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Ron Doty sits down at a wagering machine at Wyoming Downs in early 2021. The Wyoming Supreme Court affirmed a district judge's earlier ruling that the Campbell County Commission overstepped its authority with a horse racing resolution that it passed in 2021, giving 307 Horse Racing exclusive rights to off-track betting in the county.

The Wyoming Supreme Court sided with the district judge who ruled that the Campbell County Commission overstepped its authority with a horse racing resolution that it passed in 2021 that essentially gave 307 Horse Racing exclusive rights to off-track betting in the county and forced the closure of a few businesses.

In March 2022, District Judge F. Scott Peasley of Douglas ruled that the commissioners exceeded their authority by passing a resolution that gave the live horse racing operator control over off-track betting and simulcasting in the county.

This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the 2019 AlphaPilot Challenge. Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to \\({8}\\,{\\hbox {m}/\\hbox {s}}\\) and ranked second at the 2019 AlphaPilot Challenge.

In order to push the capabilities and performance of autonomous drones, in 2019, Lockheed Martin and the Drone Racing League have launched the AlphaPilot ChallengeFootnote 1\\(^{,}\\)Footnote 2, an open innovation challenge with a grand prize of $1 million. The goal of the challenge is to develop a fully autonomous drone that navigates through a race course using machine vision, and which could one day beat the best human pilot. While other autonomous drone races Moon et al. (2017, 2019) focus on complex navigation, the AlphaPilot Challenge pushes the limits in terms of speed and course size to advance the state of the art and enter the domain of human performance. Due to the high speeds at which drones must fly in order to beat the best human pilots, the challenging visual environments (e.g., low light, motion blur), and the limited computational power of drones, autonomous drone racing raises fundamental challenges in real-time state estimation, perception, planning, and control.

To overcome this difficulty, several approaches exploiting the structure of drone racing with gates as landmarks have been developed, e.g., Li et al. (2019); Jung et al. (2018); Kaufmann et al. (2018), where the drone locates itself relative to gates. In Li et al. (2019), a handcrafted process is used to extract gate information from images that is then fused with attitude estimates from an inertial measurement unit (IMU) to compute an attitude reference that guides the drone towards the visible gate. While the approach is computationally very light-weight, it struggles with scenarios where multiple gates are visible and does not allow to employ more sophisticated planning and control algorithms which, e.g., plan several gates ahead. In Jung et al. (2018), a convolutional neural network (CNN) is used to retrieve a bounding box of the gate and a line-of-sight-based control law aided by optic flow is then used to steer the drone towards the detected gate. While this approach is successfully deployed on a real robotic system, the generated control commands do not account for the underactuated system dynamics of the quadrotor, constraining this method to low-speed flight. The approach presented in Kaufmann et al. (2018) also relies on relative gate data but has the advantage that it works even when no gate is visible. In particular, it uses a CNN to directly infer relative gate poses from images and fuse the results with a VIO state estimate. However, the CNN does not perform well when multiple gates are visible as it is frequently the case for drone racing.

The advantages of the proposed system are (i) a drift-free state estimate at high speeds, (ii) a global and consistent gate map, and (iii) a real-time capable near time-optimal path planner. However, these advantages could only partially be exploited as the races neither included multiple laps, nor had complex segments where the next gates were not directly visible. Nevertheless, the system has proven that it can handle these situations and is able to navigate through complex race courses reaching speeds up to \\({8}\\,{\\hbox {m}/\\hbox {s}}\\) and completing the championship race track of \\({74}\\,{\\hbox {m}}\\) in \\({11.36}\\,{\\hbox {s}}\\).

While the 2019 AlphaPilot Challenge pushed the field of autonomous drone racing, in particularly in terms of speed, autonomous drones are still far away from beating human pilots. Moreover, the challenge also left open a number of problems, most importantly that the race environment was partially known and static without competing drones or moving gates. In order for autonomous drones to fly at high speeds outside of controlled or known environments and succeed in many more real-world applications, they must be able to handle unknown environments, perceive obstacles and react accordingly. These features are areas of active research and are intended to be included in future versions of the proposed drone racing system.

Buttons to access the online ASSIST system or to download application forms are available in Part 1 of this FOA. See your administrative office for instructions if you plan to use an institutional system-to-system solution.

Investigators should describe the strongest study design that can evaluate the effects of the intervention program with high internal validity, taking into account external validity and generalizability. In general, this would be a randomized controlled trial (RCT) or a hybrid trial design. Most studies proposed for this announcement may require randomization at the group level (Group Randomized Trial, or GRT) to match the level of intervention and to minimize or prevent contamination of the comparison group. In GRTs, groups of participants are randomized to study conditions and interventions are delivered to members of those groups, often defined by their workplace, school, primary care provider, or other organization entity. Interactions occur among participants in the same group or cluster both pre- and post-randomization. The statistical analysis and power calculations should reflect the design and the implications of the study design choice on the methods required for data analysis and sample size estimation. Standard methods are appropriate for RCTs, but adaptations are required for GRTs to reflect the positive intraclass correlation expected in data obtained from participants in the same groups or clusters. The methods proposed for data analysis and the methods used for sample size calculation should reflect both the extra variation expected in the data and the degrees of freedom available to estimate that extra variation. For more guidance, please visit this resource developed by NIH on GRTs. GRTs are often used for pragmatic trials. R21/R33 applications do not need to be powered to assess differences in physical activity since the goal of this FOA is on developing the multi-level intervention, and not on testing efficacy. If proposing a GRT, applications should be testing the feasibility of group randomization.

Welcome to Episode 307 of the podcast. Listen and access the show notes below or search for the Carey Nieuwhof Leadership Podcast on Apple Podcasts or wherever you get your podcasts and listen for free.

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