Future Robots Research Articles

Could a robot feel pain?

AbstractQuestions about robots feeling pain are important because the experience of pain implies sentience and the ability to suffer. Pain is not the same as nociception, a reflex response to an aversive stimulus. The experience of pain in others has to be inferred. Danaher’s (Sci Eng Ethics 26(4):2023–2049, 2020. https://doi.org/10.1007/s11948-019-00119-x) ‘ethical behaviourist’ account claims that if a robot behaves in the same way as an animal that is recognised to have moral status, then its moral status should also be assumed. Similarly, under a precautionary approach (Sebo in Harvard Rev Philos 25:51–70, 2018. https://doi.org/10.5840/harvardreview20185913), entities from foetuses to plants and robots are given the benefit of the doubt and assumed to be sentient. However, there is a growing consensus about the scientific criteria used to indicate pain and the ability to suffer in animals (Birch in Anim Sentience, 2017. https://doi.org/10.51291/2377-7478.1200; Sneddon et al. in Anim Behav 97:201–212, 2014. https://doi.org/10.1016/j.anbehav.2014.09.007). These include the presence of a central nervous system, changed behaviour in response to pain, and the effects of analgesic pain relief. Few of these criteria are met by robots, and there are risks to assuming that they are sentient and capable of suffering pain. Since robots lack nervous systems and living bodies there is little reason to believe that future robots capable of feeling pain could (or should) be developed.

A Biomimetic Adhesive Disc for Robotic Adhesion Sliding Inspired by the Net-Winged Midge Larva.

Net-winged midge larvae (Blephariceridae) are known for their remarkable ability to adhere to and crawl on the slippery surfaces of rocks in fast-flowing and turbulent alpine streams, waterfalls, and rivers. This remarkable performance can be attributed to the larvae's powerful ventral suckers. In this article, we first develop a theoretical model of the piston-driven sucker that considers the lubricated state of the contact area. We then implement a piston-driven robotic sucker featuring a V-shaped notch to explore the adhesion-sliding mechanism. Each biomimetic larval sucker has the unique feature of an anterior-facing V-shaped notch on its soft disc rim; it slides along the shear direction while the entire disc surface maintains powerful adhesion on the benthic substrate, just like the biological counterpart. We found that this biomimetic sucker can reversibly transit between "high friction" (4.26 ± 0.34 kPa) and "low friction" (0.41 ± 0.02 kPa) states due to the piston movement, resulting in a frictional enhancement of up to 93.9%. We also elucidate the frictional anisotropy (forward/backward force ratio: 0.81) caused by the V-shaped notch. To demonstrate the robotic application of this adhesion-sliding mechanism, we designed an underwater crawling robot Adhesion Sliding Robot-1 (ASR-1) equipped with two biomimetic ventral suckers. This robot can successfully crawl on a variety of substrates such as curved surfaces, sidewalls, and overhangs and against turbulent water currents with a flow speed of 2.4 m/s. In addition, we implemented a fixed-wing aircraft Adhesion Sliding Robot-2 (ASR-2) featuring midge larva-inspired suckers, enabling transit from rapid water surface gliding to adhesion sliding in an aquatic environment. This adhesion-sliding mechanism inspired by net-winged midge larvae may pave the way for future robots with long-term observation, monitoring, and tracking capabilities in a wide variety of aerial and aquatic environments.

A survey of communicating robot learning during human-robot interaction

For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot’s internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot’s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that—when learning and communication are developed together—the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior.

인간과 로봇의 상호작용을 위한 로봇 UX디자인에 대한 고찰

The purpose of this paper is to conduct an in-depth review of robot user experience (UX) design to optimize the interaction between humans and robots. As a research method, the existing literature on robot UX design was reviewed, related theories and concepts were summarized, and various types of human-robot interaction were analyzed. By analyzing various robot UX design cases in various fields such as service, healthcare, and education, and suggesting the direction of future robot UX design with User Experience Design, the importance and possibility of robot UX design in the future society are presented. This study provides useful guidelines for scholars and developers interested in robot UX design and will provide important implications for future research and actual design process.

Stretchable Heat Transfer Eco-Materials: Mesogen Grafted NR-Based Nanocomposites with High Thermal Conductivity and Low Dielectric Constant.

Biomass-based functional polymers have received significant attention across various fields, in view of eco-friendly human society and sustainable growth. In this context, there are efforts to functionalize the biomass polymers for next-generation polymer materials. Here, stretchable heat transfer materials are focused on which are essential for stretchable electronics and future robotics. To achieve this goal, natural rubber (NR) is chemically modified with a thiol-terminated phenylnaphthalene (TTP), and then utilized as a thermally conductive NR (TCNR) matrix. Hexagonal boron nitride (h-BN), renowned for its high thermal conductivity and low electrical conductivity, is incorporated as a filler to develop stretchable heat transfer eco-materials. The optimized TCNR/h-BN composite elongates to 140% due to great elasticity of NR, and exhibits excellent dielectric properties (a low dielectric constant of 2.26 and a low dielectric loss of 0.006). Furthermore, synergetic phonon transfer of phenylnaphthalene crystallites and h-BN particles in the composite results in a high thermal conductivity of 0.87Wm-1K-1. The outstanding thermal, mechanical, and dielectric properties of the newly developed TCNR/h-BN composite enable the successful demonstration as stretchable and shape-adaptable thermal management materials.

Zero-Biased Bionic Fingertip E-Skin with Multimodal Tactile Perception and Artificial Intelligence for Augmented Touch Awareness.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80°C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Enhancing construction management outcomes through the mitigation of robotics implementation barriers: A sustainable practice model

Incorporating technology into construction projects is highly important to achieve success. One of the critical technologies is robotics, but it faces significant barriers in terms of implementing it in the construction sector. Existing research focuses on the benefits of robotics in terms of construction management. Besides this, there is limited known study that simultaneously focuses on overcoming the barriers and their impact on success in construction projects. This research was aimed at evaluating the barriers faced in the implementation of robotics in construction and determining their impact on success. A quantitative research design was followed, starting with the identification of possible challenges from the literature, performing the pilot survey, conducting the exploratory factor analysis (EFA), and performing structural equation modeling (SEM). The results produced a structural model involving barriers and their relation to construction success with a significant positive relation value of B = 0.87. Positive implications are formulated in relation to managerial and theoretical expectations of future robotics implementation in construction projects.

The Future of Artificial Intelligence in Surgery.

Until recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon's perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon's perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon's cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.

Effect of Different Weight on the Movable PZT Device on the Damage Detection Performance of Electromechanical Impedance Technique

This study presents a novel approach to conducting the electromechanical impedance (EMI) technique for delamination detection in composite structures without the need for permanently attaching PZT (Lead Zirconate Titanate) transducers to the surface. Instead, a device is created that can be simply placed on top of the composite structure, enabling one to perform the EMI technique for detecting damage. The primary objective is to investigate the effectiveness of this device in detecting delamination within composite materials. Additionally, this study explores the impact of placing additional weight on top of the transducer to investigate the performance of the device subjected to higher pressure. Experimental results and analysis will be presented to evaluate the feasibility and reliability of this approach for non-destructive testing and structural health monitoring of composite components. This research is significant as it lays the groundwork for developing automated damage detection systems using robotics in the near future. By demonstrating the proposed concept that can be easily integrated into robotic platforms, this study contributes to the advancement of automation in structural health monitoring. Implementing this technique in robotic systems has the potential to revolutionize maintenance practices by enabling continuous, real-time monitoring of composite structures, enhancing safety, and minimizing downtime due to structural defects. Moreover, the investigation into the impact of additional weight on the transducer’s performance is crucial for setting minimum weight limits in robotic systems, ensuring optimal functionality and accuracy during automated damage detection tasks.

Self-Amputating and Interfusing Machines.

Biological organisms exhibit phenomenal adaptation through morphology-shifting mechanisms including self-amputation, regeneration, and collective behavior. For example, reptiles, crustaceans, and insects amputate their own appendages in response to threats. Temporary fusion between individuals enables collective behaviors, such as in ants that temporarily fuse to build bridges. The concept of morphological editing often involves the addition and subtraction of mass and can be linked to modular robotics, wherein synthetic body morphology may be revised by rearranging parts. This work describes a reversible cohesive interface made of thermoplastic elastomer that allows for strong attachment and easy detachment of distributed soft robot modules without direct human handling. The reversible joint boasts a modulus similar to materials commonly used in soft robotics, and can thus be distributed throughout soft robot bodies without introducing mechanical incongruities. To demonstrate utility, the reversible joint is implemented in two embodiments: a soft quadruped robot that self-amputates a limb when stuck, and a cluster of three soft-crawling robots that fuse to cross a land gap. This work points toward future robots capable of radical shape-shifting via changes in mass through autotomy and interfusion, as well as highlights the crucial role that interfacial stiffness change plays in autotomizable biological and artificial systems.

Image-to-Image Translation-Based Deep Learning Application for Object Identification in Industrial Robot Systems

Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset.

Kinematic Modeling and Performance Analysis of a 5-DoF Robot for Welding Applications

Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated robots are popular due to their diverse applications, this research proposes a novel 5-DoF robot design for industrial automation, featuring a combination of three prismatic and two revolute (2R) joints, and analyzes its workspace. The proposed techno-economically efficient design offers control over the robot manipulator to achieve any reachable position and orientation within its workspace, replacing traditional 6-DoF robots. The kinematic model integrates both parallel and serial manipulator principles, combining a Cartesian mechanism with rotational mechanisms. Simulations demonstrate the end effector’s flexibility for tasks like welding, additive manufacturing, and material inspections, achieving the desired position and orientation. The research encompasses the design of linear and rotational actuators, kinematic modeling, Human–Machine Interface (HMI) development, and welding application integration. The developed robot demonstrates a superior performance and user-friendliness in welding. The experimental work validates the design’s optimized joint trajectories, efficient power usage, singularity avoidance, easy access in application areas, and reduced costs due to fewer actuators.

Multifunctional robot skin based on liquid metal composites and electrode arrays

While human skin regulates body temperature and protects the organism, robots also need multifunctional skin that can protect their internal electronics. Currently, most of the research on robotic skin focuses on the limbs with the aim of improving their sensory functions. There is a lack of a robotic skin with protective features that can be applied to the whole body. In some complex working environments, too high or too low temperatures can directly jeopardize the operation of the robot's internal circuitry. In addition, ubiquitous electromagnetic waves can damage the electronic system of robots. Therefore, an e-skin that protects the robot from external factors was desired. In this study, with reference to the structure of human skin, a gallium-based liquid metal and a bismuth-based low-melting-point alloy were combined to prepare a highly thermally conductive e-skin that can withstand both thermal shock and low temperature. This skin not only features thermal management, but also touch positioning and electromagnetic shielding. The development of multifunctional robot skin (MFR skin) will extend the service life of robots, broaden their application areas, and enable future robots to replace humans to accomplish more complex and operationally difficult tasks.

The aesthetics of robot design: towards a classification of morphologies

AbstractRobots are becoming increasingly prevalent in the workplace. As Industry 5.0 pursues human-centric technologies, a greater understanding of what effects aesthetics has on those interacting with robots is needed. This paper sets out robot morphology as a way to characterise key form types, and proposes seven classifications: anthropomorphism, zoomorphism, phytomorphism, artemorphism, functiomorphism, amorphism, and neomorphism. Through an assessment of the current robot aesthetic landscape, design dimensions are identified with examples that can inform future robot design.

Are robots stealing our jobs? Examining robot-phobia as a job stressor in the hospitality workplace

Purpose The purpose of this study is to explore robot-phobia as a source of occupational stress among hospitality employees in the context of increasing robotization in the industry. Design/methodology/approach The study sampled 321 lodging employees and 308 food service employees in the USA. An online panel company recruited the participants and administered an online survey. The study used various analytical methods, including structural modeling, t-tests and multi-group analyses. Findings The study results reveal that hospitality workers experience robot-phobia regardless of their sector or position. Robot-phobia causes job insecurity and stress, which increases turnover intention. These negative outcomes are more pronounced for those who interact more frequently with robots. Practical implications The study findings suggest that hospitality workers fear being replaced by robots in the near future. Therefore, hospitality organizations should offer adequate training and education on the advantages and drawbacks of robots and establish a supportive and collaborative work environment that values human–robot interaction. Originality/value This study offers new insights regarding human–robot interaction from the employee perspective by introducing the concept of robot-phobia in the hospitality workplace. A comprehensive picture of how hospitality employees confront the increasing presence of robots is provided in this study.

Promising directions for human-robot interactions defined by older adults.

Introduction: Older adults are engaging more and more with voice-based agent and social robot technologies, and roboticists are increasingly designing interactions for these systems with older adults in mind. Older adults are often not included in these design processes, yet there are many opportunities for older adults to collaborate with design teams to design future robot interactions and help guide directions for robot development. Methods: Through a year-long co-design project, we collaborated with 28 older adults to understand the key focus areas that older adults see promise in for older adult-robot interaction in their everyday lives and how they would like these interactions to be designed. This paper describes and explores the robot-interaction guidelines and future directions identified by older adults, specifically investigating the change and trajectory of these guidelines through the course of the co-design process from the initial interview to the design guideline generation session to the final interview. Results were analyzed through an adapted ethnographic decision tree modeling approach to understand older adults' decision making surrounding the various focus areas and guidelines for social robots. Results: Overall, over the course of the co-design process between the beginning and end, older adults developed a better understanding of the robot that translated to them being more certain of their attitudes of how they would like a robot to engage with them in their lives. Older adults were more accepting of transactional functions such as reminders and scheduling and less open to functions that would involve sharing sensitive information and tracking and/or monitoring of them, expressing concerns around surveillance. There was some promise in robot interactions for connecting with others, body signal monitoring, and emotional wellness, though older adults brought up concerns around autonomy, privacy, and naturalness of the interaction with a robot that need to be further explored. Discussion: This work provides guidance for future interaction development for robots that are being designed to interact with older adults and highlights areas that need to be further investigated with older adults to understand how best to design for user concerns.

Gesture recognition with a 2D low-resolution embedded camera to minimise intrusion in robot-led training of children with autism spectrum disorder

Growing evidence shows the potential benefits of robot-assisted therapy for children with Autism Spectrum Disorder (ASD). However, when developing new robotics technologies, it must be considered that this condition often causes increased anxiety in unfamiliar settings. Indeed, children with ASD have difficulties accepting changes like introducing multiple new technological devices in their routines, therefore, embedded solutions should be preferred. Also, in this context, robots should be small as children find the bigger ones scary. This leads to limited computing resources onboard as small batteries power them. This article presents a study on gesture recognition using video recorded only by the camera embedded in a NAO robot, while it was leading a clinical procedure. The video is 2D and low quality because of the limits of the NAO-embedded computing resources. The recognition is made more challenging by robot movements, which alter the vision by moving the camera and sometimes by obstructing it with the robot’s arms for short periods. Despite these challenging real-world conditions, in our experiments, we have tuned and improved state-of-the-art algorithms to yield an accuracy higher than 90%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$90\\%$$\\end{document} in the gesture classification, with the best accuracy being 94%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$94\\%$$\\end{document}. This level of accuracy is suitable for evaluating the children’s performance and providing information for the diagnosis and continuous assessment of the therapy. We have also considered the performance improvement of using a low-power GPU-AI accelerator embedded system, which could be included in future robots, to enable gesture analysis during the therapy, which could be adapted to the child’s performance.Graphical abstract

Biomimetic Electronic Skin for Robots Aiming at Superior Dynamic-Static Perception and Material Cognition Based on Triboelectric-Piezoresistive Effects.

Empowering robots with tactile perception and even thinking as well as judgment capabilities similar to those of humans is an inevitable path for the development of future robots. Here, we propose a biomimetic electronic skin (BES) that truly serves and applies to robots to achieve superior dynamic-static perception and material cognition functionalities. First, the microstructured triboelectric and piezoresistive layers are fabricated by a facile template method followed by selected self-polymerization treatment, enabling BES with high sensitivity and a wide detection range. Further, through laminated-independent triboelectric and piezoresistive parts for perceiving dynamic and static pressures simultaneously, the BES is capable of supporting the robot hand to monitor the entire process during object grasping. Most importantly, by further combining a neural network model, an intelligent cognition system is constructed for real-time cognition of the object material species via one touch of the robot hand under arbitrary pressures, which goes beyond the human cognition ability.

Investigation of Artificial Intelligence Algorithms in Robot Object Recognition Systems Under the Background of Big Data

In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.

Results of an exploratory survey within ESTS membership in 2022 on current trend of robotic-assisted thoracic surgery and its training perspectives.

Robotic-assisted thoracic surgery (RATS) is increasingly used in our specialty. We surveyed European Society of Thoracic Surgeons membership with the objective to determine current status of robotic thoracic surgery practice including training perspectives. A survey of 17 questions was rolled out with 1 surgeon per unit responses considered as acceptable. A total of 174 responses were obtained; 56% (97) were board-certified thoracic surgeons; 28% (49) were unit heads. Most responses came from Italy (20); 22% (38) had no robot in their institutions, 31% (54) had limited access and only 17% (30) had full access including proctoring. Da Vinci Xi was the commonest system in 56% (96) centres, 25% (41) of them had dual console in all systems, whereas RATS simulator was available only in half (51.18% or 87). Video-assisted thoracic surgery (VATS) was the most commonly adopted surgical approach in 81% of centres (139), followed by thoracotomy in 67% (115) and RATS in 36% (62); 39% spent their training time on robotic simulator for training, 51% on robotic wet/dry lab, which being no significantly different to 46-59% who had training on VATS platform. There was indeed huge overlap between simulator models or varieties usage; 52% (90) reported of robotic surgery not a part of training curriculum with no plans to introduce it in future. Overall, 51.5% (89) responded of VATS experience being helpful in robotic training in view of familiarity with minimally invasive surgery anatomical views and dissection; 71% (124) reported that future thoracic surgeons should be proficient in both VATS and RATS. Half of the respondents found no difference in earlier chest drain removal with either approach (90), 35% (60) reported no difference in postoperative pain and 49% (84) found no difference in hospital stay; 52% (90) observed better lymph node harvest by RATS. Survey concluded on a positive response with at least 71% (123) surgeons recommending to adopt robotics in future.