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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. 

Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous positionnavigation-time tasks within stringent onboard weight and power constraints. We observe that reducing onboard operating voltage can benefit the energy efficiency of both the computation and flight mission, however, it can also result in on-chip bit failures that are detrimental to mission safety and performance. To this end, we propose BERRY, a robust learning framework to improve bit error robustness and energy efficiency for RL-enabled autonomous systems. BERRY supports robust learning, both offline and on-board the UAV, and for the first time, demonstrates the practicality of robust low-voltage operation on UAVs that leads to high energy savings in both compute-level operation and systemlevel quality-of-flight. We perform extensive experiments on 72 autonomous navigation scenarios and demonstrate that BERRY generalizes well across environments, UAVs, autonomy policies, operating voltages and fault patterns, and consistently improves robustness, efficiency and mission performance, achieving up to 15.62% reduction in flight energy, 18.51% increase in the number of successful missions, and 3.43× processing energy reduction. 

The next ubiquitous computing platform, after personal computers and smartphones, is likely one of the autonomous natures, such as drones, robots, and self-driving cars, which have moved from mere lab concepts to permeating almost every aspect of our soci- ety [16, 20, 25]. Behind the proliferation of autonomous machines is the critical need to ensure reliability [7, 22–24]. Almost every vendor, be it in the software, hardware, or systems segment, has to conform to functional safety standards when shipping products for automotives. Today’s resiliency solutions to autonomous machines, however, all make fundamental trade-offs between resiliency and cost, which manifests as high overhead in performance, energy, and chip area. For instance, hardware modular redundancy provides high safety but more than doubles the area and energy cost [1]. The reason is that today’s solutions are of the “one-size-fits-all” nature: they use the same protection scheme throughout the entire computing stack of autonomous machines. As a result, they have to accommodate the least robust component, leading to a high protection overhead. The insight of this paper is that for a resiliency solution to pro- vide high protection coverage while introducing little cost, we must exploit the inherent robustness variations in the domain-specific autonomous machine computing. In particular, we show that the different autonomous machine kernels differ significantly in their inherent robustness and performance. Building on top of that, we propose a Vulnerable-Proportional Protection (VPP) design paradigm, in which the protection budget, be it spatially (e.g., modular re- dundancy) or temporally (e.g., re-execution), should be inversely proportional to the inherent robustness of a task in the autonomous machine system. In stark contrast to the existing “one-size-fits-all” strategy, VPP wisely allocates the protection budget, thus achieving the same protection coverage with little overhead, which provides a blueprint design paradigm towards reliable autonomous machines