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1. Self-Adaptive Mechanisms for Misconfigurations in Small Uncrewed Aerial Systems

   https://doi.org/10.1109/SEAMS59076.2023.00030  

   Salil Purandare; Urjoshi Sinha; Md Nafee Al Islam; Jane Cleland-Huang; Myra B. Cohen (May 2023, IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS))

   Small uncrewed aerial systems, sUAS, provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means. Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight. However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions. To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework, CICADA, for Controller Instability-preventing Configuration Aware Drone Adaptation. CICADA dynamically detects unstable drone behavior during flight and adapts to mitigate this threat. We have built a prototype of CICADA using a popular open source sUAS simulator and experimented with a large number of different configurations. Experimental results show that CICADA’s adaptations reduce controller instability and enable the sUAS to recover from a significant number of poor configurations. In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes. 

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2. Exploration of Failures in an sUAS Controller Software Product Line

   https://doi.org/10.1145/3646548.3672597  

   Purandare, Salil; Cohen, Myra B (September 2024, ACM)

   Small uncrewed aerial systems (sUAS) are growing in their use for commercial, scientific, recreational, and emergency management purposes. A critical part of a successful flight is a correctly tuned controller which manages the physics of the vehicle. If improperly configured, it can lead to flight instability, deviation, or crashes. These types of misconfigurations are often within the valid ranges specified in the documentation; hence, they are hard to identify. Recent research has used fuzzing or explored only a small part of the parameter space, providing little understanding of the configuration landscape itself. In this work we leverage software product line engineering to model a subset of the parameter space of a widely used flight control software, using it to guide a systematic exploration of the controller space. Via simulation, we test over 20,000 configurations from a feature model with 50 features and 8.88 × 1034 products, covering all single parameter value changes and all pairs of changes from their default values. Our results show that only a small number of single configuration changes fail (15%), however almost 40% fail when we evaluate changes to two-parameters at a time. We explore the interactions between parameters in more detail, finding what appear to be many dependencies and interactions between parameters which are not well documented. We then explore a smaller, exhaustive product line model, with eight of the most important features (and 6,561 configurations) and uncover a complex set of interactions; over 48% of all configurations fail. 

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3. Learning from Users: an Elicitation Study and Taxonomy for Communicating Small Unmanned Aerial System States Through Gestures

   https://doi.org/10.1109/hri.2019.8673010  

   Firestone, Justin W.; Quinones, Rubi; Duncan, Brittany A. (March 2019, 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI))

   This paper presents a gesture set for communicating states to novice users from a small Unmanned Aerial System (sUAS) through an elicitation study comparing gestures created by participants recruited from the general public with varying levels of experience with an sUAS. Previous work in sUAS flight paths sought to communicate intent, destination, or emotion without focusing on concrete states such as Low Battery or Landing. This elicitation study uses a participatory design approach from human-computer interaction to understand how novice users would expect an sUAS to communicate states, and ultimately suggests flight paths and characteristics to indicate those states. We asked users from the general public (N=20) to create gestures for seven distinct sUAS states to provide insights for human-drone interactions and to present intuitive flight paths and characteristics with the expectation that the sUAS would have general commercial application for inexperienced users. The results indicate relatively strong agreement scores for three sUAS states: Landing (0.455), Area of Interest (0.265), and Low Battery (0.245). The other four states have lower agreement scores, however even they show some consensus for all seven states. The agreement scores and the associated gestures suggest guidance for engineers to develop a common set of flight paths and characteristics for an sUAS to communicate states to novice users. 

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4. A sensorless drone-based system for mapping indoor 3D airflow gradients: demo abstract

   https://doi.org/10.1145/3498361.3538671  

   Liu, Yanchen; Zhao, Minghui; Xia, Stephen; Wu, Eugene; Jiang, Xiaofan (June 2022, Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services (MobiSys '22))

   With the global spread of the COVID-19 pandemic, ventilation indoors is becoming increasingly important in preventing the spread of airborne viruses. However, while sensors exist to measure wind speed and airflow gradients, they must be manually held by a human or an autonomous vehicle, robot, or drone that moves around the space to build an airflow map of the environment. In this demonstration, we present DAE, a novel drone-based system that can automatically navigate and estimate air flow in a space without the need of additional sensors attached onto the drone. DAE directly utilizes the flight controller data that all drones use to self-stabilize in the air to estimate airflow. DAE estimates airflow gradients in a room based on how the flight controller adjusts the motors on the drone to compensate external perturbations and air currents, without the need for attaching additional wind or airflow sensors. 

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5. Unified Co-Simulation Framework for Autonomous UAVs

   https://doi.org/10.1145/3569951.3597544  

   Palaniappan, Sri Ranganathan; Pateel, Varun; Jijina, Sam; Young, Jeffrey; Kim, Hyesoon (July 2023, PEARC '23: Practice and Experience in Advanced Research Computing)

   Autonomous drones (UAVs) have rapidly grown in popularity due to their form factor, agility, and ability to operate in harsh or hostile environments. Drone systems come in various form factors and configurations and operate under tight physical parameters. Further, it has been a significant challenge for architects and researchers to develop optimal drone designs as open-source simulation frameworks either lack the necessary capabilities to simulate a full drone flight stack or they are extremely tedious to setup with little or no maintenance or support. In this paper, we develop and present UniUAVSim, our fully open-source co-simulation framework capable of running software-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulations concurrently. The paper also provides insights into the abstraction of a drone flight stack and details how these abstractions aid in creating a simulation framework which can accurately provide an optimal drone design given physical parameters and constraints. The framework was validated with real-world hardware and is available to the research community to aid in future architecture research for autonomous systems. 

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