September 27, 2023
Along with electrification, autonomy is a major trend in various industries ranging from manufacturing and industrial equipment to automotive and aviation. In fact, the global autonomous aircraft market was estimated at $4.56 billion in 2019 and projected to exceed $16 billion by 2027. Accordingly, advanced air mobility (AAM) companies are developing autonomous aircraft designs to move people and cargo between places more effectively. Though the terms are often used interchangeably, urban air mobility (UAM) and regional air mobility (RAM) are subsets of AAM, which focus on air transport at lower altitudes in urban and suburban areas, respectively.
Unsurprisingly, building safe, autonomous AAM systems requires complex training, engineering, development, and design. Artificial intelligence/machine learning (AI/ML) lends significant assistance to these areas by helping engineers and designers develop critical perception and decision-making functions, which are fundamental to autonomy. However, challenges — and concerns — arise around AI/ML’s inability to provide realistic representative situations in the training and validation of these autonomous functions.
Simulation offers incredible value in helping to build confidence in autonomous AAM systems, ensure their reliability, and validate their safety. In early stages, simulation provides critical insight, predictive accuracy, and thorough analyses to inform training and development. In later stages, simulation provides realistic environments and scenarios to validate and test these functions. By integrating Ansys’ solutions, AAM companies can adopt a seamless, end-to-end workflow to optimize training and validation using simulation and digital mission engineering tools for safety analysis, embedded software, sensor testing, and more.
There are both new and classical applications of autonomy in aviation. New applications center around next-generation AAM transport, which consists of vehicles that are typically small, highly automated, and carry passengers or cargo at lower altitudes. Generally, these systems, particularly UAM systems, rely on technologies such as helicopters or emerging technologies such as electric vertical takeoff and landing (eVTOL).
In contrast, classical applications are implemented in existing systems. For example, commercial aircraft manufacturers might incorporate autonomy to increase situational awareness for pilots, alleviate pilots’ responsibilities and workload, or optimize the efficiency of various flight phases. Similarly, military aircraft providers may consider autonomy to assist pilots in handling unexpected changes during a mission, such as new targets or degraded conditions.
Typically, an autonomy application must comprise three main capabilities, which influence each other as follows:
In effect, an autonomous system needs to establish reliable perception and decision-making capabilities before it can successfully execute actuation. Simulation brings significant value to both of these areas. For perception training, physics-based simulation provides raw sensor data and ground truth information, which eliminates the need for complex image processing, reduces training time, and increases accuracy. For decision-making training, simulation offers sensitivity, robustness, and reliability analyses, which help to strengthen flight performance, the safety of flight maneuvers, and collision avoidance.
Simulation also improves reinforcement learning (RL), which is an AI/ML training technique that enables a model to learn on its own by trial and error. In other words, unlike supervised learning or non-supervised learning, RL enables the AI/ML agent to learn interactively through feedback from its environment, including its own actions and experiences within that environment. For this reason, simulation greatly supports RL training by providing an opportunity to create diverse and near-countless simulated environments, which in turn improves the quality of perception and decision-making training.
Ansys provides a complete model-based systems engineering (MBSE) workflow to assist in the training and validation of autonomous functions, including simulation, system architecture, sensor testing, safety assessment, and operational design domain (ODD), as well as scenario creation, variation, and results analytics.
First, let’s get to know the key tools used in this workflow:
Now, let’s explore a sample implementation of this workflow in six steps:
By integrating some — or all — portions of the sample Ansys workflow outlined above, engineers and designers in the aviation industry are developing and validating safer and more reliable autonomous systems.
In one case study, an aircraft manufacturer is integrating Ansys solutions to ensure collision avoidance of unmanned aerial vehicles (UAV). An automated eVTOL must fly to a waypoint while avoiding collision with obstacles. Already confident in the aircraft’s perception capabilities, this team is most concerned with the eVOTL’s decision-making skills to determine its best flight path.
In another example, an aviation unit is adopting a similar Ansys workflow to conduct formation flying. A fleet of four automated eVTOLs must fly in formation following a piloted eVTOL. This example is concerned with both perception (detecting the lead eVTOL and other vehicles) and decision-making (following the lead eVTOL while avoiding any collisions).
Ansys’ simulation solutions enable customers to safely train, test, and validate critical AAM applications, building confidence around AI/ML-assisted software and autonomy in embedded systems. Further, by combining Ansys’ high-fidelity simulation and digital mission engineering tools, customers can develop and validate these systems within a realistic and time-dynamic 3D environment.
To learn more about Ansys’ autonomy solutions, register for the on-demand webinar “Ansys Autonomy: Automating Embedded Planning and Controls via Model-based Software Solutions.”
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