Browse Topic: Robotics

Items (1,994)
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
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
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
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
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
Through the method of on-site video observation, this study divides the intersection area into three parts according to the road traffic characteristics of the Y-shaped signalized intersections, and at the same time obtains the relevant parameters. These parameters include the left-turn speed and traffic density of motor vehicles within both the internal and exit areas, the frequency of lane-changing and queuing behaviors of non-motorized vehicles in the internal area, and the left-turn speed and traffic density of non-motorized vehicles in both the internal and exit areas. The data extraction and analysis of the parameters provide strong data support for further analysis of the subsequent mixed traffic flow. A cellular automaton model is developed using the intersection’s exit area as the scenario. The exit area is divided into three lanes based on the queuing patterns of mixed traffic. Corresponding traffic rules are established according to the traffic density of motorized and non
Yuan, LiLiu, Xiaowei
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
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
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.
This research, path planning optimization of the deep Q-network (DQN) algorithm is enhanced through integration with the enhanced deep Q-network (EDQN) for mobile robot (MR) navigation in specific scenarios. This approach involves multiple objectives, such as minimizing path distance, energy consumption, and obstacle avoidance. The proposed algorithm has been adapted to operate MRs in both 10 × 10 and 15 × 15 grid-mapped environments, accommodating both static and dynamic settings. The main objective of the algorithm is to determine the most efficient, optimized path to the target destination. A learning-based MR was utilized to experimentally validate the EDQN methodology, confirming its effectiveness. For robot trajectory tasks, this research demonstrates that the EDQN approach enables collision avoidance, optimizes path efficiency, and achieves practical applicability. Training episodes were implemented over 3000 iterations. In comparison to traditional algorithms such as A*, GA
Arumugam, VengatesanAlagumalai, VasudevanRajendran, Sundarakannan
LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs
Feroz Ali, L.Madhankumar, S.Hariush, V.C.Jahath Pranav, R.Jayadeep, J.Jeffrey, S.
Hemming is an incremental joining technique used in the automotive industry, it involves bending the flange of an outer panel over an inner panel to join two sheet metal panels. Different hemming methods are available such as Press die hemming, Table-top hemming and Robot roller hemming. Robot roller hemming is superior to press hemming and tabletop hemming because of its ability to hem complex-shaped parts and is typically used in low-volume automotive production lines. For higher production volumes, such as 120 Jobs per Hour (JPH), press hem or tabletop hem is generally preferred. However, to achieve high-volume production from roller hemming method multi station setup is used. This static multi station setup can be configured into a Turntable setup. This new method eliminates the robot load and unload time at each station in the existing setup, resulting in a 40% increase in hemming robot utilization. Therefore, this process reduces the number of robots and the required floor space
Raju, GokulRoy, AmlanSahu, ShishirPalavelathan, Gowtham RajJagadeesh, NagireddiChava, Seshadri
Researchers and engineers at the U.S. Army Combat Capabilities Development Command Chemical Biological Center have developed a prototype system for decontaminating military combat vehicles. U.S. Army Combat Capabilities Development Command, Aberdeen Proving Ground, MD The U.S. Army Combat Capabilities Development Command Chemical Biological Center (DEVCOM CBC) is paving the way and helping the Army transform into a multi-domain force through its modernization and priority research efforts that are linked to the National Defense Strategy and nation's goals. CBC continues to lead in the development of innovative defense technology, including autonomous chem-bio defense solutions designed to enhance accuracy and safety to the warfighter.
The future of wireless technology - from charging devices to boosting communication signals - relies on the antennas that transmit electromagnetic waves becoming increasingly versatile, durable and easy to manufacture. Researchers at Drexel University and the University of British Columbia believe kirigami, the ancient Japanese art of cutting and folding paper to create intricate three-dimensional designs, could provide a model for manufacturing the next generation of antennas. Recently published in the journal Nature Communications, research from the Drexel-UBC team showed how kirigami - a variation of origami - can transform a single sheet of acetate coated with conductive MXene ink into a flexible 3D microwave antenna whose transmission frequency can be adjusted simply by pulling or squeezing to slightly shift its shape.
Honda has long been at the cutting edge of mobility and tech, with everything from the Asimo robot of 20 years ago to plans for reusable rockets to launch lightweight satellites into orbit. During a Tech Day event in early October in Tochigi, Japan, the Japanese automaker announced further details of its upcoming Honda 0 architecture (Honda calls it “Honda Zero” but writes it with the number), its first in-house electric platform designed from the ground up. Honda also discussed some of the advanced manufacturing techniques it's pioneering to reach its core design and technology tenants.
Bassett, Abigail
Liebherr and Fortescue unveiled their first autonomous battery-electric T 264 haul truck at MINExpo 2024, garnering a steady stream of attendees eyeing and climbing on the giant machine. The truck is the culmination of nearly three years of development work and collaboration among the autonomy and zero-emission units of Liebherr and Fortescue. The T 264 electric hauler features a 3.2-MWh battery system, comprising eight sub-packs, developed by Fortescue Zero. Fortescue also developed a stationary fast-charging solution to support the new T 264. The charger will be available in both manual and robotic versions. An automated quick charger of up to 6 MW with two megawatt charging system (MCS) plugs can reportedly charge the current battery-electric T 264 in 30 minutes.
Gehm, Ryan
The Hospital for Sick Children/University of Toronto Toronto, ON, Canada
Researchers have successfully demonstrated the four-dimensional (4D) printing of shape memory polymers in submicron dimensions that are comparable to the wavelength of visible light. 4D printing enables 3D-printed structures to change their configurations over time and is used in a variety of fields such as soft robotics, flexible electronics, and medical devices.
Since the COVID-19 pandemic that advanced contactless service, robots are increasingly being seen conducting routine deliveries around hospitals and hotels. Developed by Robotise Technologies, JEEVES is one such autonomous service robot used in hotels, healthcare facilities, offices, airports, and other settings. Its main duty is to transport materials and products.
Brain-machine interfaces enable direct communication between a brain’s electrical activity and an external device such as a computer or a robotic limb that allows people to control machines using their thoughts. Researchers have developed a novel biohybrid neuroprosthetic research platform comprised of a dexterous artificial hand electrically interfaced with biological neural networks.
Imagine having to straighten up a messy kitchen, starting with a counter littered with sauce packets. If your goal is to wipe the counter clean, you might sweep up the packets as a group. If, however, you wanted to first pick out the mustard packets before throwing the rest away, you would sort more discriminately, by sauce type. MIT engineers have developed a method that enables robots to make similarly intuitive, task-relevant decisions.
Researchers led by Professor Young Min Song from the Gwangju Institute of Science and Technology (GIST) have unveiled a vision system inspired by feline eyes to enhance object detection in various lighting conditions. Featuring a unique shape and reflective surface, the system reduces glare in bright environments and boosts sensitivity in low-light scenarios. By filtering unnecessary details, this technology significantly improves the performance of single-lens cameras, representing a notable advancement in robotic vision capabilities.
Conventional robotic grippers struggle with the unique shapes, properties, and delicate nature of different crops. Consequently, there has been an increasing demand for more versatile robots that can adapt to objects with various shapes, sizes, and textures.
Penn Engineers have developed a new algorithm that allows robots to react to complex physical contact in real time, making it possible for autonomous robots to succeed at previously impossible tasks, like controlling the motion of a sliding object.
This work aims to define a novel integration of 6 DOF robots with an extrusion-based 3D printing framework that strengthens the possibility of implementing control and simulation of the system in multiple degrees of freedom. Polylactic acid (PLA) is used as an extrusion material for testing, which is a thermoplastic that is biodegradable and is derived from natural lactic acid found in corn, maize, and the like. To execute the proposed framework a virtual working station for the robot was created in RoboDK. RoboDK interprets G-code from the slicing (Slic3r) software. Further analysis and experiments were performed by FANUC 2000ia 165F Industrial Robot. Different tests were performed to check the dimensional accuracy of the parts (rectangle and cylindrical). When the robot operated at 20% of its maximum speed, a bulginess was observed in the cylindrical part, causing the radius to increase from 1 cm to 1.27 cm and resulting in a thickness variation of 0.27 cm at the bulginess location
Srivastava, KritiKumar, Yogesh
Scientists have developed an innovative wearable fabric that is flexible but can stiffen on demand. Developed through a combination of geometric design, 3D printing, and robotic control, the new technology, RoboFabric, can quickly be made into medical devices or soft robotics.
Meet CARMEN — short for Cognitively Assistive Robot for Motivation and Neurorehabilitation — a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory, attention, and executive functioning at home.
Inspired by the paper-folding art of origami, North Carolina State University engineers have discovered a way to make a single plastic cubed structure transform into more than 1,000 configurations using only three active motors. The findings could pave the way for shape-shifting artificial systems that can take on multiple functions and even carry a load — like versatile robotic structures used in space, for example.
Self-driving cars are coming, but will you really be okay sitting passively while a 2,000-pound autonomous robot motors you and your family around town?
Pick-and-place machines are a type of automated equipment used to place objects into structured, organized locations. These machines are used for a variety of applications — from electronics assembly to packaging, bin picking, and even inspection — but many current pick-and-place solutions are limited. Current solutions lack “precise generalization,” or the ability to solve many tasks without compromising on accuracy.
Researchers from Tohoku University and Kyoto University have successfully developed a DNA-based molecular controller that autonomously directs the assembly and disassembly of molecular robots. This pioneering technology marks a significant step toward advanced autonomous molecular systems with potential applications in medicine and nanotechnology.
Inspired by the paper-folding art of origami, North Carolina State University engineers have discovered a way to make a single plastic cubed structure transform into more than 1,000 configurations using only three active motors. The findings could pave the way for shape-shifting artificial systems that can take on multiple functions and even carry a load – like versatile robotic structures used in space, for example.
Mi Rancho has been delighting customers with authentic and fresh tortillas, chips, and salsas since its establishment in 1939. Originally founded as a grocery store in Oakland, CA, the business has evolved and grown into a food provider for large nation-wide retail partners. To enable their continued growth, Mi Rancho recently partnered with Formic to introduce robotic automation to their food processing and packaging production operations.
In nature, many organisms like octopuses with their flexible tentacles or elephants with their trunks, exhibit remarkable dexterity. Inspired by these natural structures, researchers aim to develop highly flexible continuum robots that offer robustness and safety. Ideally, a continuum robot is characterized by many degrees of freedom (DOFs) and the number of joints, more than needed for most tasks. These characteristics allow them to adjust and modify their shape dynamically, enabling them to avoid obstacles and unexpected situations. However, their complex movements make it difficult to characterize their shape and motion.
More than 80 percent of stroke survivors experience walking difficulty, significantly impacting their daily lives, independence, and overall quality of life. Now, new research from the University of Massachusetts Amherst pushes forward the bounds of stroke recovery with a unique robotic hip exoskeleton, designed as a training tool to improve walking function. This invites the possibility of new therapies that are more accessible and easier to translate from practice to daily life compared to current rehabilitation methods.
A new algorithm may make robots safer by making them more aware of human inattentiveness.
Researchers have developed SPINDLE, a pioneering robotic rehabilitation system. Combining virtual reality (VR) with customized resistance training, SPINDLE offers personalized therapy to enhance strength and dexterity for activities of daily living (ADLs). Its adaptability and potential for home use represent a major advancement in tremor rehabilitation, with broader healthcare implications.
The automotive industry faces unprecedented regulatory and societal pressure to adopt sustainable manufacturing practices. A recent survey by Accenture shows that more than 34 percent of today’s largest manufacturers have committed to zero-emission goals, yet 93 percent of them will miss their targets unless they double their emission reduction rates by 2030.
A new method leverages AI and computer simulations to train robotic exoskeletons that can help users save energy while walking, running, and climbing stairs. The novel method rapidly develops exoskeleton controllers to assist locomotion without relying on lengthy human-involved experiments.
Neural networks have made a seismic impact on how engineers design controllers for robots, catalyzing more adaptive and efficient machines. Still, these brain-like machine-learning systems are a double-edged sword: Their complexity makes them powerful, but it also makes it difficult to guarantee that a robot powered by a neural network will safely accomplish its task.
Today, advancements in industrial laser cleaning automation show great promise in boosting productivity and safety when rust and contaminant removal or surface preparation is required for higher volumes of components and equipment.
Getting 800 robots in a warehouse to and from their destinations efficiently while keeping them from crashing into each other is no easy task. In a sense, these robots are like cars trying to navigate a crowded city center.
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