Framework for developing and evaluating MAV control algorithms in a realistic urban setting

Orr, M.W.; Rasmussen, Steven J.; Karni, Etan D.; Blake, William

doi:10.1109/acc.2005.1470619

Cited by 42 publications

(34 citation statements)

References 4 publications

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“…2 A layout of the general concept of operations is shown in Figure 1 below. [2] The SAV will be launched from a forward area and fly over a city, town, or other area of interest. The SAV will act as the "mother ship" and carry the MAVs.…”

Section: Counter Program Descriptionmentioning

confidence: 99%

Cooperative operations in urban terrain (COUNTER)

Gross

Rasmussen

Chandler³

et al. 2006

SPIE Proceedings

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The Air Force Research Laboratory (AFRL) has an ongoing investigation to evaluate the behavior of Small Unmanned Aerial Vehicles (SAVs) and Micro Aerial Vehicles (MAVs) flying through an urban setting. This research is being conducted through the Cooperative Operations in UrbaN TERrain (COUNTER) 6.2 research and flight demonstration program. COUNTER is a theoretical and experimental program to develop the technology needed to integrate a single SAV, four MAVs, and a human operator for persistent intelligence, reconnaissance and surveillance for obscured targets in an urban environment. The research involves development of six-degree-of-freedom models for integration into simulations, modeling and integration of wind data for complex urban flows, cooperative control task assignment and path planning algorithms, video tracking and obstacle avoidance algorithms, and an Operator Vehicle Interface (OVI) system. The COUNTER concept and the contributing technologies will be proven via a series of flight tests and system demonstrations.The first of six planned COUNTER flight demonstrations occurred in July of 2005. This demonstration focused on the simultaneous flight operations of both the SAV and the MAV while displaying their respective telemetry data on a common ground station (OVI). Current efforts are focused on developing the architecture for the Cooperative Control Algorithm. In FY 2006, the COUNTER program will demonstrate the ability to pass vehicle waypoints from the OVI station to the SAV and MAV vehicles. In FY 2007, the COUNTER program will focus on integrating solutions to the optical target tracking (SAV) and obstacle avoidance (MAV) issues into the capabilities of the OVI station and Cooperative Control Algorithm.

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Section: Counter Program Descriptionmentioning

confidence: 99%

Cooperative operations in urban terrain (COUNTER)

Gross

Rasmussen

Chandler³

et al. 2006

SPIE Proceedings

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“…Orr et al [9], [10] used a computational fluid dynamics (CFD) solver to generate static wind fields and assess the waypoint following performance of a fixed-wing UAV. Another effort was made by Galway [11], [12] to simulate an autonomous helicopter in a sparse urban environment.…”

Section: Related Workmentioning

confidence: 99%

An analysis of wind field estimation and exploitation for quadrotor flight in the urban canopy layer

Ware

Roy

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Although unmanned air vehicles' increasing agility and autonomy may soon allow for flight in urban environments, the impact of complex urban wind fields on vehicle flight performance remains unclear. Unlike synoptic winds at high altitudes, urban wind fields are subject to turbulence generated by the buildings and terrain. The resulting spatial and temporal variation makes inference about the global wind field based on local wind measurements difficult and prevents the use of most simple wind models. Fortunately, the structure of the urban environment provides exploitable predictability given a suitable computational fluid dynamics solver, a representative 3D model of the environment, and an estimate of the expected prevailing wind speed and heading. The prevailing wind speed and direction at altitude and computational fluid dynamics solver can generate the corresponding wind field estimate over the map. By generating wind fields in this way, this work investigates a quadrotor's ability to exploit them for improved flight performance. Along with the wind field estimate, an empirically derived power consumption model is used to find minimum-energy trajectories with a planner both aware of and naive to the wind field. When compared to minimum-energy trajectories that do not incorporate wind conditions, the wind-aware trajectories demonstrate reduced flight times, total energy expenditures, and failures due to excess air speed for trajectories across MIT campus.

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“…Let (1) be the set of targets designated, by a human operator, to the MAVs and (2) be the set of homogeneous MAVs servicing these targets.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The COUNTER concept and the contributing technologies are proven via a series of flight tests and system demonstrations. Details regarding the program are outlined in [1] and are briefly described next for the sake of completeness. The purpose of this staged system is to get time-sensitive information unobtainable by other means.…”

Section: Introductionmentioning

confidence: 99%

Assigning Micro UAVs to Task Tours in an Urban Terrain

Shima

Rasmussen²,

Gross³

2007

IEEE Trans. Contr. Syst. Technol.

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An optimization algorithm for assigning in realtime multiple unmanned aerial vehicles (UAVs) to task tours is presented and tested as part of a flight demonstration program. The scenario of interest is one where multiple microaerial vehicles are launched from a small UAV in order to investigate selected targets in an urban terrain. For path planning, we use the Dubin's car model so that the vehicles' dynamic constraint of minimum turning radius is taken into account. Due to the prohibitive computational complexity of the coupled path optimization and assignment problem, we solve the problem by ordering a set of tasks based on the Euclidean distance, utilizing a traveling salesman problem solver. We apply upper and lower bounding procedures iteratively on active subsets within the set of feasible group assignments, enabling efficient search of the solution space. The online implementation of the algorithm is discussed and simulation results confirm the efficiency of the proposed algorithm. Results from recent flight tests are also provided.

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Framework for developing and evaluating MAV control algorithms in a realistic urban setting

Cited by 42 publications

References 4 publications

Cooperative operations in urban terrain (COUNTER)

Cooperative operations in urban terrain (COUNTER)

An analysis of wind field estimation and exploitation for quadrotor flight in the urban canopy layer

Assigning Micro UAVs to Task Tours in an Urban Terrain

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