This paper presents a nonlinear integrated guidance and control (IGC) approach for flexible leader-follower formation flight of fixed-wing unmanned aerial vehicles (UAVs) while accounting for high-fidelity aerodynamics and thrust dynamics. Unlike conventional leader-follower schemes that fix the follower's position relative to the leader, the follower is steered to maintain range and bearing angles (which is the angle between its velocity vector and its line-of-sight (LOS) with respect to the leader) arbitrarily close to the prescribed values, enabling the follower to maintain formation on a hemispherical region behind the leader. The proposed IGC framework directly maps leader-follower relative range dynamics to throttle commands, and the follower's velocity orientation relative to the LOS to aerodynamic control surface deflections. This enables synergism between guidance and control subsystems. The control design uses a dynamic surface control-based backstepping approach to achieve convergence to the desired formation set, where Lyapunov barrier functions are incorporated to ensure the follower's bearing angle is constrained within specified bounds. Rigorous stability analysis guarantees uniform ultimate boundedness of all error states and strict constraint satisfaction in the presence of aerodynamic nonlinearities. The proposed flexible formation scheme allows the follower to have an orientation mismatch relative to the leader to execute anticipatory reconfiguration by transitioning between the relative positions in the admissible formation set when the leader aggressively maneuvers. The proposed IGC law relies only on relative information and onboard sensors without the information about the leader's maneuver, making it suitable for GPS-denied or non-cooperative scenarios. Finally, we present simulation results to vindicate the effectiveness and robustness of our approach.

This paper presents an input-constrained nonlinear guidance law to address the problem of intercepting a stationary target in contested environments with multiple defending agents. Contrary to prior approaches that rely on explicit knowledge of defender strategies or utilize conservative safety conditions based on a defender's range, our work characterizes defender threats geometrically through engagement zones that delineate inevitable interception regions. Outside these engagement zones, the interceptor remains invulnerable. The proposed guidance law switches between a repulsive safety maneuver near these zones and a pursuit maneuver outside their influence. To deal with multiple engagement zones, we employ a smooth minimum function (log-sum-exponent approximation) that aggregates threats from all the zones while prioritizing the most critical threats. Input saturation is modeled and embedded in the non-holonomic vehicle dynamics so the controller respects actuator limits while maintaining stability. Numerical simulations with several defenders demonstrate the proposed method's ability to avoid engagement zones and achieve interception across diverse initial conditions.

Bengaluru, India – On January 23, an icon of Indian cinema and politics, M Karunanidhi appeared before a live audience on a large projected screen, to congratulate his 82-year-old friend and fellow politician TR Baalu on the launch of his autobiographical book. Dressed in his trademark black sunglasses, white shirt, and a yellow shawl around his shoulders -- Karunanidhi's style was spot on. In his eight-minute speech, the veteran poet-turned-politician congratulated the book's author but was also effusive in his praise for the able leadership of MK Stalin, his son and the current leader of the state. Karunanidhi has been dead since 2018. This was the third time, in the past six months, that the iconic leader of the Dravida Munnetra Kazhagam (DMK) party was resurrected using artificial intelligence (AI) for such public events.

Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: a) it allows for online real-time monitoring of a dynamical system; b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; c) it is computationally fast and less intensive than the popular Extended Dynamic Mode Decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

Inside the womb, fetuses can begin to hear some sounds around 20 weeks of gestation. However, the input they are exposed to is limited to low-frequency sounds because of the muffling effect of the amniotic fluid and surrounding tissues. A new MIT-led study suggests that this degraded sensory input is beneficial, and perhaps necessary, for auditory development. Using simple computer models of the human auditory processing, the researchers showed that initially limiting input to low-frequency sounds as the models learned to perform certain tasks actually improved their performance. Along with an earlier study from the same team, which showed that early exposure to blurry faces improves computer models' subsequent generalization ability to recognize faces, the findings suggest that receiving low-quality sensory input may be key to some aspects of brain development.

Imagine flying a small, robotic aircraft from goal post to goal post on an American football field. Now, repeat the flight 470 more times, and you'll match the record-setting 32-mile autonomous drone flight recorded by Aakash Sinha's industry-leading startup based in New Delhi. "It's only the beginning," said Sinha, a 2003 School of Computer Science graduate with a master's degree in robotics. "I'm super excited about how drones can change things, not just here in India but globally." From delivering vaccines in hard-to-reach areas to limiting fossil fuel leaks in expansive pipelines, the possibilities for positive change are endless.

AI-based teaching assistant startup ByteLearn has raised $9.5 Mn in its seed round and came out of its stealth mode. The platform is now open for early access, as per the startup's website. In February 2021, edtech startup Vedantu had acquired the doubt-solving platform InstaSolv to boost its test prep and K-12 tutoring verticals. Two of InstaSolv founders Aditya Singhal and Nishant Sinha took an exit and went on to build a new venture -- ByteLearn. The startup plans to use the funds for product development, global expansion and enhancing technological capabilities.

Riva Health, founded by scientist Tuhin Sinha and Siri co-founder Dag Kittlaus, wants to help people measure their blood pressure in a clinically approved way. Blood pressure can help indicate at-risk patients before they are actually at risk, showing early signs of heart disease. While other hardware solutions on the market promise the same end goal, Riva wants to be a purely software solution that integrates with hardware that it thinks its end user has anyway: their smartphone. The company, launching out of stealth today, has raised $15.5 million in seed funding in a round led by Menlo Ventures, with participation from True Ventures. Greg Yap of Menlo, who talked to Sinha for three years before investing, will be joining the board.

In 1896, Italian physician Riva Rocci published the first of four papers on an invention that is still widely used. It was his take on the sphygmomanometer, a device to measure the pressure that a pumping heart exerts on the arteries. Rocci's basic approach of tying a cuff to the upper arm remains standard, and it is a vital tool because hypertension is one of the most serious medical ailments. The CDC reports that nearly half of all adults in the US have high blood pressure, and it is a primary or contributing factor in 500,000 deaths annually--it's like Covid-19 every year. Only a fourth of people with hypertension have it under control, in part because sphygmomanometers, whether used in a doctor's office or via clunky home units, don't supply a steady stream of readings, multiple times a day and in different settings, to help determine the proper treatment.

Researchers from MIT, Stanford University, and the University of Pennsylvania have devised a method for predicting failure rates of safety-critical machine learning systems and efficiently determining their rate of occurrence. Safety-critical machine learning systems make decisions for automated technology like self-driving cars, robotic surgery, pacemakers, and autonomous flight systems for helicopters and planes. Unlike AI that helps you write an email or recommends a song, safety-critical system failures can result in serious injury or death. Problems with such machine learning systems can also cause financially costly events like SpaceX missing its landing pad. Researchers say their neural bridge sampling method gives regulators, academics, and industry experts a common reference for discussing the risks associated with deploying complex machine learning systems in safety-critical environments. In a paper titled "Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems," recently published on arXiv, the authors assert their approach can satisfy both the public's right to know that a system has been rigorously tested and an organization's desire to treat AI models like trade secrets.