After 27 years of developing airliners, my involvement in electric aircraft started suddenly one afternoon in February 2017. I was asked to comment on the eHang 184, a Chinese passenger drone, which could in theory provide automated taxi services in Dubai. The oft-quoted part of the resulting article will probably appear in my obituary. Wright added that he would not be volunteering for an early flight. 'I'd have to be taken on board kicking and screaming.'"

The advent of Urban Air Mobility (UAM) presents the scope for a transformative shift in the domain of urban transportation. However, its widespread adoption and economic viability depends in part on the ability to optimally schedule the fleet of aircraft across vertiports in a UAM network, under uncertainties attributed to airspace congestion, changing weather conditions, and varying demands. This paper presents a comprehensive optimization formulation of the fleet scheduling problem, while also identifying the need for alternate solution approaches, since directly solving the resulting integer nonlinear programming problem is computationally prohibitive for daily fleet scheduling. Previous work has shown the effectiveness of using (graph) reinforcement learning (RL) approaches to train real-time executable policy models for fleet scheduling. However, such policies can often be brittle on out-of-distribution scenarios or edge cases. Moreover, training performance also deteriorates as the complexity (e.g., number of constraints) of the problem increases. To address these issues, this paper presents an imitation learning approach where the RL-based policy exploits expert demonstrations yielded by solving the exact optimization using a Genetic Algorithm. The policy model comprises Graph Neural Network (GNN) based encoders that embed the space of vertiports and aircraft, Transformer networks to encode demand, passenger fare, and transport cost profiles, and a Multi-head attention (MHA) based decoder. Expert demonstrations are used through the Generative Adversarial Imitation Learning (GAIL) algorithm. Interfaced with a UAM simulation environment involving 8 vertiports and 40 aircrafts, in terms of the daily profits earned reward, the new imitative approach achieves better mean performance and remarkable improvement in the case of unseen worst-case scenarios, compared to pure RL results.

This paper develops a graph reinforcement learning approach to online planning of the schedule and destinations of electric aircraft that comprise an urban air mobility (UAM) fleet operating across multiple vertiports. This fleet scheduling problem is formulated to consider time-varying demand, constraints related to vertiport capacity, aircraft capacity and airspace safety guidelines, uncertainties related to take-off delay, weather-induced route closures, and unanticipated aircraft downtime. Collectively, such a formulation presents greater complexity, and potentially increased realism, than in existing UAM fleet planning implementations. To address these complexities, a new policy architecture is constructed, primary components of which include: graph capsule conv-nets for encoding vertiport and aircraft-fleet states both abstracted as graphs; transformer layers encoding time series information on demand and passenger fare; and a Multi-head Attention-based decoder that uses the encoded information to compute the probability of selecting each available destination for an aircraft. Trained with Proximal Policy Optimization, this policy architecture shows significantly better performance in terms of daily averaged profits on unseen test scenarios involving 8 vertiports and 40 aircraft, when compared to a random baseline and genetic algorithm-derived optimal solutions, while being nearly 1000 times faster in execution than the latter.

Near future air taxi operations with electric vertical take-off and landing (eVTOL) aircraft will be constrained by the need for frequent recharging of eVTOLs, limited takeoff and landing pads in vertiports, and subject to time-varying demand and electricity prices, making the eVTOL dispatch problem unique and particularly challenging to solve. Previously, we have developed optimization models to address this problem. Such optimization models however suffer from prohibitively high computational run times when the scale of the problem increases, making them less practical for real world implementation. To overcome this issue, we have developed two deep reinforcement learning-based eVTOL dispatch algorithms, namely single-agent and multi-agent deep Q-learning eVTOL dispatch algorithms, where the objective is to maximize operating profit. An eVTOL-based passenger transportation simulation environment was built to assess the performance of our algorithms across $36$ numerical cases with varying number of eVTOLs, vertiports, and demand. The results indicate that the multi-agent eVTOL dispatch algorithm can closely approximate the optimal dispatch policy with significantly less computational expenses compared to the benchmark optimization model. The multi-agent algorithm was found to outperform the single-agent counterpart with respect to both profits generated and training time.

Some companies think it's time the aviation industry got a makeover, and many are betting it'll come in the form of eVTOLs: electric vertical take-off and landing vehicles. There are hundreds of companies working to bring the small aircrafts that take off and land like a helicopter and fly like a plane to the skies. If they gain regulatory approval, they could change how we think about flight. But that's a big "if," and there are other questions for the industry to answer before these new flying vehicles become a reality. So, how close are today's eVTOLs to taking off, and is any of this a good idea for the climate?

Majority of aircraft under the Urban Air Mobility (UAM) concept are expected to be of the electric vertical takeoff and landing (eVTOL) vehicle type, which will operate out of vertiports. While this is akin to the relationship between general aviation aircraft and airports, the conceived location of vertiports within dense urban environments presents unique challenges in managing the air traffic served by a vertiport. This challenge becomes pronounced within increasing frequency of scheduled landings and take-offs. This paper assumes a centralized air traffic controller (ATC) to explore the performance of a new AI driven ATC approach to manage the eVTOLs served by the vertiport. Minimum separation-driven safety and delays are the two important considerations in this case. The ATC problem is modeled as a task allocation problem, and uncertainties due to communication disruptions (e.g., poor link quality) and inclement weather (e.g., high gust effects) are added as a small probability of action failures. To learn the vertiport ATC policy, a novel graph-based reinforcement learning (RL) solution called "Urban Air Mobility- Vertiport Schedule Management (UAM-VSM)" is developed. This approach uses graph convolutional networks (GCNs) to abstract the vertiport space and eVTOL space as graphs, and aggregate information for a centralized ATC agent to help generalize the environment. Unreal Engine combined with Airsim is used as the simulation environment over which training and testing occurs. Uncertainties are considered only during testing, due to the high cost of Mc sampling over such realistic simulations. The proposed graph RL method demonstrates significantly better performance on the test scenarios when compared against a feasible random decision-making baseline and a first come first serve (FCFS) baseline, including the ability to generalize to unseen scenarios and with uncertainties.

Break out the champagne and roll out the red carpets, CES is back! After two rough, COVID-addled years that saw the world's greatest tech show reduced to a shell of its former self, the show is primed to spring back to its former glory for 2023. And our team of writers and editors will be on the ground in Las Vegas, bringing it all to you. But much has changed since the last "normal" CES of 2020. The economy has boomed and busted, supply chains have knotted, and attitudes over excess have shifted as climate change looms larger and larger in our global conversation.

Kittyhawk was founded in 2010 to pioneer the market for so-called eVTOLs -- electric vertical takeoff and landing aircraft -- with the lofty goal of democratizing the skies. The secretive company was run by Sebastian Thrun, a Google veteran who worked on self-driving cars, augmented-reality glasses and other projects. The business was one of several startups working on the concept, which has proven to be a greater challenge than some expected. Air taxis have suffered crashes during testing in recent months, raising concerns about their safety. Insider previously reported on Kittyhawk's plans to close.

As urban mobility giants like Boeing, Hyundai, Airbus, Toyota, Uber and Joby Aviation plan to soon ferry passengers in air taxis, the homegrown ePlane Company is all set to to build India's first flying electric taxi to make passenger commute and cargo transport up to 10 times faster, says its founder and CTO Satya Chakravarthy The startup is in the final stages of building a sub-scale version of the flying aircraft and expects to commence its flight trials in the next couple of months. "We are developing the full-scale prototype, the ePlane e200, and aim to have the e200 cargo variant built towards the end of 2022 and undergo the certification process through the next year for it to be ready for commercial deployment approximately by late 2023," Chakravarthy told IANS. The passenger version of the ePlane e200 would undergo additional development and flight tests for a more rigorous certification process, "which would take us until 2024 for its certification and their commercialisation as air taxis will happen subsequently," he noted. The market for flying cars, now known as electric air taxis, can reach $1.5 trillion globally by 2040, according to a recent study by Morgan Stanley Research. Earlier this year, the ePlane company that aims to develop electric planes for short-range intra-city commutes, raised $5 million in a pre-series A round.

Doroni Aerospace, Inc. ("Doroni") announces the launch of their new crowdfunding campaign on the equity crowdfunding platform. The company previously closed their first Reg CF raise on StartEngine on April 29, 2022, having officially raised $1,069,850 from 916 investors. Now, the company has its sights set on a $2M offering max and is offering investors 50% Bonus Shares of Preferred Stock for the first 3 days the campaign is live as part of a limited-time, welcome back promotion. Doroni CEO/Founder Doron Merdinger is also inviting long-time supporters as well as new investors to join the team for an exclusive welcome back webinar on Wednesday, July 20th 3PM EST. Doron will be providing an overview of the company, current development progress of the H1 eVTOL, and will be answering questions in addition to offering a glimpse at what's next for the company.