Micro aerial vehicles (MAVs) have high potential for information gathering tasks to support situation awareness in search and rescue scenarios. Manually controlling MAVs in such scenarios requires experienced pilots and is error-prone, especially in stressful situations of real emergencies. The conditions of disaster scenarios are also challenging for autonomous MAV systems. The environment is usually not known in advance and GNSS might not always be available. We present a system for autonomous MAV flights in unknown environments which does not rely on global positioning systems. The method is evaluated in multiple search and rescue scenarios and allows for safe autonomous flights, even when transitioning between indoor and outdoor areas.

Autonomous flight is a grand challenge in aviation -- and a gold mine. The first company to crack it at scale stands to reap handsome profits from transportation and logistics alone. In 2020, the size of the global cargo airline industry was $110.8 billion, according to Statista, and one source estimates that it'll generate hundreds of billions in revenue by 2027. Xwing is one of the startups chasing after self-flying planes, as is Reliable Robotics, Pyka and the unicorn Volocopter. Roughly a year ago, Boston-based Merlin Labs emerged from stealth with an autonomous flight system designed to be installed in existing aircraft.

One of the important challenges of autonomous flight is the Sense and Avoid (SAA) task to maintain enough separation from obstacles. While the route of an autonomous drone might be carefully planned ahead of its mission, and the airspace is relatively sparse, there is still a chance that the drone will encounter unforeseen airborne objects or static obstacles during its autonomous flight. The autonomous SAA module has to take on the tasks of situational awareness, decision making, and flying the aircraft, while performing an evasive maneuver. There are several alternatives for onboard sensing including radar, LIDAR, passive electro-optical sensors, and passive acoustic sensors. Solving the SAA task with visual cameras is attractive because cameras have relatively low weight and low cost. For the purpose of this challenge, we consider a solution that solely relies on a single visual camera and Computer Vision technique that analyzes a monocular video. Flying airborne objects pose unique challenges compared to static obstacles. In addition to the typical small size, it is not sufficient to merely detect and localize those objects in the scene, because prediction of the future motion is essential to correctly estimate if the encounter requires a collision avoidance maneuver and create a safer route. Such prediction will typically rely on analysis of the motion over a period of time, and therefore requires association of the detected objects across the video frames.

In the last online technical talk, Adam Bry and Hayk Martiros from Skydio explained how their company tackles real-world issues when it comes to drone flying. Skydio is the leading US drone company and the world leader in autonomous flight. Our drones are used for everything from capturing amazing video, to inspecting bridges, to tracking progress on construction sites. At the core of our products is a vision-based autonomy system with seven years of development at Skydio, drawing on decades of academic research. This system pushes the state of the art in deep learning, geometric computer vision, motion planning, and control with a particular focus on real-world robustness.

Pilot-less jetliners may still be far off in the future due to several reasons, public trust in automated systems not being the least of them. However, this does not mean the software technology to support such operations has not developed in leaps and bounds. While there are several start-ups in tech-driven unmanned airborne vehicles, let's take a look at how the two main aircraft manufacturers use artificial intelligence in the quest for safe autonomous flight. Artificial Intelligence (AI) is a divisive subject. Some herald it as the key solution to everything from Alzheimer's and cancer to food shortages and climate change.

EASA envisions three stages of AI's rollout in aviation: systems that will assist pilots (2022–2025); human-machine collaboration in flying an aircraft, such as a "virtual" first officer (2025–2030); and autonomous commercial air transport, or, more colloquially, pilotless airliners that fly themselves (2035 and beyond). EASA broadly defines AI as "any technology that appears to emulate the performance of a human." Ultimately, the widespread deployment of AI in aviation comes down to a matter of trust, EASA stated. "A European ethical approach to AI is central to strengthen citizens' trust in the digital development and aims at building a competitive advantage for European companies," according to the EASA roadmap. "Only if AI is developed and used in a way that respects widely shared ethical values can it be considered trustworthy. Therefore, there is a need for ethical guidelines that build on the existing regulatory framework. In June 2018, the [European] Commission set up a High-Level Expert Group on Artificial Intelligence (AI HLEG), the general objective of which was to support the implementation of the European strategy on AI. This includes the elaboration of recommendations on future-related policy development and on ethical, legal and societal issues related to AI, including socio-economic challenges. In April 2019, the AI HLEG proposed the following seven key requirements for trustworthy AI, which were published in its report on Ethics Guidelines on Trustworthy Artificial Intelligence."

Multilinked aerial robot is one of the state-of-the-art works in aerial robotics, which demonstrates the deformability benefiting both maneuvering and manipulation. However, the performance in outdoor physical world has not yet been evaluated because of the weakness in the controllability and the lack of the state estimation for autonomous flight. Thus we adopt tilting propellers to enhance the controllability. The related design, modeling and control method are developed in this work to enable the stable hovering and deformation. Furthermore, the state estimation which involves the time synchronization between sensors and the multilinked kinematics is also presented in this work to enable the fully autonomous flight in the outdoor environment. Various autonomous outdoor experiments, including the fast maneuvering for interception with target, object grasping for delivery, and blanket manipulation for firefighting are performed to evaluate the feasibility and versatility of the proposed robot platform. To the best of our knowledge, this is the first study for the multilinked aerial robot to achieve the fully autonomous flight and the manipulation task in outdoor environment. We also applied our platform in all challenges of the 2020 Mohammed Bin Zayed International Robotics Competition, and ranked third place in Challenge 1 and sixth place in Challenge 3 internationally, demonstrating the reliable flight performance in the fields.

This new implementation could be key for autonomous cars, in addition to search and rescue.

Team MAVLab received a $1 million cash prize for winning the 2019 Artificial Intelligence Robotic Racing Championship. Team MAVLab, the drone research lab of the Delft University of Technology, won a $1 million cash prize as the leading AlphaPilot team of the 2019 Artificial Intelligence Robotic Racing Circuit, the autonomous drone racing series that accelerates AI innovation through futuristic sports competition. The winning team was announced by Lockheed Martin and The Drone Racing League (DRL), the professional drone racing circuit, following the AIRR Championship, which took place Friday (December 6) at the Austin American-Statesman in Austin, Texas. Lockheed Martin sponsored the $1 million cash prize. The AIRR Championship marked the final race of a four-event series that aims to advance the development and testing of fully autonomous drone technologies for real-world applications including disaster relief, search and rescue missions, and space exploration.

While AIs are increasingly beating us mere mortals at many things, racing drones is something we still have the upper hand at. The Drone Racing League (DRL) orchestrated its first AI racing competitions this year, with the final of a four-part series held in Texas earlier this month. The races aim to advance the development and testing of fully autonomous drone technologies for real-world applications including disaster relief, search and rescue missions, and space exploration. The DRL RacerAI is the first autonomous drone designed to defeat a human in a physical sport. The drone features the NVIDIA Jetson AGX Xavier AI-at-the-edge compute platform in addition to four onboard stereoscopic cameras which enable the AI to detect and identify objects with twice the field of view as human pilots.