Robot Experiments Research Articles

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments.

Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery

The urgency of finding solutions to global energy, sustainability, and healthcare challenges has motivated rethinking of the conventional chemistry and material science workflows. Self‐driving labs, emerged through integration of disruptive physical and digital technologies, including robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, have the potential to accelerate the pace of materials and molecular discovery by 10–100X. Using autonomous robotic experimentation workflows, self‐driving labs enable access to a larger part of the chemical universe and reduce the time‐to‐solution through an iterative hypothesis formulation, intelligent experiment selection, and automated testing. By providing a data‐centric abstraction to the accelerated discovery cycle, in this perspective article, the required hardware and software technological infrastructure to unlock the true potential of self‐driving labs is discussed. In particular, process intensification as an accelerator mechanism for reaction modules of self‐driving labs and digitalization strategies to further accelerate the discovery cycle in chemical and materials sciences are discussed.

An Improved Spanning Tree-Based Algorithm for Coverage of Large Areas Using Multi-UAV Systems

In this work, we propose an improved artificially weighted spanning tree coverage (IAWSTC) algorithm for distributed coverage path planning of multiple flying robots. The proposed approach is suitable for environment exploration in cluttered regions, where unexpected obstacles can appear. In addition, we present an online re-planner smoothing algorithm with unexpected detected obstacles. To validate our approach, we performed simulations and real robot experiments. The results showed that our proposed approach produces sub-regions with less redundancy than its previous version.

A Random Sampling-Based Method via Gaussian Process for Motion Planning in Dynamic Environments

Motion planning is widely applied to industrial robots, medical robots, bionic robots, and smart vehicles. Most work environments of robots are not static, which leads to difficulties for robot motion planning. We present a dynamic Gaussian local planner (DGLP) method to solve motion planning problems in dynamic environments. In a dynamic environment, dynamic obstacles sometimes make part of the global path invalid, so the local invalid path needs to be local re-planned online. Compared with the node sampling-based methods building large-scale random trees or roadmaps, the Gaussian random path sampling (GRPS) module integrated in the DGLP directly samples smooth random paths discretized into sparse nodes to improve the local path re-planning efficiency. We also provide the path end orientation constraint (PEOC) method for the local re-planning paths in order to merge them smoothly into the global paths. In the robot experiments, the average planning time of the DGLP is 0.04s, which is at least 92.31% faster than the test methods, and its comprehensive evaluation scores, which consider the consuming time, path quality, and success rate of local re-planning, are at least 44.92% higher than the test methods. The results demonstrate that the proposed DGLP method is able to efficiently provide high-quality local re-planning paths in dynamic environments.

HPB P22 The stepwise implementation of robotic HPB program in a single UK centre; a continuum of practice

Abstract Background Since the first documented use of a robot for surgery in 1985, there has been a rapid global adoption of this technology. Hepatobiliary and pancreatic (HPB) surgery centres worldwide are increasingly sharing their robotic experiences, testing its potential to become a new standard for surgery. We are among the small number of known robotic HPB programs in the UK and here describe our stepwise implementation and share our learning curve. Methods We performed a retrospective review of our prospective database of robotic HPB surgeries carried out by two surgeons from the start of the program in September 2021 up to May 2022. Metrics analysed included surgery console times and 30-day patient perioperative outcomes. Results are shown as mean +/- std unless stated otherwise. Results There were 53 cases performed in 8 months. For all cases mean patient age was 58 years (range 26–82 years), BMI was 27.3 ± 4.3 Kg/m2 and docking time was 3.20 ± 0.97 minutes. There were 2 defined performance phases; early practice which included the following cases - marsupialisation of liver cysts (n = 7), cholecystectomies (n = 22) and radical cholecystectomies (n = 5). Intermediate practice followed when no decrease in console time was observed, and patients’ safety was maintained, and cases included liver wedge resections (n = 9) and distal pancreatectomies (n = 10). Mean console time for marsupialisation of liver cysts was 39 ± 0.01 minutes, cholecystectomies 44 ± 0.01 minutes, radical cholecystectomies 109 ± 0.01 minutes, liver wedge resections 87 ± 0.04 minutes and distal pancreatectomies 153 ± 0.03 minutes. Length of stay for distal pancreatectomies was 6.8 ± 2.2 days and liver wedge resections 2.3 ± 1.1 days. There was 1 conversion to open, 1 reoperation and 3 readmissions. There were 9 postoperative complications Clavien-Dindo grade I (n=1), grade II (n=5), grade IIIa (n= 2) and grade IIIb (n= 1). The rate of pancreatic fistula (International Study Group of Pancreatic Fistula classification grade B) was 3% (n=3), there were no 30-day mortalities. Conclusions Our outcomes show acceptable safety metrics during the implementation our HPB robotic program. Only after evaluating our learning curve and patients’ outcomes in each performance phase do we progress to the next phase, which for our program is the complex phase and includes pancreaticoduodenectomy and more complex liver resections.

Biomimetic Electric Sense-Based Localization: A Solution for Small Underwater Robots in a Large-Scale Environment

This article presents a novel localization scheme for free-swimming small underwater robots in a large-scale environment. Accurate localization technology has always been a challenge for small underwater robots since the underwater lighting conditions can limit their vision, while the multipath interference issue occurring near a wall plagues the sonars. By contrast, some kinds of fish perceive their positions by sensing the electric field in their environment, giving them the ability to accurately localize in underwater environments. Inspired by the electric sense behavior in fish, this article proposes a large-scale localization scheme based on electric sense for small underwater robots. Our scheme includes an electric sense-based hardware solution and localization methods. Specifically, we first design a hardware solution, including electric emitters placed in an underwater environment and an electric receiver that can be furnished on a small underwater robot. Then, we propose distributed emitter architectures for large-scale localization. Finally, we propose three localization methods to estimate the position and orientation of the robot. We have conducted four types of localization experiments for a small underwater robot, demonstrating the robustness and effectiveness of our proposed electric sense-based scheme. Our study provides a novel solution to the localization of free-swimming underwater robots with a limited payload and also helps provide insights into large-scale underwater localization.

Condition monitoring and mechanism analysis of belt wear in robotic grinding of TC4 workpiece using acoustic emissions

Sharp abrasion of the abrasive belt always causes surface quality to deteriorate in the robotic grinding of thin-walled blades, which has stymied the further advancement of robot machining technology. A novel belt wear prediction approach based on the acoustic emissions (AE) signal is presented in this study to monitor belt wear conditions to achieve the expected machining result. A time-varying model of belt wear height and tangential force is initially established by employing the force analysis for various belt wear states. Next, a relationship between AE signal power and belt wear height is established utilizing the energy principle. Finally, robotic machining experiments on Ti-6Al-4V alloy workpieces are performed to achieve accurate belt wear prediction of an average error of approximately 10%, and the influence analysis of belt wear on the surface quality is further investigated to extend effective service life of abrasive belt by about 20% and realize the desired blade’s machining quality of surface roughness Ra<0.4 μm and contour accuracy ≤±0.17mm.

A Novel Toolpath for Robotic Adaptive Grinding of Extremely Thin Blade Edge Based on Dwell Time Model

High precision and high efficiency in robotic grinding of aero-engine extremely thin blade edges is a challenging problem. In this article, a novel toolpath planning method is proposed based on the derived dwell time calculation model and the developed interpolation algorithm. The dwell time of each cutter contact point is solved based on the matrix method by consideration of the revised material removal model and machining allowance distribution. To optimize the previous interpolation algorithm based on geometric intersection, an arc length and Taylor binary expansion optimization are proposed to plan cutter contact points according to the dwell time and the curvature changes of blade edges. Subsequently, the tool orientation is corrected based on a double-vector controlling method to avoid the processing interference. Simulation results show that the proposed interpolation algorithm has improved the path coverage by 17.6%, and its computational efficiency is 34 times higher than that in previous algorithm. The comparative robotic grinding experiment results show that the proposed toolpath has improved the surface profile accuracy of blade edge to 0.042 mm, and its machining accuracy is 14.3–46.2% higher than that of other methods.

THE USE OF ROBOTS FOR DELIVERY OF PACKAGES IN FOOD INDUSTRIES AND ITS IMPACT ON JOB LOSS

Artificial intelligence (AI) and robotics have developed rapidly in the last few years, both in terms of the resources and outputs they produce. The robots that work in the food industry contribute significantly to the transportation of food packages from one location to another. The main task of the robots in the food industry on which this research focuses is delivery of packages. Before the implementation of robots in food industries and in delivery packages, it was done by human beings. Now that the robots are utilised, human beings will lose their jobs. This might not necessarily be the case or there might be some unforeseen issues. That is why this research aims to investigate the influence of system quality, information quality, and business value on the delivery of packages by robots in replacing people's jobs. This study applied a quantitative research method in order to achieve all of the research objectives. A total of 310 participants took part in the survey. Based on PLS-SEM analysis, the path coefficient of system quality to business value was examined by hypothesis one (H1), and it was shown to be supported. It is clear that the relationship between system quality and business value is significant because there is a strong link between system quality and business value. Furthermore, the path coefficient of information quality to business value was investigated in hypothesis two (H2).Our second hypothesis is supported by the p-value being significant and positive. Hypothesis three (H3) is significant, as evidenced by the fact that it is supported. As a result, there are important connections between the use of robots and business value. Finally, the business value, which is a dependent variable, has a positive and significant relationship with all independent variables of this research. There is a strong path coefficient link from business value to system quality, information quality, and the use of robots. In the measurement model, there are substantial disparities between the industries with high levels of working experience and those with less working experience of robots. Information quality, use of robots, and business value all have positive and significant influences on the use of robots in the food industry (delivery of packages by robots). Furthermore, the analyses of PLS-SEM also show that system quality has a positive relationship but not a significant impact. Technological skills are highly important in order to improve and adopt delivery of packages by robots in the food industry. In addition, the findings of this research contribute to the literature on robots’ implementation in the food industry and also contribute to limited empirical research on assessing the delivery packages by robots in food industries on replacing people's jobs.

Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control.

In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.

Energy Autonomy for Robot Systems With Constrained Resources

One of the key factors for extended autonomy and resilience of battery-powered multirobot systems is their ability to maintain energy sufficiency by recharging when needed. In situations with limited access to charging facilities, robots need to be able to share and coordinate recharging activities, with guarantees that no robot will run out of energy. In this work, we present an approach based on control barrier functions (CBFs) to enforce both energy sufficiency (ensuring that no robot runs out of battery) and coordination constraints (guaranteeing mutual exclusive use of an available charging station) in a mission agnostic fashion. Moreover, we investigate the system capacity in terms of the relation between individual robot properties and the limit on temporal separation requirements within charging cycles. We show physics-based simulation results as well as real robot experiments that demonstrate the effectiveness of the proposed approach.

Sliding Mode Impedance Control for Dual Hand Master Single Slave Teleoperation Systems

For the purpose of avoiding injury and realizing precise operations, the multilateral teleoperation system is the most efficient way to transport trace toxic or radioactive substances, to perform minimally invasive surgery, etc. It is essential to enhance the transparency of a multilateral teleoperation system including multiple masters and the single slave manipulator. However, there are few researchers focus on the allocation of the contact force of the single slave manipulator to different master manipulators. In this paper, we firstly introduce the concept of force translation for teleoperation systems consisting of dual hand master (left and right hands) manipulators and a single slave manipulator. Force translation reflects how the impedance on the single slave side is translated or allocated to contact forces on different master sides. To maintain the stability of the system and to improve transparency, we elucidate the mechanism of the force translation and analyze the relation among masters and the slave. Furthermore, the force translation mechanism is analyzed through numerical simulations and CHAI 3D virtual physical simulations. It is used to propose a multilateral impedance control, and the Lyapunov function is used to analyze the system stability. The results of numerical simulations and real robot experiments verify the effectiveness of the proposed control methods based on the proposed force translation mechanism.

A Review on Usability and User Experience of Assistive Social Robots for Older Persons

In the advancement of human-robot interaction technology, assistive social robots have been recognized as one of potential technologies that can provide physical and cognitive supports in older person’s care. However, a major challenge faced by the designers is to develop an assistive social robot with prodigious usability and user experience for older persons who were known to have physical and cognitive limitations. A considerable number of published literatures was reporting on the technological design process of assistive social robots. However, only a small amount of attention has been paid to review the usability and user experience of the robots. The objective of this paper is to provide an overview of established researches in the literatures concerning usability and user experience issues faced by the older persons when interacting with assistive social robots. The authors searched relevant articles from the academic databases such as Google Scholar, Scopus and Web of Science as well as Google search engine for the publication period 2000 to 2021. Several search keywords were typed such as ‘older persons’ ‘elderly’, ‘senior citizens’, ‘assistive social robots’, ‘companion robots’, ‘personal robots’, ‘usability’ and ‘user experience’. This online search found a total of 215 articles which are related to assistive social robots in elderly care. Out of which, 54 articles identified as significant references, and they were examined thoroughly to prepare the main content of this paper. This paper reveals usability issues of 28 assistive social robots, and user experience feedback based on 41 units of assistive social robots. Based on the research articles scrutinized, the authors concluded that the key elements in the design and development of assistive social robots to improve acceptance of older persons were determined by three factors: functionality, usability and users’ experience. Functionality refers to ability of robots to serve the older persons. Usability is ease of use of the robots. It is an indicator on how successful of interaction between the robots and the users. To improve usability, robot designers should consider the limitations of older persons such as vision, hearing, and cognition capabilities when interactingwith the robots. User experience reflects to perceptions, preferences and behaviors of users that occur before, during and after use the robots. Combination of superior functionality and usability lead to a good user experiencein using the robots which in the end achieves satisfaction of older persons.

Design, analysis, and application of projected k-winner-take-all network

Competition is considered to be a driving force of many swarm systems in maintaining vitality, as important as collaboration. To model this competitive nature, an operation called k-winner-take-all (kWTA) is introduced that can output k maximum values from p input signals with 1⩽k⩽p. In this paper, different projection functions (PFs) are combined with kWTA operations as activation functions for the first time, and a P-kWTA network is proposed, analyzed, and applied, which accelerates the convergence rate and enhances the robustness of existing models. With the aid of various characteristics, such as saturated or nonconvex, the convergent process of the proposed P-kWTA network is able to present diverse effects of PFs. Additionally, numerical simulations, an application to a multi-agent system, and an experiment of multiple E-puck robots on the CoppeliaSim platform all demonstrate the feasibility and effectiveness of the P-kWTA network.

Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator

The improvement of recurrent neural network (RNN) algorithms is one of targets of many researchers, and these algorithms are widely used to solve time-variant problems in a variety of domains. A novel direct derivation scheme of discrete time-variant RNN (DT-RNN) algorithm for addressing discrete time-variant matrix pseudo-inversion is discussed in this paper. To be more specific, firstly, a DT-RNN algorithm mathematically founded on the second-order Taylor expansion is proposed for dealing with discrete time-variant matrix pseudo-inversion, and it does not require the theoretical support of continuous time-variant RNN (CT-RNN) algorithm. Secondly, the results of theoretical analyses of the proposed DT-RNN algorithm are also presented in this paper. These results demonstrate that the novel DT-RNN algorithm has remarkable computing performance. The efficiency and applicability of the DT-RNN algorithm have been verified through one numerical experiment and two robotic manipulator experiments.

The Construction of Robot Experiment Teaching Course for the Cultivation of Outstanding Talents in Intelligent Manufacturing

With the continuous progress of science and technology, intelligent manufacturing has become one of the main directions of industrial development, and training outstanding talents for it has become a historical mission that relevant universities must complete. As an important basis for realizing intelligent manufacturing, robot learning and application naturally become a key link in the cultivation of outstanding talents in intelligent manufacturing. On the basis of theoretical learning, the construction of robot experimental teaching courses can truly cultivate outstanding talents with practical application ability. This paper takes the construction of "Autonomous Navigation and Obstacle Avoiding Mobile Robot System Design Experiment" as an example to analyze how to build a student centered, interest oriented, practice based, and education oriented robot experiment teaching course that combines reality with fiction. This experimental course is based on scientific research projects, built around the student center, and oriented to teaching needs. It includes robot autonomous positioning and navigation, autonomous path planning, full coverage path planning, etc. Its experimental content, teaching methods, and concept design can be applied to the construction of similar experimental courses, with great promotion and application value.

Development and assessment of a contactless 3D joystick approach to industrial manipulator gesture control

This paper explores a novel design of ergonomic gesture control with visual feedback for the UR3 collaborative robot that aims to allow users with little to no familiarity with robots to complete basic tasks and programming. The principle behind the design mirrors that of a 3D joystick but utilises the Leapmotion device to track the user's hands and prevents any need for a physical joystick or buttons. The Rapid Upper Limb Assessment (RULA) ergonomic tool was used to inform the design and ensure the system was safe for long-term use. The developed system was assessed using the RULA tool for an ergonomic score and through an experiment requiring 19 voluntary participants to complete a basic task with both the gesture system and the UR3's RTP (Robot Teach Pendant), then filling out SUS (System Usability Scale) questionnaires to compare the usability of both systems. The task involved controlling the robot to pick up a pipe and then insert it into a series of slots of decreasing diameter, allowing for both the speed and accuracy of each system to be compared. The experiment found that even those with no previous robot experience were able to complete the tasks after only a brief description of how the gesture system works. Despite beating the RTP's ergonomic score, the system narrowly lost on average usability scores. However, as a contactless gesture system it has other advantages over the RTP and through this experiment many potential improvements were identified, paving the way for future work into assessing the significance of including the visual feedback and comparing this system against other gesture-based systems. Relevance to IndustryIn industrial environments where the robots may need to be frequently reprogrammed through complex RTPs, companies must call on those with specialist training even to make minor adjustments. The presented system takes advantage of gesture control to allow for easy interaction with industrial robots, even for untrained operators.

Remote Laboratory as an Educational Tool in Robotics Experimental Course

A remote lab is a technology that allows participants to efficiently conduct experimental teaching where users can connect to lab equipment from anywhere without being in a specific physical location. The COVID-19 pandemic affects all areas of human activity. ​As a result, students did not receive face-to-face instruction, and access to the laboratory was limited or practically impossible, and access to laboratory facilities has been limited or nearly impossible. Especially in engineering education, students’ practical abilities cannot be developed comprehensively. In this paper, this paper built an online remote robotics experiment system using digital twin (DT) technology and IoT technology and adopted ADDIE (Analysis, Design, Development, Implementation, and Evaluation) teaching method. With these measures, students can design and debug robot programs at home, just like in the laboratory. This study sent questionnaires to 64 students, and 58 were returned. The results show that more than 80% of students believe that the remote labs for industrial robotics courses have improved the efficiency and quality of students' skills training as opposed to virtual simulation and watching videos on the computer.

"I was Confused by It; It was Confused by Me:" Exploring the Experiences of People with Visual Impairments around Mobile Service Robots

Mobile service robots have become increasingly ubiquitous. However, these robots can pose potential accessibility issues and safety concerns to people with visual impairments (PVI). We sought to explore the challenges faced by PVI around mainstream mobile service robots and identify their needs. Seventeen PVI were interviewed about their experiences with three emerging robots: vacuum robots, delivery robots, and drones. We comprehensively investigated PVI's robot experiences by considering their different roles around robots---direct users and bystanders. Our study highlighted participants' challenges and concerns about the accessibility, safety, and privacy issues around mobile service robots. We found that the lack of accessible feedback made it difficult for PVI to precisely control, locate, and track the status of the robots. Moreover, encountering mobile robots as bystanders confused and even scared the participants, presenting safety and privacy barriers. We further distilled design considerations for more accessible and safe robots for PVI.

HeterBot: A heterogeneous mobile manipulation robot for versatile operation

AbstractThis study presents the overall architecture of HeterBot, a heterogeneous mobile manipulation robot developed in our lab, which is designed for versatile operation in hazardous environments. The most significant feature of HeterBot is the heterogeneous design created by adopting the idea of ‘big arm + small arm’ and ‘big car + mini car’. This combination has the advantage of functional complementation, which achieves performance promotion in both locomotion and manipulation capabilities, making HeterBot distinguished from other mobile manipulation robots. Besides, multiple novel technologies are integrated into HeterBot to expand its versatility and ease of use, including Virtual Robot Experimentation Platform‐based teleoperation, reconfigurable end effectors, laser‐aided grasping, and manipulation with customised tools. Experimental results validate the effectiveness of HeterBot in various locomotion and manipulation tasks. HeterBot displays huge potential in future applications, such as searching and rescue.