Field Of Robotics Research Articles

Dynamic analysis of six-bar tensegrity-based robot

Abstract The tensegrity-based robot is a hot research topic in the research field of robotics. The dynamics of the robot, describing the specific motion patterns under the influence of forces and torques, play an important role in the application of robotics. In addition, the dynamics should be considered when revealing the internal mechanisms of robots and predicting their dynamic behaviors. In this work, a dynamic model for a rolling six-bar tensegrity-based robot is established. In terms of kinematic representation, quaternions are chosen as the tool for attitude description, simplifying the derivation process of the complex three-dimensional rotational kinematics calculations. Quaternions play a crucial role in mathematical processing for robot attitude control and path planning due to their effective representation of three-dimensional space rotations. The classic Newton-Euler dynamic framework was adopted to conduct in-depth and detailed studies on the dynamic characteristics of the robot under various force conditions and motion states, with a particular focus on rolling dynamic analysis. Special attention was paid to the dynamic response mechanisms of each component of the robot under the combined action of internal and external forces and torques.

High-throughput Identification of Fusarium Head Blight Resistance in Wheat Varieties Using Field Robot-Assisted Imaging and Deep Learning Techniques

Fusarium head blight (FHB) has attracted much attention in food science and agriculture for its threat to wheat yields and food safety, due to the production of mycotoxins like deoxynivalenol (DON). Breeding of wheat varieties with improved FHB resistance is essential for controlling this disease. However, identifying resistance in different materials during variety selection remains time-consuming and labor-intensive. Therefore, this paper proposed a high-throughput method for the evaluation of FHB disease symptoms. It enabled the semiautomatic acquisition of images of individual wheat ears in a field environment with the aid of a field robot. The images obtained were semantically segmented to get a single wheat ear, from which the infected spikelets (ISs) were extracted, and then the disease degree was calculated. The results showed that the accuracy value of the individual wheat ear using DeepLabV3+ reached 0.996. The accuracy value of ISs was over 0.98. The mean Accuracy of this method for identifying the resistance of varieties was 0.967, and the precision of a single severity grade reached 0.980. The results indicate that the proposed method enables the acquisition and extraction of disease phenotype and rapid identification of resistance to FHB. The study also provides a reference for accurately identifying phenotypes of wheat ears in other field environments and for selecting and breeding other fungal-toxin-resistant varieties.

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Bioinspired soft robotics is an emerging field that aims to develop flexible and adaptive robots inspired by the movement and capabilities of biological organisms. This review article examines recent advances in materials, actuation mechanisms, sensors, and control strategies and discusses the challenges and future prospects of bioinspired soft robotics. Key innovations highlighted include pneumatic, elastomer actuators, variable‐length shape memory alloy tendons, closed‐loop control with soft sensors, and the incorporation of soft materials including shape memory polymers and conductive composites. Challenges in soft robotics such as achieving complex motion control, incorporating feedback systems, modeling soft material dynamics, and replicating biological muscle efficiency with artificial muscles are also discussed. Promising future directions are explored including the integration of biodegradable materials, machine learning‐based control algorithms, and leveraging data‐driven techniques for modeling and control. Building on progress in multi‐functional materials, manufacturing techniques, and bioinspired design principles, soft robots hold considerable promise for expanding robot capabilities, enhancing versatility and adaptability, enabling applications from wearable assistive devices to search and rescue operations. This review provides a holistic perspective encompassing key drivers propelling innovations in the vibrant field of bioinspired soft robotics.

Multi-scale and multi-receptive field-based feature fusion for robust segmentation of plant disease and fruit using agricultural images

The accurate and fast assessment of plant diseases and fruits is important for sustainable and productive agriculture. However, typically manual methods are adopted for the assessment of plant diseases and fruit, which is a time-consuming, resource-intensive, and error-prone approach. A few artificial intelligence (AI)-based methods have been introduced to automate this process, but they show limitations in attaining high performance in challenging imaging conditions such as occlusions, poor lighting, noise, indistinctive boundaries, and excessive morphological variations. Moreover, current approaches also lack in providing computationally efficient solutions. To overcome these issues, two novel architectures are developed to segment plant diseases and fruits with higher segmentation performance and less computational requirements. The efficient feature fusion segmentation network (EFFS-Net) is the base network, and a multi-scale dilated feature fusion segmentation network (MDFS-Net) is the final network of this study. EFFS-Net uses an identity skip path-based feature fusion mechanism with an efficient grouped convolutional depth (EGCD) to provide satisfactory segmentation performance with high computational efficiency. MDFS-Net uses a multi-scale feature fusion mechanism and fuses low-level information in the EGCD and other sections of the network to learn detailed input features for delivering promising performance even in challenging imaging conditions. MDFS-Net also applies multiple receptive fields to the low-level information in the effective receptive field processing (ERFP) block for fusion near the pixel classification stage for further performance enhancement.Both networks are evaluated using a Brazilian Arabica coffee leaf (BRACOL) image dataset, an Australian Center for Field Robotics orchard fruit (apple) dataset, and a necrotized cassava root cross-section image dataset. The proposed method provides a promising segmentation performance, achieving dice similarity coefficients of 88.81 %, 95.01 %, and 86.04 % with performance improvement of 3.5 %, 2.6 %, and 1.07 % on the three datasets, respectively, with approximately ten times less number of required trainable parameters compared with the state-of-the-art methods.

ROS Gateway: Enhancing ROS Availability across Multiple Network Environments.

As the adoption of large-scale model-based AI grows, the field of robotics is undergoing significant changes. The emergence of cloud robotics, where advanced tasks are offloaded to fog or cloud servers, is gaining attention. However, the widely used Robot Operating System (ROS) does not support communication between robot software across different networks. This paper introduces ROS Gateway, a middleware designed to improve the usability and extend the communication range of ROS in multi-network environments, which is important for processing sensor data in cloud robotics. We detail its structure, protocols, and algorithms, highlighting improvements over traditional ROS configurations. The ROS Gateway efficiently handles high-volume data from advanced sensors such as depth cameras and LiDAR, ensuring reliable transmission. Based on the rosbridge protocol and implemented in Python 3, ROS Gateway is compatible with rosbridge-based tools and runs on both x86 and ARM-based Linux environments. Our experiments show that the ROS Gateway significantly improves performance metrics such as topic rate and delay compared to standard ROS setups. We also provide predictive formulas for topic receive rates to guide the design and deployment of robotic applications using ROS Gateway, supporting performance estimation and system optimization. These enhancements are essential for developing responsive and intelligent robotic systems in dynamic environments.

Perspectives on Intelligence in Soft Robotics

Engineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real‐world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so‐called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real‐world environment.

Increasing Horizontal Controlled Force Delivery Capabilities of Aerial Manipulators by Leveraging the Environment

Delivering large horizontal controlled forces for long periods with aerial manipulators is not an easy task to accomplish; several factors including disturbances, reaction forces from the on-board arm or actuator’s limitations might diminish their horizontal force delivery capabilities. Aiming to mitigate these drawbacks, this paper presents a comprehensive study of the force delivery capabilities of aerial manipulators leveraging the environment. The methodology to evaluate the aforementioned capabilities is the Generalized-Jacobian-based force ellipsoid, which was applied to different types of both free-flying and attached-to-the-environment aerial manipulators. In addition, large controlled force delivery experiments with the mentioned manipulators were conducted on physics-engine-based robotics simulation software. The obtained results categorically demonstrate that aerial manipulators leveraging the environment are capable of delivering larger forces than their free-flying counterparts, which has not been proved in the literature so far in the field of aerial robotics.

Research on Indoor Automatic Path Planning Algorithm for Robots

The field of robotics has significantly advanced, especially in indoor autonomous navigation, with applications in households, offices, factories, and tourism. This research focuses on developing and optimizing algorithms for indoor autonomous path planning, aiming to enhance robots' ability to navigate efficiently and safely in various indoor environments. The research adopts a comprehensive methodological approach, beginning with a literature review to understand current state-of-the-art techniques in path planning and obstacle avoidance. Novel algorithms were designed and implemented, focusing on efficiency and real-time performance. These algorithms were tested in simulation environments and validated on actual robotic platforms. Performance metrics such as path efficiency, computational time, and obstacle avoidance success rates were used to assess the algorithms. The study evaluated global path planning algorithms like A* and Dijkstra’s, and local path planning algorithms such as Rapidly-exploring Random Trees (RRT) and Dynamic Window Approach (DWA). A* and Dijkstra's algorithms proved effective for global planning but required optimization for real-time applications. RRT excelled in complex environments but needed improvements for path optimality. DWA provided robust real-time obstacle avoidance. Hybrid approaches combining these algorithms showed enhanced performance, balancing global and local planning strengths. Machine learning techniques further improved adaptability and efficiency. The integration of advanced sensor technologies and accurate map construction methods is crucial for effective indoor navigation. LIDAR, ultrasonic sensors, and depth cameras were key in providing precise environmental perception. Hybrid maps combining grid and topological approaches offered detailed local information and efficient long-distance planning. Optimization techniques like parameter tuning and algorithm fusion, along with machine learning, significantly enhanced algorithm performance. Future research should explore intelligent algorithms, multi-robot collaboration, and human-robot interaction to further advance the field.

Field-grown tomato yield estimation using point cloud segmentation with 3D shaping and RGB pictures from a field robot and digital single lens reflex cameras

The aim of this study was to estimate field-grown tomato yield (weight) and quantity of tomatoes using a self-developed robot and digital single lens reflex (DSLR) camera pictures. The authors suggest a new approach to predicting tomato yield that is based on images taken in the field, 3D scanning, and shape. Field pictures were used for tomato segmentation to determine the ripeness of the crop. A convolution neural network (CNN) model using TensorFlow library was devised for the segmentation of tomato berries along with a small robot, which had a 59.3 % F1 score. To enhance the accurate tomato crop model and to estimate the yield later, point cloud imaging was applied using a Ciclops 3D scanner. The best fitting sphere model was generated using the 3D model. The most optimal model was the 3D model, which gave the best representation and provided the weight of the tomatoes with a relative error of 21.90 % and a standard deviation of 17.9665 %. The results indicate a consistent object-based classification of the tomato crop above the plant/row level with an accuracy of 55.33 %, which is better than in-row sampling (images taken by the robot). By comparing the measured and estimated yield, the average difference for DSLR camera images was more favorable at 3.42 kg.

ACA-Net: adaptive context-aware network for basketball action recognition.

The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences.

Enhanced Particle Swarm Optimisation for Multi-Robot Path Planning with Bezier Curve Smoothing

This paper presents an Enhanced Particle Swarm Optimisation (EPSO) algorithm to improve multi-robot path planning by integrating a new path planning scheme with a cubic Bezier curve trajectory smoothing algorithm. Traditional PSO algorithms often result in suboptimal paths with numerous turns, necessitating frequent stops and higher energy consumption. The proposed EPSO algorithm addresses these issues by generating smoother paths that reduce the number of turns and enhance the efficiency of multi-robot systems. The proposed algorithm was evaluated through simulations in two scenarios, and its performance was compared against the basic PSO algorithm. The results demonstrated that EPSO consistently produced shorter, smoother paths with fewer directional changes, albeit with slightly longer execution times. This improvement translates to more efficient navigation, reduced energy consumption, and enhanced overall performance of multi-robot systems. The findings underscore the potential of EPSO in applications requiring precise and efficient path planning, highlighting its contribution to advancing the field of robotics.

Challenges in contemporary spinal robotics: encouraging spine surgeons to drive transformative changes in the development of future robotic platforms.

The field of spinal robotics has witnessed considerable advances, which have primarily focused on enhancing pedicle screw placement. This article critically evaluates the current direction of spinal robotics development, raising concerns about the disproportionate emphasis on pedicle screw placement when existing techniques already yield commendable results. Discussions on various parameters, including quality, cost-effectiveness, and accessibility, highlight the need for a broader perspective in the development of robotics for spinal surgery. Comparative analyses reveal that navigation systems offer cost-effective and time-efficient alternatives to robotics, with similar accuracy levels. Patient demand for robotic interventions is influenced by perceived superiority, warranting careful consideration of public sentiment. This article also underscores the need for future spine surgeons to maintain proficiency in traditional techniques. The influence of industry and key opinion leaders in steering the focus toward pedicle screw placement is discussed, emphasizing the need for a more holistic approach. Accessibility issues and legal considerations in the evolving field of spinal robotics are addressed, and the potential for robotics to enhance various aspects of surgical procedures beyond pedicle screw placement is explored. In conclusion, we advocate for a shift in focus in spinal robotics, emphasizing the untapped potential to streamline common surgical procedures (such as discectomy, laminectomy, and endoscopy), enhance precision, and improve patient outcomes in areas beyond pedicle screw placement. Future advances in spinal robotics have the potential to transform the surgical landscape, benefitting all stakeholders, including patients, surgeons, and hospitals.

Programming Hydrogen Bonds for Reversible Elastic‐Plastic Phase Transition in a Conductive Stretchable Hydrogel Actuator with Rapid Ultra‐High‐Density Energy Conversion and Multiple Sensory Properties

AbstractSmart hydrogels have recently garnered significant attention in the fields of actuators, human‐machine interaction, and soft robotics. However, when constructing large‐scale actuated systems, they usually exhibit limited actuation forces (≈2 kPa) and actuation speeds. Drawing inspiration from hairspring energy conversion mechanism, an elasticity‐plasticity‐controllable composite hydrogel (PCTA) with robust contraction capabilities is developed. By precisely manipulating intermolecular and intramolecular hydrogen‐bonding interactions, the material's elasticity and plasticity can be programmed to facilitate efficient energy storage and release. The proposed mechanism enables rapid generation of high contraction forces (900 kPa) at ultra‐high working densities (0.96 MJ m−3). Molecular dynamics simulations reveal that modifications in the number and nature of hydrogen bonds lead to a distinct elastic‐plastic transition in hydrogels. Furthermore, the conductive PCTA hydrogel exhibits multimodal sensing capabilities including stretchable strain sensing with a wide sensing range (1–200%), fast response time (180 ms), and excellent linearity of the output signal. Moreover, it demonstrates exceptional temperature and humidity sensing capabilities with high detection accuracy. The strong actuation power and real‐time sensory feedback from the composite hydrogels are expected to inspire novel flexible driving materials and intelligent sensing systems.

Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments

Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.

Review and analysis of software simulators for robotic information systems

The rapid development of educational technologies requires the active involvement of new means and effective teaching methods. One of the innovative tools of learning in the field of robotics is the use of robot simulators in the educational process, which open up many opportunities for better assimilation of knowledge and development of practical skills among students. Robot simulators are software complexes that allow you to visualize and simulate the behavior of real robots. With these tools, students can: program robots in a virtual environment, test code and debug without the risk of damaging real equipment; to study robotics and the principles of robot operation, exploring different types of sensors, actuators and algorithms of robot behavior; to solve problems in various fields, such as production automation, logistics, construction, etc.; practices your skills in a safe and controlled environment. Among the benefits of using robotic simulators are next: availability for a large number of students, as simulators are much cheaper than real jobs; safety, as the virtual environment eliminates the risks associated with working with real robots; flexibility that allows students to work at their own pace, explore different scenarios and experiment with different parameters; interactivity with a realistic 3D-environment and visualization. This work presents an overview and analysis of software that allows modeling robot control systems and simulating their operation for various algorithms. A review and analysis of the proposed software tools for simulating robotic systems, which are used to teach bachelor's and master's degree students in the specialties of "Computer Science", "Information Systems", "Industrial Mechanical Engineering" and "Applied Mechanics" and have proven their effectiveness and effectiveness in distance learning at the Kyiv National University of Construction and Architecture.

ForzaETH Race Stack—Scaled Autonomous Head‐to‐Head Racing on Fully Commercial Off‐the‐Shelf Hardware

ABSTRACTAutonomous racing in robotics combines high‐speed dynamics with the necessity for reliability and real‐time decision‐making. While such racing pushes software and hardware to their limits, many existing full‐system solutions necessitate complex, custom hardware and software, and usually focus on Time‐TrIals rather than full unrestricted Head‐to‐head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well‐resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head‐to‐Head autonomous racing competition, which simplifies replication by using commercial off‐the‐shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout, which is exemplified by the various modularly implemented state estimation and control systems. Capable of handling both Time‐Trials and Head‐to‐Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times. Furthermore, the stack demonstrated its reliability and performance at unprecedented scales, up to over on tracks up to 150 m in length.

Magnetic elastomer composites with tunable magnetization behaviors for flexible magnetic transducers

AbstractMagnetic elastomer composites are extending the field of soft robotics by integrating the flexibility of elastomers with the versatile performance of magnetic materials. In this work, a series of magnetic elastomers (BaCoxTixFe12–2xO19/PDMS) with tunable magnetic response have been successfully synthesized. In this composite, the magnetic component consists of Co‐Ti co‐doped barium hexaferrite (BaCoxTixFe12–2xO19, BCTF) nano powders, and the matrix is made of polydimethylsiloxane (PDMS). The coercivity (Hc) of BCTF can be reduced from 3950 Oe to 70 Oe, which bestow on the magnetic elastomer different magnetic response behavior. These magnetic elastomer composites obtained by blending have low Young's modulus, approximately 1.5 MPa. Furthermore, when applying the same external magnetic field, BaCo1.2Ti1.2Fe9.6O19/PDMS the magnetic deformation Angle can be as high as 60°. These magnetic elastomers exhibit different magnetic‐induced deformation owing to the adjustable permeability of the magnetic nanoparticles. This research significantly advances the design of applications with specific magnetic characteristics, such as sensors and transducers, thus opening new horizons for innovation in soft robotics and related fields.Highlights All elastomer composite exhibit a low modulus of approximately 1.5 MPa. These composites exhibit different magnetic response behaviors. This research provides a solution for the innovation of transducers.

Cross-National Comparison of Technology Innovation Capabilities in Automation and Robotics

In today’s dynamic and global economic environment, technological innovations are a key determinant of international competitiveness. In the field of automation and robotics, the development of modern technologies is of particular significance, influencing not only industrial progress but also social aspects. Automation and robotics play a crucial role in the concept of modern industry, contributing to the creation of more sustainable, flexible, and human-centric production systems. The article aims to conduct an in-depth analysis of the patent activity in automation and robotics in Europe. Patent documentation serves as a source of knowledge about the directions of research, inventive activities, and consequently, the innovative and competitive potential of the economy. The number of patents over time reflects the dynamics of a country’s technological development. The article discusses the use of patent databases to evaluate a country’s innovation in automation and robotics. The conducted analysis of data on European countries enabled the identification of trends based on the International Patent Classification and specialisation from a geographical perspective using revealed comparative technological advantage. The European countries were classified using cluster analysis, demonstrating the diversity, and identifying leaders in each group.

Multi-cameras calibration System Based Deep Learning Approach and Beyond: A Survey

The process of determining camera settings to deduce geometric attributes from recorded sequences is known as camera calibration. This process is essential in the fields of robotics and computer vision, encompassing both two-dimensional and three-dimensional applications. Traditional calibration methods, however, are time-consuming and require specific expertise. Recent endeavors have demonstrated that learning-based systems can replace the monotonous tasks associated with manual calibrations. Responses have been examined through a range of learning techniques, networks, geometric assumptions, and datasets. A thorough examination of camera calibration systems that rely on learning algorithms is offered in this paper, assessing their advantages and disadvantages. The primary categories of calibration presented are the regular pinhole camera model, distortion camera model, cross-sensor model, and cross-view model. These categories align with current research trends and have diverse applications. As there is no existing standard in this field, a large dataset of calibration has been created, which can be used as a public platform to assess the effectiveness of current methods. This collection consists of both artificially generated and genuine data, including images and videos obtained from various cameras in different locations. The difficulties faced will be analyzed, and alternative avenues for further research will be suggested in the next stage of this project. This survey represents the initial attempt to perform camera calibration using learning-based methods spanning a period of eight years. Our findings indicate that learning-based methods significantly reduce the time and expertise required for calibration while maintaining or improving accuracy compared to traditional methods. Specifically, our research demonstrates a calibration error reduction of up to 20% and speed improvements by a factor of three compared to traditional methods, as well as better adaptability to different camera types and environments.

German sugar beet farmers’ intention to use autonomous field robots for seeding and weeding

Robotic weed control is not yet widely adopted, despite its technological availability and proven economics and sustainability in crop cultivation by replacing seasonal labor and synthetic pesticides. This impedes technologically enabled changes toward more sustainable agricultural systems. Given that adopting robotics for the weeding process requires changing existing systems, farmers' appraisals for the new and the current weeding technology may constitute barriers. However, this dualism has been largely ignored by previous studies. Based on a duality approach, we investigate farmers’ beliefs, and adaptive and maladaptive appraisals of current and new robotic weeding in sugar beets. The main variable of interest is their behavioral intention to adopt weeding robots. For our sample of German farmers, we identify the main enablers perceived efficacy of the robots and social norms. The main barrier are maladaptive rewards from traditional weeding. We recommend policy incentives to promote large-scale uptake of new and more sustainable robotic technologies. To improve efficacy perceptions of such robotic systems public demonstrations/talks are mostly relevant. Maladaptive rewards can be reduced, for instance, by notifying about the dependency of the current practices on future availability of synthetic inputs or seasonal workers.