With the rapid advances in aviation technology, the idea of unmanned drones and air taxis in our cities is becoming more concrete. But as we await this fascinating future, the question remains: How can we ensure that these autonomous flying systems operate safely and efficiently? This is where the SIMBA (smart, simulation-based flight planning of UAS to increase the level of automation) came in, aiming to significantly increase the level of automation in the operation of drones and air cabs. A key challenge was to transfer the knowledge and tasks of traditional pilots into intelligent software. This would not only enable increased safety but also provide answers to the essential questions of a changing aviation industry.
Project Goal & Challenges
The SIMBA project had the overarching goal of significantly increasing the level of automation in the operation of drones and air taxis. This was achieved by converting the knowledge and all tasks normally performed by pilots into software. The automation of pilot knowledge and activities was crucial for autonomous UAS (Unmanned Aircraft Systems) flights, as the pilot's safety function in unmanned aviation had inevitably been taken over by technology. The goal of the project was to further develop Unisphere's existing 4d technology to market maturity.
By automating pilot knowledge that was essential for the safe execution of flights, SIMBA aimed to solve the following four problems of unmanned aviation:
Challenging operating environment: UAS (Unmanned Aircraft Systems) operated in lower airspace, where the weather had a stronger influence on the flight. Obstacles close to the ground could lead to collisions, which made flight management in lower airspace complex and challenging.
Ability to react quickly: According to studies, the number of UAS flights would increase by a factor of five hundred compared to conventional aviation in the near future, especially in urban areas. The high flight density required quick decisions and maximum automation.
Remote decisions: Remote decisions changed the role of the UAS pilot. As the pilot was not in the aircraft, essential information was lost, such as the stability of the aircraft in the air. It was therefore necessary for safety to technologically represent the pilot and his or her evaluation of information.
Lack of UAS performance models: In the field of unmanned aerial systems, there were hardly any models to illustrate flight performance to that day, in contrast to conventional aviation. The complex procedures of conventional aviation did not apply to less expensive UAS.
Smart 4D trajectory as a solution
The SIMBA project aimed at solving these challenges in UAS operations. For this purpose, a smart 4D trajectory was being developed that consisted of the two following components.
Simulation of the flight, in which he various parameters were combined (4D trajectory) to simulate the flight according to the current conditions (weather, flight performance model of the UAS, operational limitation, etc.).
Fully automatic evaluation of the simulation to check feasibility, considering all relevant parameters such as the risk of icing, the strength of wind gusts, or precipitation rates.
The development of this underlying technology followed three principles:
Automated decision-making processes: To replace a pilot's decision-making process for flight execution with software, all the information used was integrated into the software. This relied on our understanding of various trade-offs, such as whether a flight was possible if no parameters were in the “red zone” but all indicated marginal conditions. This will advanced further the traffic light visualization in the NOVA Operations & Weather Management Platform.
Accuracy of flight performance models: To ensure safe and profitable operations, the uncertainty about the accuracy of flight performance models based on historical flight data needed to be understood and monitored.
Range of applications: The simulation algorithm mapped the variety of UAS applications, from building site inspection to passenger transportation between cities. This drove the design of the algorithm accommodating different flight times, from a few minutes for delivery drones to several hours for air taxis.
In order to meet the requirements of this new technology and to overcome the risks mentioned above, Unisphere drew on its knowledge and experience gained from previous projects such as Solar Impulse — the first-ever global flight accomplished solely by an electrically powered solar airplane. Christoph Schlettig and Michael Anger, the founders and managing directors of Unisphere, played pivotal roles in the flight test, planning, and execution of this historic journey.
Drawing from the gained unique insights, the Unisphere team leveraged this knowledge for the success of the current SIMBA project. Their expertise included a profound understanding of how weather impacted flights at lower altitudes compared to traditional aviation and extensive hands-on experience in operating electric aircraft, a realm closely aligned with Unmanned Aircraft Systems.
NextGen Aviation and Sustainability
Following, unmanned aerial vehicles played a critical role in promoting environmental sustainability through automation and miniaturization. An illustrative example was the use of lightweight drones for the rapid and resource-efficient delivery of medicines, outperforming conventional fossil-fuel-powered vehicles.
In terms of social sustainability, drones were already essential for the safe transport of various goods in regions with poor infrastructure. By transforming pilot knowledge into software, industries without an aviation background could integrate drones and air taxis into their workflows in a cost-effective and automated manner. This promoted macroeconomic impact and social sustainability.
Overall, the SIMBA project contributed to ensuring the safety and efficiency of autonomous aerial systems — drones and air cabs. The project aimed to advance the automation of drones and eVTOL through intelligent software, which would enable non-aviation industries to use UAS in commercial operations. The goal was to solve complex unmanned aerial vehicle problems through the application of the Smart 4D Trajectory. This meant that, for the first time, a user could check the feasibility of a planned flight completely automatically.