Browse Topic: Robotics

Items (2,036)
The automation of labor-intensive picking and planting operations is having an immediate impact in the agricultural indutry. In its simplest form, robotic automation can reduce the labor and soil disturbance while enabling organic soil cover and increasing species diversification through precision approaches to planting, weeding, and spraying. With this, pesticides and fertilizers can be applied in a more targeted way, and with machinery visiting fields more frequently, earlier and more targeted intervention can occur before pests become established. Small, Mobile, and Autonomous Agricultural Robots identifies issues that need to be resolved fo for this technology to thrive, including improving methods of acquiring and labeling training data to facilitate more accurate models for specific applications. It also discusses concepts such as general-purpose mechanical platforms for use as carriers of agricultural automation systems with high stability, positional accuracy, and variable
Muelaner, Jody E.
Trajectory planning is a major challenge in robotics and autonomous vehicles, ensuring both efficient and safe navigation. The primary objective of this work is to generate an optimal trajectory connecting a starting point to a destination while meeting specific requirements, such as minimizing travel distance and adhering to the vehicle’s kinematic and dynamic constraints. The developed algorithms for trajectory design, defined as a sequence of arcs and straight segments, offer a significant advantage due to their low computational complexity, making them well-suited for real-time applications in autonomous navigation. The proposed trajectory model serves as a benchmark for comparing actual vehicle paths in trajectory control studies. Simulation results demonstrate the robustness of the proposed method across various scenarios.
Soundouss, HalimaMsaaf, MohammedBelmajdoub, Fouad
Swimming robots play a crucial role in mapping pollution, studying aquatic ecosystems, and monitoring water quality in sensitive areas such as coral reefs or lake shores. However, many devices rely on noisy propellers, which can disturb or harm wildlife. The natural clutter in these environments — including plants, animals, and debris — also poses a challenge to robotic swimmers.
It’s a game a lot of us played as children — and maybe even later in life: unspooling measuring tape to see how far it would extend before bending. But to engineers at the University of California San Diego, this game was an inspiration, suggesting that measuring tape could become a great material for a robotic gripper.
Repartly, a startup based in Guetersloh, Germany, is using ABB’s collaborative robots to repair and refurbish electronic circuit boards in household appliances. Three GoFa cobots handle the sorting, visual inspection and precise soldering tasks enabling the company to enhance efficiency and maintain high quality standards.
Low-cost jelly-like materials, developed by researchers at the University of Cambridge, can sense strain, temperature, and humidity. And unlike earlier self-healing robots, they can also partially repair themselves at room temperature.
Innovators at NASA Johnson Space Center have developed a robotic system whose primary structural platform, or “orb,” can be injected into a pipe network and perform reconnaissance of piping infrastructure and other interior volumes. When deployed, this technology uses throttled fluid flow from a companion device for passive propulsion. A tethered line facilitates directional control by the orb’s operator, allowing it to navigate through various piping configurations, including 90° junctions.
Engineers have designed robots that crawl, swim, fly, and even slither like a snake, but no robot can hold a candle to a squirrel, which can parkour through a thicket of branches, leap across perilous gaps and execute pinpoint landings on the flimsiest of branches.
For the team at SmartCap, building top-notch gear for outdoor adventurers isn’t just a business — it’s a passion driven by their own love for the wild. But as demand for their rugged, modular truck caps soared after their move to North America in 2022, they hit a snag: How do you ramp up production without sacrificing the meticulous quality you are known for, all while navigating a tough labor market? Their answer? A bold step into the world of intelligent automation, teaming up with GrayMatter Robotics, and employing the company’s innovative Scan&Sand™ system.
Researchers have developed a tiny magnetic robot that can take 3D scans from deep within the body and could revolutionize early cancer detection.
A team of UCLA engineers and their colleagues have developed a new design strategy and 3D printing technique to build robots in one single step. The breakthrough enabled the entire mechanical and electronic systems needed to operate a robot to be manufactured all at once by a new type of 3D printing process for engineered active materials with multiple functions (also known as metamaterials). Once 3D printed, a “meta-bot” will be capable of propulsion, movement, sensing, and decision-making.
When we last heard from MELD Manufacturing, the large-scale 3D printer supplier was taking first place in the Robotics/Automation/Manufacturing category at the 2018 .
Industries that require high-accuracy automation in the creation of high-mix/low-volume parts, such as aerospace, often face cost constraints with traditional robotics and machine tools due to the need for many pre-programmed tool paths, dedicated part fixtures, and rigid production flow. This paper presents a new machine learning (ML) based vision mapping and planning technique, created to enhance flexibility and efficiency in robotic operations, while reducing overall costs. The system is capable of mapping discrete process targets in the robot work envelope that the ML algorithms have been trained to identify, without requiring knowledge of the overall assembly. Using a 2D camera, images are taken from multiple robot positions across the work area and are used in the ML algorithm to detect, identify, and predict the 6D pose of each target. The algorithm uses the poses and target identifications to automatically develop a part program with efficient tool paths, including
Langan, DanielHall, MichaelGoldberg, EmilySchrandt, Sasha
With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for
Bao, ZhibinLang, HaoxiangLin, Xianke
Test procedures such as EuroNCAP, NHTSA’s FMVSS 127, and UNECE 152 all require specific pedestrian to vehicle overlaps. These overlap variations allow the vehicle differing amounts of time to respond to the pedestrian’s presence. In this work, a compensation algorithm was developed to be used with the STRIDE robot for Pedestrian Automatic Emergency Braking tests. The compensation algorithm uses information about the robot and vehicle speeds and positions determine whether the robot needs to move faster or slower in order to properly overlap the vehicle. In addition to presenting the algorithm, tests were performed which demonstrate the function of the compensation algorithm. These tests include repeatability, overlap testing, vehicle speed variation, and abort logic tests. For these tests of the robot involving vehicle data, a method of replaying vehicle data via UDP was used to provide the same vehicle stimulus to the robot during every trial without a robotic driver in the vehicle.
Bartholomew, MeredithNguyen, AnHelber, NicholasHeydinger, Gary
Towards the goal of real-time navigation of autonomous robots, the Iterative Closest Point (ICP) based LiDAR odometry methods are a favorable class of Simultaneous Localization and Mapping (SLAM) algorithms for their robustness under any light conditions. However, even with the recent methods, the traditional SLAM challenges persist, where odometry drifts under adversarial conditions such as featureless or dynamic environments, as well as high motion of the robots. In this paper, we present a motion-aware continuous-time LiDAR-inertial SLAM framework. We introduce an efficient EKF-ICP sensor fusion solution by loosely coupling poses from the continuous time ICP and IMU data, designed to improve convergence speed and robustness over existing methods while incorporating a sophisticated motion constraint to maintain accurate localization during rapid motion changes. Our framework is evaluated on the KITTI datasets and artificially motion-induced dataset sequences, demonstrating
Kokenoz, CigdemShaik, ToukheerSharma, AbhishekPisu, PierluigiLi, Bing
While numerous advancements have been made in autonomous navigation for structured indoor and outdoor environments, these solutions often do not generalize well to off-road settings. There are unique challenges in such settings such as unreliable GPS, limited computational and memory resources, and sparse environmental features, making navigation particularly difficult. In our work, we propose a novel data structure called Hierarchical Dynamic Scene Graphs (HDSG) to address these challenges. HDSG captures environmental information at different resolutions, integrating both geometric and semantic features. It enables various navigation tasks such as localization, loop closure, and human interaction through the visualization of environmental features for remote operators. We evaluated the performance of localizing a robot’s position within the world frame by comparing compact spatial descriptors extracted from semi-consecutive scene graphs, derived from 3D LiDAR point clouds. Compared to
Alam, Fardifa FathmiulLuricich, FedericoLi, NianyiJia, YunyiLi, Bing
The unicycle self-balancing mobility system offers superior maneuverability and flexibility due to its unique single-wheel grounding feature, which allows it to autonomously perform exploration and delivery tasks in narrow and rough terrains. In this paper, a unicycle self-balancing robot traveling on the lunar terrain is proposed for autonomous exploration on the lunar surface. First, a multi-body dynamics model of the robot is derived based on quasi-Hamilton equations. A three-dimensional terramechancis model is used to describe the interaction between the robot wheels and the lunar soil. To achieve stable control of the robot's attitude, series PID controllers are used for pitch and roll attitude self-balancing control as well as velocity control. The whole robot model and control strategy were built in MATLAB and the robot's traveling stability was analyzed on the lunar terrain.
Shi, JunweiZhang, KaidiDuan, YupengWu, JinglaiZhang, Yunqing
Accurate object pose estimation refers to the ability of a robot to determine both the position and orientation of an object. It is essential for robotics, especially in pick-and-place tasks, which are crucial in industries such as manufacturing and logistics. As robots are increasingly tasked with complex operations, their ability to precisely determine the six degrees of freedom (6D pose) of objects, position, and orientation, becomes critical. This ability ensures that robots can interact with objects in a reliable and safe manner. However, despite advancements in deep learning, the performance of 6D pose estimation algorithms largely depends on the quality of the data they are trained on.
Researchers at Universidad Carlos III de Madrid (UC3M) have developed a new soft joint model for robots with an asymmetrical triangular structure and an extremely thin central column. This breakthrough, recently patented, allows for versatility of movement, adaptability and safety, and will have a major impact in the field of robotics.
Drone show accidents highlight the challenges of maintaining safety in what engineers call “multiagent systems” — systems of multiple coordinated, collaborative, and computer-programmed agents, such as robots, drones, and self-driving cars.
Over the decades, robotics deployments have been driven by the rapid in-parallel research advances in sensing, actuation, simulation, algorithmic control, communication, and high-performance computing among others. Collectively, their integration within a cyber-physical-systems framework has supercharged the increasingly complex realization of the real-time ‘sense-think-act’ robotics paradigm. Successful functioning of modern-day robots relies on seamless integration of increasingly complex systems (coming together at the component-, subsystem-, system- and system-of-system levels) as well as their systematic treatment throughout the life-cycle (from cradle to grave). As a consequence, ‘dependency management’ between the physical/algorithmic inter-dependencies of the multiple system elements is crucial for enabling synergistic (or managing adversarial) outcomes. Furthermore, the steep learning curve for customizing the technology for platform specific deployment discourages domain
Varpe, Harshal BabsahebColeman, JohnSalvi, AmeyaSmereka, JonathonBrudnak, MarkGorsich, DavidKrovi, Venkat N
Several challenges remain in deploying Machine Learning (ML) into safety critical applications. We introduce a safe machine learning approach tailored for safety-critical industries including automotive, autonomous vehicles, defense and security, healthcare, pharmaceuticals, manufacturing and industrial robotics, warehouse distribution, and aerospace. Aiming to fill a perceived gap within Artificial Intelligence and ML standards, the described approach integrates ML best practices with the proven Process Failure Mode & Effects Analysis (PFMEA) approach to create a robust ML pipeline. The solution views ML development holistically as a value-add, feedback process rather than the resulting model itself. By applying PFMEA, the approach systematically identifies, prioritizes, and mitigates risks throughout the ML development pipeline. The paper outlines each step of a typical pipeline, highlighting potential failure points and tailoring known best practices to minimize identified risks. As
Schmitt, PaulSeifert, Heinz BodoBijelic, MarioPennar, KrzysztofLopez, JerryHeide, Felix
Los Angeles-based plastics contract manufacturer Kal Plastics deployed UR10e trimming cobot for a fraction of the cost and lead time of a CNC machine, cut trimming time nearly in half, and reduced late shipments to under one percent — all while improving employee safety and growth opportunities.
Advances in artificial intelligence (AI), machine learning (ML), and sensor fusion drive robotics functionality across many applications, including healthcare. Ongoing innovations in high-speed connectivity, edge computing, network redundancy, and fail-safe procedures crucial to optimizing robotics opportunities. The emergence of natural language processing and emotional AI functionality are poised to propel more intuitive, responsive, and adaptive human-machine interaction.
A team of engineers is on a mission to redefine mobility by providing innovative wearable solutions to physical therapists, orthotic and prosthetic professionals, and individuals experiencing walking impairment and disability. Co-founded by Ray Browning and Zach Lerner, Portland-based startup Biomotum, aims “to empower mobility by energizing every step” through their wearable robotics technology.
In creating a pair of new robots, Cornell researchers cultivated an unlikely component: fungal mycelia. By harnessing mycelia’s innate electrical signals, the researchers discovered a new way of controlling “biohybrid” robots that can potentially react to their environment better than their purely synthetic counterparts.
Researchers from the School of Engineering of the Hong Kong University of Science and Technology (HKUST) have successfully developed what they believe is the world’s smallest multifunctional biomedical robots. Capable of imaging, high-precision motion, and multifunctional operations like sampling, drug delivery, and laser ablation, the robot offers competitive imaging performance and a tenfold improvement in obstacle detection, paving the way for robotic applications in narrow and challenging channels of the human body, such as the lung’s end bronchi and the oviducts.
Researchers are developing soft sensor materials based on ceramics. Such sensors can feel temperature, strain, pressure, or humidity, for instance, which makes them interesting for use in medicine, but also in the field of soft robotics.
Robotics researchers have already made great strides in developing sensors that can perceive changes in position, pressure, and temperature — all of which are important for technologies like wearable devices and human-robot interfaces. But a hallmark of human perception is the ability to sense multiple stimuli at once, and this is something that robotics has struggled to achieve.
Researchers have developed a multifunctional sensor based on semiconductor fibers that emulates the five human senses. Prof. Bonghoon Kim, department of robotics and mechatronics engineering of Daegu Gyeongbuk Institute of Science & Technology (DGIST), conducted the study in collaboration with Prof. Sangwook Kim at KAIST, Prof. Janghwan Kim at Ajou University, and Prof. Jiwoong Kim at Soongsil University. The technology developed in the study is expected to be utilized in fields such as wearables, Internet of Things (IoT), electronic devices, and soft robotics.
Insect cyborgs may sound like science fiction, but it’s a relatively new phenomenon based on using electrical stimuli to control the movement of insects. These hybrid insect computer robots, as they are scientifically called, herald the future of small, high mobile, and efficient devices.
Soft-bending actuators are gaining considerable attention in robotics for handling delicate objects and adapting to complex shapes, making them ideal for biomimetic robots. Soft pneumatic actuators (SPAs) are preferred in soft robotics because to their safety and compliance characteristics. Using negative pressure for actuation, it enhances stability by reducing the risk of sudden or unintended movements, crucial for delicate handling and consistent performance. Negative pressure actuation is more energy-efficient, safe and are less prone to leakage, increasing reliability and durability. This paper involves development of a new soft pneumatic actuator design by comparing various designs and to determine its performance parameters. This paper depicts on designing, and fabricating flexible soft pneumatic actuators working under negative pressure for soft robotic applications. The material used for fabrication was liquid silicone rubber and uniaxial tensile tests were conducted to
Warriar J S, SreejithSadique, AnwarGeorge, Boby
Soft-bending actuators have garnered significant interest in robotics and biomedical engineering due to their ability to mimic the bending motions of natural organisms. Using either positive or negative pressure, most soft pneumatic actuators for bending actuation have modified their design accordingly. In this study, we propose a novel soft bending actuator that utilizes combined positive and negative pressures to achieve enhanced performance and control. The actuator consists of a flexible elastomeric chamber divided into two compartments: a positive pressure chamber and a negative pressure chamber. Controlled bending motion can be achieved by selectively applying positive and negative pressures to the respective chambers. The combined positive and negative pressure allowed for faster response times and increased flexibility compared to traditional soft actuators. Because of its adaptability, controllability, and improved performance can be used for various jobs that call for careful
Lalson, AbiramiSadique, Anwar
A fast and agile robotic insect developed by MIT could someday aid in mechanical pollination.
A team led by Emily Davidson has reported that they used a class of widely available polymers called thermoplastic elastomers to create soft 3D printed structures with tunable stiffness. Engineers can design the print path used by the 3D printer to program the plastic’s physical properties so that a device can stretch and flex repeatedly in one direction while remaining rigid in another. Davidson, an assistant professor of chemical and biological engineering, says this approach to engineering soft architected materials could have many uses, such as soft robots, medical devices and prosthetics, strong lightweight helmets, and custom high-performance shoe soles.
Soft skin coverings and touch sensors have emerged as a promising feature for robots that are both safer and more intuitive for human interaction, but they are expensive and difficult to make. A recent study demonstrates that soft skin pads doubling as sensors made from thermoplastic urethane can be efficiently manufactured using 3D printers.
Researchers have helped create a new 3D printing approach for shape-changing materials that are likened to muscles, opening the door for improved applications in robotics as well as biomedical and energy devices.
The permanent magnet synchronous motor (PMSM) has become the preferred driving technology in robotic control engineering due to its high-power density and excellent dynamic response capability. However, traditional vector control strategies, while widely used, reveal certain limitations due to their reliance on high-precision sensors and the complex coordinate transformation calculations. These limitations affect the performance of robots in high-speed environments. This paper proposes a decoupling design for the PMSM current loop based on Internal model control (IMC), aiming to improve control accuracy and response speed by simplifying the control algorithm. This new strategy not only maintains the basic framework of vector control but also enhances the dynamic performance of the system through effective decoupling. Simulations conducted using Simulink demonstrate that this strategy significantly improves system stability and dynamic response speed, achieving more precise and rapid
Chen, HaoHuan, DiGong, ChaoLiu, Chenliang
Nowadays, there are many technologies emerging like firefighting robots, quadcopters, and drones which are capable of operating in hazardous disaster scenarios. In recent years, fire emergencies have become an increasingly serious problem, leading to hundreds of deaths, thousands of injuries, and the destruction of property worth millions of dollars. According to the National Crime Records Bureau (NCRB), India recorded approximately 1,218 fire incidents resulting in 1,694 deaths in 2020 alone. Globally, the World Health Organization (WHO) estimates that fires account for around 265,000 deaths each year, with the majority occurring in low- and middle-income countries. The existing fire-extinguishing systems are often inefficient and lack proper testing, causing significant delays in firefighting efforts. These delays become even more critical in situations involving high-rise buildings or bushfires, where reaching the affected areas is particularly challenging. The leading causes of
Karthikeyan, S.Nithish, U.Sanjay, S.Sibiraj, T.Vishnu, J.
Need a moment of levity? Try watching videos of astronauts falling on the Moon. NASA’s outtakes of Apollo astronauts tripping and stumbling as they bounce in slow motion are delightfully relatable. For MIT engineers, the lunar bloopers also highlight an opportunity to innovate.
Researchers have developed a fully embedded wireless brain neural signal recorder. The device was created by Prof. Jang Kyung-in of the department of robotics and mechanical electronics at DGIST in collaboration with a research team led by Lee Young-jeon of the Korea Research Institute of Bioscience & Biotechnology.
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