are inclined to one another by 19.9 (3)° in mol­ecule A and 7.3 (3)° in mol­ecule B. The absolute structure of the title compound was referred to the transfered chiral center (S) of one of the starting reacta­nts. In the crystal, A mol­ecules are linked by C—H⋯N inter­actions involving the two oxadiazole N atoms

Path learning in human-robot collaboration tasks using iterative learning methods

In a repetitive human-robot collaboration (HRC) task, robots typically are required to learn the intended motion of the human user to improve the collaboration efficiency. However, the human user's trajectory is of uncertainty when repeating the same task (e.g., human hand tremor and uncertain movement speed), which may directly deteriorate the learning performance. To address this issue, a path characterized by spatial correlation parameters, is of necessity to be learned by robots so that the aforementioned time-related uncertainty will be avoided. In this article, based on the path parameterization, a gradient-based iterative path learning (IPL) strategy is designed for the robot to learn the desired path of human. The proposed IPL strategy draws on the iterative learning methods with a properly designed performance index. Since the gradient of the performance index is hard to directly obtain in HRC, two learning methods with gradient search (GS) and gradient estimation (GE) are developed. The GS estimates the gradient online and has an advantage of easy implementation. By contrast, the advantage of GS is more obvious when the number of learned parameters is considerable due to its high learning efficiency. With these two methods, the unknown path parameters can be iteratively updated toward the desired values. To verify the effectiveness of the proposed IPL algorithm, experiments are carried out. In the experiment, a comparison between GS and GE methods is made to display their respective advantages. Besides, the proposed IPL is compared with an existing trajectory learning method subject to two different kinds of uncertainties and its better learning performance verifies its stability and capability in dealing with uncertainty

Robot assisted training for upper limbs using impedance control based on iterative learning

This paper proposes an approach to improve robot assisted physical training subject to human uncertainties. This approach is based on impedance control which is used to regulate the dynamic relationship between the robot’s position and contact force. Repetitive exercise is considered and impedance parameters are adapted in accordance with the human user to provide physical training as needed. Different from the existing approaches, the proposed one has the capacity to deal with time-length-varying cycles, which is a critical issue in physical training of human’s upper limbs. By theoretical analysis and experimental results, we show that the approach can effectively learn the required robot’s impedance parameters and improve the performance of physical training

Iterative learning control based on stretch and compression mapping for trajectory tracking in human-robot collaboration

This paper presents a novel iterative learning control (ILC) scheme based on stretch and compression mapping for a robotic manipulator to learn its human partner’s desired trajectory, which is a typical task in the field of human-robot interaction. The proposed scheme is used to reduce the interaction force between the robot and the human partner in repetitive learning process. Thus, the robot can track the human partner’s repetitive trajectory with a small interaction force, leading to little control effort from the human. As the human is involved in the control loop, there are various uncertainties in the system, including variable iteration period in the task under study. The stretch and compression mapping is applied to this problem. In the simulation, the proposed scheme is implemented in the human-robot interaction scenario. Results confirm the effectiveness of the proposed scheme and also illustrate better performance of the proposed ILC compared with other ILC methods with variable periods

Adaptive virtual guides for compliance control skill teaching in partially known tasks

Compliant manipulation is widely studied in the domain of robotics due to urgent industrial demands of flexible production. In many tasks that require human demonstration of the end-effector’s pose and contact force, cognitive and physical loads on human operators are high. Fortunately, virtual guides (VGs) can effectively assist human users to carry out the corresponding work. This article aims to design an adaptive virtual guides (AVGs) scheme for kinesthetic teaching that can be applied to surface finishing and to reduce human load. For this purpose, the end-effector’s orientation is automatically adjusted according to the workpiece’s 3D model and the end-effector’s position. The force provided by the robot along the end-effector’s z-axis is adjustable, which is used to reduce human’s control effort for contact force demonstration. Our method utilizes the virtual mass-damper system and joint admittance control model for the AVGs design, whose validity is verified on a Sawyer robot in simulation

Spatial iterative learning control for robotic path learning

A spatial iterative learning control (sILC) method is proposed for a robot to learn a desired path in an unknown environment. When interacting with the environment, the robot initially starts with a predefined trajectory so an interaction force is generated. By assuming that the environment is subjected to fixed spatial constraints, a learning law is proposed to update the robot's reference trajectory so that a desired interaction force is achieved. Different from existing iterative learning control methods in the literature, this method does not require repeating the interaction with the environment in time, which relaxes the assumption of the environment and thus addresses the limits of the existing methods. With the rigorous convergence analysis, simulation and experimental results in two applications of surface exploration and teaching by demonstration illustrate the significance and feasibility of the proposed method

A proactive controller for human-driven robots based on force/motion observer mechanisms

This article investigates human-driven robots via physical interaction, which is enhanced by integrating the human partner's motion intention. A human motor control model is employed to estimate the human partner's motion intention. A system observer is developed to estimate the human's control input in this model, so that force sensing is not required. A robot controller is developed to incorporate the estimated human's motion intention, which makes the robot proactively follow the human partner's movements. Simulations and experiments on a physical robot are carried out to demonstrate the properties of our proposed controller

Spatial iterative learning control with human guidance and visual detection for path learning and tracking

A popular path learning method is to use off-line programming by demonstration (PbD) to plan a rough path, but it is subjected to uncertainties in the environment so needs to be updated during the task execution. For this purpose, a spatial iterative learning control (sILC) is developed to learn an accurate path through intuitive online correction based on human-robot interaction (HRI). To improve the efficiency and accuracy of the path learning, a visual assistance system is added to HRI, which helps the robot to find the initial path point and complement the correction of the learning error. This method mitigates the requirement on classic ILC that the time period should be consistent in the repetitive interaction task and utilizes the complementary advantages of vision and force sensing, thus addressing the limitations of the vision-based or HRI methods. The rigorous proof of learning convergence and the results of the simulation and experiments on a 7-degree-of-freedom (DoF) Sawyer robot platform illustrate the significance and advantages of the proposed method

Preparation of flower-like hydrogel and its application in sea water desalination

The situation of global water crisis is becoming more and more serious[1]. Due to the inconvenience of fresh water carrying or long-term storage and deterioration, the personnel and equipment of oceangoing ships are in urgent need of fresh water resources[2]. However, the traditional seawater desalination technology will consume fossil energy or its economy is not high. Under the background of a series of green ship development plans, people focus on green clean energy to solve the problem of fresh water shortage[3]. Interfacial photoevaporation is an effective strategy to promote seawater desalination and pollutant treatment. Photothermal conversion materials and evaporators have shown their good performance in improving seawater desalination efficiency. In this paper, the photohot water gel prepared by calcium chloride, polyvinyl alcohol, sodium citrate, tannic acid and ferric chloride was used as the photothermal conversion material[4]. The properties of photoevaporative seawater desalination materials were studied by a series of instruments such as optical microscope and contact Angle measuring instrument. The experimental results show that the flower-like hydrogel has high photothermal conversion efficiency and has a good application prospect in green Marine desalination field