Landing Zone Determination for Autonomous Rotorcraft in Surveillance Applications

McLain, Timothy W.; Ellingson, Gary; Mackay, Justin

doi:10.2514/6.2016-1137

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Other researchers proposed edge detection as a basis for candidate landing site identification [2]. Landing site identification from a point-cloud model that can be generated from lidar sensors was also proposed [16].…”

Section: A Emergency Flight Planningmentioning

confidence: 99%

“…Candidate landing areas are then evaluated for risk, and the best one is selected. A similar approach using lidar point-cloud data was proposed to optimize UAS landing site during a tracking mission [16]. An alternate database-centric approach follows the architecture for GIS data use proposed by [1], using a matrix or cell grid with associated cost to represent possible landing areas.…”

Section: A Emergency Landing Site Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Risk to People and Property for Aircraft Emergency Landing Planning

Donato

Atkins

2017

Journal of Aerospace Information Systems

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General aviation and small unmanned aircraft systems are less redundant, may be less thoroughly tested, and are flown at lower cruise altitudes than commercial aviation counterparts. These factors result in a higher probability of a forced or emergency landing scenario. Currently, general aviation relies on the pilot to select a landing site and plan a trajectory, even though workload in an emergency is typically high, and decisions must be made rapidly. Although sensors can provide local real-time information, awareness of more distant or occluded regions requires database and/or offboard data sources. This paper considers different data sources and how to process these data to inform an emergency landing planner regarding risks posed to property, people on the ground, and the aircraft itself. Detailed terrain data are used for selection of candidate emergency landing sites. Mobile phone activity is evaluated as a means of real-time occupancy estimation. Occupancy estimates are combined with population census data to estimate emergency landing risk to people on the ground. Openly available databases are identified and mined as part of an emergency landing planning case study. = total number of country codes n g = total number of grid cells n t = total number of time intervals R a = risk based on landing area R h = risk to people on ground R ls = overall landing site risk R p = risk to property on ground R v = risk to vehicle t = time, min t 10 m = starting time of 10 min time interval, min W h , W p , W v , W a = weights associated with landing site risks w = A transition cost weight Θ = median of maximum aggregated SMS and call activity during a day, for a day of the week λ census = occupancy from census datâ λ census = normalized occupancy from census data λ rt = real-time occupancy estimation λ 0 = A fixed transition cost element Φ = median of aggregated SMS and call activity for each grid cell, day of the week and time interval of the day Φ grid = sum of Φ for all grid cellŝ Φ = normalized Φ ϕ = aggregated SMS and call activitŷ ϕ = normalized ϕ ϕ 1 = activity in terms of SMS-in ϕ 2 = activity in terms of SMS-out ϕ 3 = activity in terms of call-in ϕ 4 = activity in terms of call-out ϕ 5 = activity in terms of internet ϕ i = modified activity feature for heat map plot

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Section: A Emergency Flight Planningmentioning

confidence: 99%

Section: A Emergency Landing Site Identificationmentioning

confidence: 99%

Evaluating Risk to People and Property for Aircraft Emergency Landing Planning

Donato

Atkins

2017

Journal of Aerospace Information Systems

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“…[2][3][4][5] As a consequence of estimated topographical information, a two-dimensional elevation map was thus obtained to comprehend the region flatness by, for instance, least median squares 6 or plane fit. 7 Incorporated with the coordinates about the surrounding, the information is useful for the likely landing site. SFM and related methods enable an imagebased 3-D scene as well as determine the safety landing site, but with the cost of heavy computation.…”

Section: Introductionmentioning

confidence: 99%

Learning-based risk assessment and motion estimation by vision for unmanned aerial vehicle landing in an unvisited area

2019

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We proposed a vision-based methodology as an aid for an unmanned aerial vehicle (UAV) landing on a previously unsurveyed area. When the UAV was commanded to perform a landing mission in an unknown airfield, the learning procedure was activated to extract the surface features for learning the obstacle appearance. After the learning process, while hovering the UAV above the potential landing spot, the vision system would be able to predict the roughness value for confidence in a safe landing. Finally, using hybrid optical flow technology for motion estimation, we successfully carried out the UAV landing without a predefined target. Our work combines a well-equipped flight control system with the proposed vision system to yield more practical versatility for UAV applications.

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“…Once the data is in point-cloud form the ground plane is usually detected using a robust RANSAC (random sample consensus) plane detection algorithm. This approach has been used by many robotic systems ranging from humanoid robots [65] to UAVs (unmanned flying vehicles) [66]. This method can be implemented easily using the open source PCL (point cloud library) [67].…”

Section: Processing Stepsmentioning

confidence: 99%

Autonomous Operation and Human-Robot Interaction on an Indoor Mobile Robot

Robinson¹

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<p>MARVIN (Mobile Autonomous Robotic Vehicle for Indoor Navigation) was once the flagship of Victoria University’s mobile robotic fleet. However, over the years MARVIN has become obsolete. This thesis continues the the redevelopment of MARVIN, transforming it into a fully autonomous research platform for human-robot interaction (HRI). MARVIN utilises a Segway RMP, a self balancing mobility platform. This provides agile locomotion, but increases sensor processing complexity due to its dynamic pitch. MARVIN’s existing sensing systems (including a laser rangefinder and ultrasonic sensors) are augmented with tactile sensors and a Microsoft Kinect v2 RGB-D camera for 3D sensing. This allows the detection of the obstacles often found in MARVIN’s unmodified office-like operating environment. These sensors are processed using novel techniques to account for the Segway’s dynamic pitch. A newly developed navigation stack takes the processed sensor data to facilitate localisation, obstacle detection and motion planning. MARVIN’s inherited humanoid robotic torso is augmented with a touch screen and voice interface, enabling HRI. MARVIN’s HRI capabilities are demonstrated by implementing it as a robotic guide. This implementation is evaluated through a usability study and found to be successful. Through evaluations of MARVIN’s locomotion, sensing, localisation and motion planning systems, in addition to the usability study, MARVIN is found to be capable of both autonomous navigation and engaging HRI. These developed features open a diverse range of research directions and HRI tasks that MARVIN can be used to explore.</p>

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Landing Zone Determination for Autonomous Rotorcraft in Surveillance Applications

Cited by 6 publications

References 13 publications

Evaluating Risk to People and Property for Aircraft Emergency Landing Planning

Evaluating Risk to People and Property for Aircraft Emergency Landing Planning

Learning-based risk assessment and motion estimation by vision for unmanned aerial vehicle landing in an unvisited area

Autonomous Operation and Human-Robot Interaction on an Indoor Mobile Robot

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