Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, also create new security risks. In this paper, we introduce CHAI (Command Hijacking against embodied AI), a new class of prompt-based attacks that exploit the multimodal language interpretation abilities of Large Visual-Language Models (LVLMs). CHAI embeds deceptive natural language instructions, such as misleading signs, in visual input, systematically searches the token space, builds a dictionary of prompts, and guides an attacker model to generate Visual Attack Prompts. We evaluate CHAI on four LVLM agents; drone emergency landing, autonomous driving, and aerial object tracking, and on a real robotic vehicle. Our experiments show that CHAI consistently outperforms state-of-the-art attacks. By exploiting the semantic and multimodal reasoning strengths of next-generation embodied AI systems, CHAI underscores the urgent need for defenses that extend beyond traditional adversarial robustness.

By entering the habitats of wild animals, wildlife watchers can engage closely with them. There are some wild animals that are not always safe to approach. Therefore, we suggest this system for observing wildlife. Android phones can be used by users to see live events. Wildlife observers can thus get a close-up view of wild animals by employing this robotic vehicle. The commands are delivered to the system via a Wi-Fi module. As we developed the technology to enable our robot to deal with the challenges of maintaining continuous surveillance of a target, we found that our robot needed to be able to move silently and purposefully when monitoring a natural target without being noticed. After processing the data, the computer sends commands to the motors to turn on. The driver motors, which deliver the essential signal outputs to drive the vehicle movement, are now in charge of driving the motors.

Abstract--In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressing environmental concerns combined with the recent surge of e-commerce sales have sparked renewed interest in automation and electrification of last-mile logistics. To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots. The presented framework enables end-to-end automation and optimization of the logistic process while catering for realworld imposed operational uncertainties, clients' preferred time schedules, and safety of pedestrians. To this end, the integrated optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows, where routes are constructed under uncertain travel times with an objective to minimize the total latency of deliveries (i.e., the overall waiting time of customers, which can negatively affect their satisfaction). We demonstrate the proposed LMD system's utility through real-world trials in a university campus with a single robotic courier. Implementation aspects as well as the findings and practical insights gained from the deployment are discussed in detail. Lastly, we round up the contributions with numerical simulations to investigate the scalability of the developed mathematical formulation with respect to the number of robotic vehicles and customers.

To the CSE community: We are excited to introduce you to the newest member of our department -SmartWheels-, a miniature robotic vehicle! Our robotic vehicle is outfitted with the most advanced sensor technology, including LiDAR, radar, and cameras, enabling it to perceive its surroundings in real-time and make intelligent decisions based on its surroundings. It is also powered by cutting-edge AI algorithms that enable it to learn from its experiences and enhance its performance continuously. This platform will host numerous master's theses and graduation projects. We appreciate you taking the time to learn about our latest innovation.

His case rests on a viral video he tweeted last month of a goldfish driving a water-tank-equipped robotic vehicle down the side of a street and inside his lab at Ben-Gurion University of the Negev in Israel. The roboride was part of a scientific study to test whether goldfish had the mental acuity to navigate a terrestrial environment toward a target using a machine. The six goldfish that took part in driver's training passed their test. They weren't the first to cross the finish line. Other neuroscientists have taught rats to drive cars as part of experiments testing how experience affects learning.

Fox News national security correspondent Jennifer Griffin discusses a report alleging China is developing'brain control weapons' on'Fox Report.' Reports from India claim that China has started to deploy armed robotic vehicles to handle the altitude and terrain that has proven too difficult for its troops. China and India clashed in Sept. 2020 during a border dispute along the southern coast of Pangong Lake in an area known in China as Shenpaoshan and in India as Chushul, but the armies continued their standoff along the two nations' borders throughout 2021. China has now reportedly deployed unmanned ground vehicles (UGV) to the region of Tibet to strengthen its position. People's Liberation Army (PLA) soldiers march next to the entrance to the Forbidden City during the opening ceremony of the Chinese People's Political Consultative Conference (CPPCC) in Beijing on May 21, 2020.

Learn how to program robotic vehicles with ardupilot libraries and pixhawk autopilot, both of which are open source technologies with a global scope. This book is focused on quadcopters but the knowledge is easily extendable to three-dimensional vehicles such as drones, submarines, and rovers. Pixhawk and the ardupilot libraries have grown dramatically in popularity due to the fact that the hardware and software offer a real-time task scheduler, huge data processing capabilities, interconnectivity, low power consumption, and global developer support. This book shows you how to take your robotic programming skills to the next level. By the end of this book, you'll learn the pixhawk software and ardupilot libraries to develop your own autonomous vehicles.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. Army will soon operate robots able to destroy enemy armored vehicles with anti-tank missiles, surveil warzones under heavy enemy fire and beam back identified targeting details in seconds due to rapid progress with several new armed robot programs. Several of the new platforms now operate with a Kongsberg-built first-of-its-kind wireless fire control architecture for a robotic armored turret with machine guns, Javelin Anti-Tank Missiles and robot-mounted 30mm cannon selected by the Army to arm its fast-emerging Robotic Combat Vehicles. These now-in-development robotic systems are intended to network with manned vehicles in high-risk combat operations.

The 2025 market for AI, including ADAS and robotic vehicles, is estimated at $2.75 billion – of which $2.5 billion will be "ADAS only"... Artificial Intelligence (AI) is gradually invading our lives through everyday objects like smartphones, smart speakers, and surveillance cameras. The hype around AI has led some players to consider it as a secondary objective, more or less difficult to achieve, rather than as a central tool to achieve the real objective: autonomy. Who are the winners and losers in the race for autonomy? "AI is gradually invading our lives and this will be particularly true in the automotive world" asserts Yohann Tschudi, Technology & Market Analyst, Computing & Software at Yole Développement (Yole). "AI could be the central tool to achieve AD, in the meantime some players are afraid of overinflated hype and do not put AI at the center of their AD strategy".

The researchers, based at UBC's faculty of applied science, designed three types of stealth attack on robotic vehicles that caused the machines to crash, miss their targets or complete their missions much later than scheduled. The attacks required little to no human intervention to succeed on both real and simulated drones and rovers. "We saw major weaknesses in robotic vehicle software that could allow attackers to easily disrupt the behaviour of many different kinds of these machines," said Karthik Pattabiraman, the electrical and computer engineering professor who supervised the study. "Especially worrisome is the fact that none of these attacks could be detected by the most commonly used detection techniques." Robotic vehicles use special algorithms to stay on track while in motion, as well as to flag unusual behaviour that could signal an attack.