Register for a range of sessions:
U-Space Regulation in Europe: Mandatory and Early Adoption of Remote ID Technology
12:00 PM – 12:45 PM ET
The European Aviation Safety Agency has just approved a U-Space (aka UAS traffic management, UTM) regulatory package that will become law by 2023. The package provides the legal basis required to implement U-Space across Europe, and calls for mandatory services including Network Remote Identification (NET-RID), which was developed and implemented by members of the public-private partnership Swiss U-Space Implementation (SUSI), under the supervision of Switzerland’s Civil Aviation Authority FOCA.
NET-RID enables information sharing about drone operations via the internet, and is already useable in Switzerland on a voluntary basis by all operators and authorised users, including law enforcement agencies, airport managers and the general public.
FOCA is already offering this service now, before it becomes compulsory. Why? It allows the regulator to test the service and its infrastructure; to suggest tested/better Acceptable Means of Compliance; to establish a plan for the implementation of future U-Space services; and to measure voluntary participation by drone operators and the level of acceptance of drones by the population.
Should you think about implementing NET-RID now? Listen as these stakeholders discuss what to expect in Europe and the world, and when the best time is to incorporate NET-RID into your operation.
Path to Commerce for Autonomy. Edge Infrastructure and Easements for Intelligent & Autonomous Operations within Cities. “Investment in a 21st-century Intelligent & Autonomous Infrastructure is among the highest priorities for stimulating economic expansion, national security, and job growth. ” Creating Highways and Byways for the Digital and Autonomous World. Urban Air, autonomous cars & trucks, inspection drones, etc., all need to be part of, and to share, a common infrastructure. Local governments are struggling today with permitting disparate infrastructures that need to be a single open and intelligent infrastructure. The country that does this well first will likely capture the preponderance of the economic benefit. Conceptually, the autonomy landscape is the cloud computing landscape writ large. The digital infrastructure at the edge needs intelligent system developers to have a market and the intelligent system developers are equally dependent on the digital infrastructure being deployed at the edge. The autonomy infrastructure permits “apps” like optimized traffic management, autonomous cars, or drones that respond to 911 calls, or autonomous delivery, or automated road and bridge inspection. The digital infrastructure these “apps” need is the public, open, locally controlled intelligent, and autonomous infrastructure. The Public Infrastructure Network Nodes support Industry 4.0.
Ocean Going Vessels (OGV’s) are significant air pollution sources. OGV fuels are largely to blame. Under an international convention (MARPOL) certain sea areas (ECA’s) are designated where only low sulfur fuels are allowed (or high sulfur fuels if the OGV has scrubbers). Public health experts estimate the lower sulfur caps (0.5 % in ECA & 0.1% in CA) prevent over 150,000 premature deaths & 7.6 million child asthma cases per year. But there are strong indications OGV’s cheat within ECA’s and in California waters. Since 1999, monitoring of sulfur content in fuel supplied to OGV’s is conducted by the International Maritime Organization (IMO) via bunker report reviews. But this method is not fool proof. Advances in sensor and UAV technologies allow for greater efficacy to monitor OGV emissions. Sensors, when mounted on a suitably designed UAV – can be positioned for a sufficiently long time in an OGV plume– and thus detect with sufficient accuracy the sulfur content in its fuel. In the EU, there’s an EMSA program and another called SCIPPER to evaluate the pro’s and con’s of such systems. MARAD, EPA, CARB & Hitachi are co-sponsoring a similar and complementary program in California (adapted to US conditions).
Assured Onboard Autonomy Architecture for Autonomous Underwater Vehicles
2:00 PM – 2:45 PM ET
Autonomous Underwater Vehicles (AUVs) are becoming widely used in naval operations and commercial applications such as oil and gas, environmental surveying and monitoring, search and rescue, etc. To be most effective, AUVs should be operable by any person giving simple mission instructions. The end-users should be able to trust AUV platforms and autonomy software to complete missions with minimal human interaction.
For assured autonomy, AUVs should be able to:
- Understand the state of its surroundings and itself (situational awareness)
- Relate the current state to the mission objectives and goals (mission planning)
- Re-configure mission and behavior parameters to the unforeseen changes in the environment and in the AUV (adaptation)
- Receive mission commands using highly constrained communication channels (underwater communication)
- Provide detailed reports to the end user explaining the actions taken in executing the mission (explainable AI)
Hostile and complex environments and a lack of multi-modal sensors make it challenging to produce AUVs with these capabilities. In this paper, we present an adaptive autonomy architecture which will make AUV adaptable to varying environmental and internal state changes. We will discuss the implementations and performance of this architecture onboard AUVs.
CAL Analytics, an Ohio-based company, and the Ohio State University (OSU) have been integrating and testing ACAS-sXu, a Detect-and-Avoid capability for small Unmanned Aircraft Systems (sUAS) being developed by the Federal Aviation Administration, Johns Hopkins University / Applied Physics Lab, and Lincoln Labs. Integration and test were performed with simulated and real data to assess DAA performance and the feasibility of integrating a Class III DAA system into a representative UAS Traffic Management (UTM) ecosystem in Ohio. In support of this effort, CAL developed a Software as a Service (SaaS) DAA platform using ACAS-sXu as its core technology. Additionally, OSU developed an automated response system which allows UAS participating within the UTM ecosystem to receive recommended guidance from the SaaS DAA platform and respond autonomously. CAL, working with OSU and AiRXOS, integrated these capabilities into an UTM ecosystem. These capabilities were then flight tested to verify modeled performance and feasibility of the Class III DAA system and associated automation capabilities. CAL and OSU will present modeled DAA results, results from live DAA flight testing, and lessons learned regarding use of ACAS-sXu for DAA and the interplay of results with and without automation within a relevant UTM ecosystem.
Detect-and-avoid systems to allow unmanned aircraft to avoid conflicts with manned aircraft when flown beyond the visual line of sight of the pilot is critical to expanding the UAS industry, providing safety, obtaining public and regulatory trust, and delivering the full economic impact of real autonomy, The industry is eager for the release of detect-and-avoid safety systems that are reliable, portable or on-board, affordable and standards-compliant. Developing this system is challenging due to the need to integrate multiple sensors, develop algorithms and demonstrate working technology in actual field conditions. Meeting this challenge represents the leading edge of technical capability and innovation in the UAS industry but is now closer than ever to being ready for delivery. This presentation will review the status of detect-and-avoid technology, the impact of recent technical and system innovations, ongoing development and testing efforts involving multiple national stakeholders, review of new standards and rules, and review of the remaining challenges to delivery of a full DAA system for commercial UAS. This presentation will also specifically review the status of an FAA-supported detect-and-avoid system development and testing effort being conducted in cooperation with a national UAS Test Site.
Visual ID and Tracking as Part of Counter UAS Systems
3:00 PM – 3:45 PM ET
Counter-UAS (C-UAS) systems continue to be an important priority in the industry. There are several different architectures C-UAS systems currently use in the market. Many of them include a camera system for operator visual confirmation. Adding an AI/ML classification function and auto-tracker to this architecture would reduce false detections thereby easing operator workload. Discussion will examine the current state of COTS video processors to provide this needed functionality, and explore the tradeoffs of in-house development vs COTS integration.