Robot Experiments Research Articles

The Doors of Social Robot Perception: The Influence of Implicit Self-theories

Understanding people’s perceptions and inferences about social robots and, thus, their responses toward them, constitutes one of the most pervasive research themes in the field of Human–Robot interaction today. We herein augment and extend this line of work by investigating, for the first time, the novel proposition that one’s implicit self-theory orientation (underlying beliefs about the malleability of self-attributes, such as one’s intelligence), can influence one’s perceptions of emerging social robots developed for everyday use. We show that those who view self-attributes as fixed (entity theorists) express greater robot anxiety than those who view self-attributes as malleable (incremental theorists). This result holds even when controlling for well-known covariate influences, like prior robot experience, media exposure to science fiction, technology commitment, and certain demographic factors. However, only marginal effects were obtained for both attitudinal and intentional robot acceptance, respectively. In addition, we show that incremental theorists respond more favorably to social robots, compared to entity theorists. Furthermore, we find evidence indicating that entity theorists exhibit more favorable responses to a social robot positioned as a servant. We conclude with a discussion about our findings.

Robot-assisted segmental ureterectomy with psoas hitch ureteral reimplantation: Oncological, functional and perioperative outcomes of case series of a single centre.

According to the Urology guidelines, in selected cases of distal upper tract urothelial carcinoma (UTUC) segmental ureterectomy (SU) can be offered. There is no consensus in the surgical technique of preference. Robot-assisted SU could be an option to overcome all the limitations of open and laparoscopic techniques. We describe our first experience of robot assisted SU with psoas hitch ureteral reimplantation (RAPHUR). 11 patients underwent RAPHUR for distal UTUC between 2013 and 2017 in a single centre. Pre-, intra-, and postoperative outcomes were assessed. Conventional imaging was performed after 1, 3, 6 months and 1 year from surgery as follow up protocol. We retrospectively evaluated the technical feasibility, oncological and functional outcomes. Median age was 71 years (57-91). The median length of the ureteral defect was 23 mm (10-40). Median preoperative creatinine level was 1.22 mg/dl (0.7-1.85) and median eGFR was 57.5 ml/min/1.73m2 (31-80). Five (45.5%) patients were symptomatic and 7 (63.6%) had hydronephrosis. Median operative time was 185 min (120-240), with a median blood loss of 100 ml (50-300). No case required conversion to open surgery. Overall, only 1 (9%) patient developed Clavien Dindo ≥ 3 postoperative complications. Average hospital stay was 7 (2-9) days. Mean postoperative creatinine was 1.05 mg/dl (0.8-1.85) and mean postoperative eGFR was 72 (36-83). During a median follow up time of 25.5 months (12-53), 4 (36.4%) patients experienced recurrence of urothelial cancer at conventional imaging follow up and 2 (18.2%) died due to its progression. In our initial experience RAPHUR can be proposed to selected cases of distal ureteral carcinoma with optimal perioperative and functional outcomes. However, cancer control may be undermined compared to nephroureterectomy. Thus, further prospective studies are needed to confirm our findings.

Analysis and prediction of surface roughness for robotic belt grinding of complex blade considering coexistence of elastic deformation and varying curvature

Precision prediction of machined surface roughness is challenging facing the robotic belt grinding of complex blade, since this process is accompanied by significant elastic deformation. The resulting poor prediction accuracy, to a great extent, is attributed to the existing prediction model which less considers the dynamics. In this paper, an improved scallop height model is developed to predict and assess the machined surface roughness by taking into account the elastic deformation and the varying curvature of blade, then robotic belt grinding experiments are carried out to evaluate the proposed model from the perspective of surface roughness. Finally factors that influence the scallop height are analyzed, and the suitable empirical equation of surface roughness is proposed to assess and predict the surface quality from the aspect of blade concave and convex surface by adopting the constant scallop height machining.

Keypoint-Based Robotic Grasp Detection Scheme in Multi-Object Scenes.

Robot grasping is an important direction in intelligent robots. However, how to help robots grasp specific objects in multi-object scenes is still a challenging problem. In recent years, due to the powerful feature extraction capabilities of convolutional neural networks (CNN), various algorithms based on convolutional neural networks have been proposed to solve the problem of grasp detection. Different from anchor-based grasp detection algorithms, in this paper, we propose a keypoint-based scheme to solve this problem. We model an object or a grasp as a single point—the center point of its bounding box. The detector uses keypoint estimation to find the center point and regress to all other object attributes such as size, direction, etc. Experimental results demonstrate that the accuracy of this method is 74.3% in the multi-object grasp dataset VMRD, and the performance on the single-object scene Cornell dataset is competitive with the current state-of-the-art grasp detection algorithm. Robot experiments demonstrate that this method can help robots grasp the target in single-object and multi-object scenes with overall success rates of 94% and 87%, respectively.

Learning Newtonian Physics through Programming Robot Experiments

Novel technology has been applied to improve students’ learning abilities in different disciplines. The research in this field is still finding suitable methodologies, tools, and evaluation mechanisms to devise learning frameworks with high impact on students’ performance. This article describes an instructional method to perform Newtonian physics experiments by programming a mobile robot. Such experiments allow the learners to design, implement and visualize physics concepts, thus using the robot as a cognitive tool or mindtool. An accurate assessment of the students learning gain, involving 29 high-school students, shows that the proposed method provided significant improvements in the students understanding of the first Newton’s law, the second Newton law and the superposition principle. The learning gain has been measured through the Force Concept Inventory questionnaire. From this study, we can state that programming a mobile robot to perform physics experiments can improve knowledge about Newtonian physics, even without giving specific lectures in the subject and with a much shorter lecture plan with respect to traditional lectures.

Nonlinear MPC for collision-free and deadlock-free navigation of multiple nonholonomic mobile robots

In this paper, we present an online nonlinear Model Predictive Control (MPC) method for collision-free, deadlock-free navigation by multiple autonomous nonholonomic Wheeled Mobile Robots (WMRs). Our proposed method solves a nonlinear constrained optimization problem at each time step over a specified horizon to compute a sequence of optimal control inputs that drive the robots to target poses along collision-free trajectories, where the robots’ future states are predicted according to a unicycle kinematic model. To reduce the computational complexity of the optimization problem, we formulate it without stabilizing terminal constraints or terminal costs. We describe a computationally efficient approach to programming and solving the optimization problem, using open-source software tools for fast nonlinear optimization and applying the multiple-shooting method. We also provide rigorous proofs of the feasibility of the optimization problem and the stability of the proposed method. To validate the performance of our MPC method, we implement it in both 3D robot simulations and experiments with real nonholonomic WMRs for different multi-robot navigation scenarios with up to six robots. In all scenarios, the robots successfully navigate to their goal poses without colliding with one another or becoming trapped in a deadlock.

6D pose estimation with combined deep learning and 3D vision techniques for a fast and accurate object grasping

Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network and a subsequent accurate and fast 6D pose estimation based on Point Pair Feature framework to form a real-time 3D object recognition and grasping solution capable of multi-object class scenes. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy. In order to validate our method, we conducted extensive and thorough experiments involving laborious preparation of our own dataset. The experiment results show that the proposed method scores 97.37% accuracy in 5cm5deg metric and 99.37% in Average Distance metric. Experiment results have shown an overall 62% relative improvement (5cm5deg metric) and 52.48% (Average Distance metric) by using the proposed method. Moreover, the pose estimation execution also showed an average improvement of 47.6% in running time. Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy. This experiment video is available at https://sites.google.com/view/dl-ppf6dpose/.

The robot consciousness based on empirical knowledge

How to make robot conscious is an important goal of artificial intelligence. Despite the emergence of some very creative ideas, no convincing way to realize consciousness on a machine has been proposed so far. For example, the integrated information theory of consciousness proposes that consciousness can exist in any place that can reasonably process information, whether brain or machine. It points out that a physical system must satisfy two basic conditions to cause the emergence of consciousness: it must have rich information, and it must be highly integrated. However, the theory does not give an idea of how to realize consciousness on a machine. In this paper, we propose the robot consciousness based on empirical knowledge. We believe that the empirical knowledge of robots is an important basis for robot consciousness, any cognitive experience of robot can lead to the generation of consciousness. We firstly propose a formal framework for describing robot empirical knowledge; then we discuss the robot consciousness based on its own empirical knowledge; finally, we propose a cost-oriented evolutionary method of robot consciousness.

Mutual-Collision-Avoidance Scheme Synthesized by Neural Networks for Dual Redundant Robot Manipulators Executing Cooperative Tasks.

Collision between dual robot manipulators during working process will lead to task failure and even robot damage. To avoid mutual collision of dual robot manipulators while doing collaboration tasks, a novel recurrent neural network (RNN)-based mutual-collision-avoidance (MCA) scheme for solving the motion planning problem of dual manipulators is proposed and exploited. Because of the high accuracy and low computation complexity, the linear variational inequality-based primal-dual neural network is used to solve the proposed scheme. The proposed scheme is applied to the collaboration trajectory tracking and cup-stacking tasks, and shows its effectiveness for avoiding collision between the dual robot manipulators. Through network iteration and online learning, the dual robot manipulators will learn the ability of MCA. Moreover, a line-segment-based distance measure algorithm is proposed to calculate the minimum distance between the dual manipulators. If the computed minimum distance is less than the first safe-related distance threshold, a speed brake operation is executed and guarantees that the robot cannot exceed the second safe-related distance threshold. Furthermore, the proposed MCA strategy is formulated as a standard quadratic programming problem, which is further solved by an RNN. Computer simulations and a real dual robot experiment further verify the effectiveness, accuracy, and physical realizability of the RNN-based MCA scheme when manipulators cooperatively execute the end-effector tasks.

태양광 패널 청소용 로봇의 설계, 구현 및 실험

태양광 패널 청소용 로봇의 설계, 구현 및 실험

Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators

With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning is designed. The stability is investigated via Lyapunov type techniques where global uniform ultimate boundedness of closed loop system is guaranteed. Numerical simulation results obtained from a two degree of freedom robot manipulator model and experiments performed on a robot manipulator demonstrate the efficacy of the proposed control methodology.

A Disturbance Rejection Control Method Based on Deep Reinforcement Learning for a Biped Robot

The disturbance rejection performance of a biped robot when walking has long been a focus of roboticists in their attempts to improve robots. There are many traditional stabilizing control methods, such as modifying foot placements and the target zero moment point (ZMP), e.g., in model ZMP control. The disturbance rejection control method in the forward direction of the biped robot is an important technology, whether it comes from the inertia generated by walking or from external forces. The first step in solving the instability of the humanoid robot is to add the ability to dynamically adjust posture when the robot is standing still. The control method based on the model ZMP control is among the main methods of disturbance rejection for biped robots. We use the state-of-the-art deep-reinforcement-learning algorithm combined with model ZMP control in simulating the balance experiment of the cart–table model and the disturbance rejection experiment of the ASIMO humanoid robot standing still. Results show that our proposed method effectively reduces the probability of falling when the biped robot is subjected to an external force in the x-direction.

Experiments in Artificial Sociality

In this article, I explore how experiments with social robots enact and reconfigure more-than-human forms of sociality. I combine recent anthropological discussions of nonhuman sociality with Andy Pickering’s work on dances of agency (1993, 1995) and John Law’s method assemblages (2004) to show how human-robot interaction experiments enact open-ended and decentred configurations of entangling relations between humans and robots. I propose the concept of artificial sociality to capture both the ongoing enactments and multiple results of such experimental reconfigurations. Using these conceptual tools, I unpack the “curious robot experiment” from my ethnographic fieldwork in a Japanese robotics laboratory and compare the kinds of sociality produced in the two experimental conditions. I argue that the curious robot exemplifies what Pickering calls technologies of engagement (2018) by manifesting a form of artificial sociality that augments the unpredictability of dances of agency enacted in (re)configurations of entangling relations.

Distributed Formation Control for Multiagent Systems Using a Fractional-Order Proportional–Integral Structure

This work presents the development of a distributed formation control for multiagent systems (MASs) using a fractional-order proportional–integral (FOPI) structure. The fractional control protocol proposed is evaluated on agents modeled with single- and double-integrator dynamics, in which it enhances the convergence speed of the formation and improves the robustness of the system. In addition, a novel method to study the stability and convergence of the MASs in the frequency domain is designed. This allows deriving constraints on the control parameters to guarantee the stability and ensure the formation when the system topology is modeled by a time-invariant undirected graph. Finally, realistic simulations through MATLAB and a Virtual Robot Experimentation Platform (V-REP) validate the effectiveness of the theoretical results.

Cultura maker e robótica sustentável como estratégia para ensinar conceitos de electricidade

The Maker movement (MM) is a methodology based on the “do it yourself ” assumptions to enable the student to have real interaction with the concrete material. Thus, in this paper, we present an experience of educational robotics using electronic waste as a didactic resource to teach electricity concepts o middle school students. The results showed that the proposal was successful and enabled the learning of electrical power, electrical current, electrical resistance, and matter and energy interaction. Based on our investigation, we believe that this research is relevant because it allows science classes to be more interesting for students.

Adaptive climbing motion of snake robot based on joint decoupling and extended Kalman filter

This paper proposes a decoupled gait control function to solve the adaptive transition problem of the snake robot climbing the rod with variable diameter. Firstly, the Sigmoid function is combined with the general gait control function to achieve the decoupling control of the robot joints. Then an adaptive adjustment algorithm of key control parameters is designed based on the extended Kalman filter (EKF) to realize the decoupling gait changes continuously and compliantly as the environment changes. Finally, the physical meanings of the key control parameters are reiterated by simulation, and their influence on the decoupling of climbing gait is analyzed. The smooth transition climbing experiments of the snake robot on rods with different diameters verify the effectiveness of the decoupling and adaptive algorithm.

An Efficient Method to Recover Relative Pose for Vehicle-Mounted Cameras Under Planar Motion

In this paper, a 2-point algorithm is proposed to estimate the relative pose as well as the absolute scale between two vehicle-mounted cameras efficiently. The system model is deduced by combining a two-view geometric model and the planar motion constraint to reduce the degrees of freedom. 2-point correspondences are utilized to calculate the rotation information independently from the translation information, which indicates that the proposed algorithm can deal with pure rotation scenes. Besides, provided that the camera's configuration satisfies a certain condition, the absolute scale can be recovered. An approximation algorithm is developed and combined with the random sample and consensus scheme to deal with the uneven ground surfaces in practice. As only 2-point correspondences are required, less iterations are demanded in the estimating procedure compared with many other existing related algorithms. Both simulation and experiments are implemented to evaluate the proposed algorithm, in which the synthetic data, virtual robot experimentation platform, KITTI Vision Benchmark, and SUMMIT-XL platform are acquired. According to the results, the proposed algorithm performs better than many related algorithms including the well-known 5-point algorithm in many cases, especially when the camera's trajectory contains sharp corners.

Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow

Self‐Driven Multistep Quantum Dot Synthesis Enabled by Autonomous Robotic Experimentation in Flow

A smart path planner for wheeled mobile robots using adaptive particle swarm optimization

Finding the shortest path to the destination is a vital need for autonomous mobile robots. In this article, a smart adaptive particle swarm optimization (APSO) algorithm is proposed for robot path planning. It allows the robot to reach the target point with the shortest possible path and to avoid the obstacles safely in uncertain environments. A new objective function is derived with distance function and a path smoothening parameter is integrated to avoid sharp turns. The results of the proposed method rely on computer simulation and real robot experimentation in different environments. It is proved that they are in good agreement. A comparative study between the proposed algorithm and various other algorithms is also presented. The results showed that the proposed smart algorithm is capable of successfully avoiding various types of obstacles including the local minima situation.

Grinding trajectory generator in robot-assisted laminectomy surgery.

Grinding trajectory planning for robot-assisted laminectomy is a complicated and cumbersome task. The purpose of this research is to automatically obtain the surgical target area from the CT image, and based on this, formulate a reasonable robotic grinding trajectory. We propose a deep neural network for laminae positioning, a trajectory generation strategy, and a grinding speed adjusting strategy. These algorithms can obtain surgical information from CT images and automatically complete grinding trajectory planning. The proposed laminae positioning network can reach a recognition accuracy of 95.7%, and the positioning error is only 1.12mm in the desired direction. The simulated surgical planning on the public dataset has achieved the expected results. In a set of comparative robotic grinding experiments, those using the speed adjustment algorithm obtained a smoother grinding force. Our work can automatically extract laminar centers from the CT image precisely to formulate a reasonable surgical trajectory plan. It simplifies the surgical planning process and reduces the time needed for surgeons to perform such a cumbersome operation manually.