A Modular Algorithm for Exhaustive Map Searching Using Occupancy Based Maps

Lum, Christopher W.; Vagners, Juris

doi:10.2514/6.2009-1839

Cited by 14 publications

(16 citation statements)

References 26 publications

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“…However, unlike previous results [9] where guarantees are shown for all agents in the team, most of the guarantees for the case of explicit cooperation can be shown only for the case of i = I. Theorem 3.2: Under assumptions A.3, A.4, A.5, and the previously described search strategy with explicit cooperation, if agent I is not currently at the point z HI the agent must move to a point other than z agtI once the quantity αx w (k, z agtI ) decreases below the value δ.…”

Section: Exhaustive Searching and Strategic Collision Avoidancecontrasting

confidence: 80%

“…It can be shown that the scores of each cell either monotonically increase or decrease with each sensor measurement for the majority of values of h and s k−1 [9].…”

Section: B Updating Map Cell Scoresmentioning

confidence: 97%

“…Previous work investigated formulating a search policy where agents had no explicit knowledge of each other [9]. Without explicit cooperation, there are scenarios where two or more agents might choose the same location to search.…”

Section: Search Strategymentioning

confidence: 99%

“…Groups such as Fox et al [8] have looked at generating searching algorithms with the ideas of exploration and map building in mind. Previous work at the University of Washington [9,10] These methods, while effective for their respective applications, contain shortcomings when addressing the search problem considered in this application. To address this, a search policy is presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Partitioned searching and deconfliction: Analysis and flight tests

Lum

Vagners

Jang

et al. 2010

Proceedings of the 2010 American Control Conference

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Searching a complex environment for a hidden target is a common problem encountered by many autonomous systems. Many modern autonomous systems use a hierarchical structure for mission management where different algorithms perform different tasks to give agents desired behavior. This work investigates a search policy that guarantees both an exhaustive search of the map and conflict free paths of all agents. Agents formulate control decisions for a fixed number of time steps using a modular algorithm that allows parameterizations of agent capabilities. High fidelity simulation and flight test data using multiple autonomous vehicles are used to verify and validate the algorithms in real time.

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Section: Exhaustive Searching and Strategic Collision Avoidancecontrasting

confidence: 80%

“…It can be shown that the scores of each cell either monotonically increase or decrease with each sensor measurement for the majority of values of h and s k−1 [9].…”

Section: B Updating Map Cell Scoresmentioning

confidence: 97%

Section: Search Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Partitioned searching and deconfliction: Analysis and flight tests

Lum

Vagners

Jang

et al. 2010

Proceedings of the 2010 American Control Conference

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“…The subsequent class in 2015 expended on this idea and build custom UAS designed to obtain high-resolution imagery of an area using multi-spectral cameras [13]. Many of these topics are aligned with the faculty's own research programs [14], [15], [16], [17] thereby allowing students to continue these projects when the return to their home university by becoming involved as undergraduate researchers in the faculty's research groups or laboratories. Research topics that the students undertake during this class is equivalent to a quarter long undergraduate research project.…”

Section: Figure 1: Students From the 2014 Class Gathering Experimentamentioning

confidence: 99%

Expanding Diversity in STEM: Developing International Education and Research Partnerships in a Global Society

Lum¹,

Gardner²,

Jordan³

et al.

2016 ASEE Annual Conference &Amp; Exposition Proceedings

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includes coordinated multi-vehicle searching, automatic target recognition, formation flight of swarms of vehicles, risk assessment of UAS in the national airspace, collision avoidance/deconfliction, and UAS flight operations. He has worked closely with industry partners such as the Boeing Company, Insitu, Aerovel, Hood Technology, and the Washington Joint Center for Aerospace Technology Innovation to implement academic technologies onto deployed platforms. Dr. Lum teaches both undergraduate and graduate course on automatic control, flight mechanics, modeling and simulation, mathematical tools for engineers, sensors and actuators, and other controls related courses. He has been awarded the department's "Instructor of the Year" award twice (2012 and 2013). He is also the faculty advisor to the department's design, build, fly team. He has served as an adjunct professor at Seattle University and as a visiting fellow at

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Conservative algorithms for automated collision awareness for multiple Unmanned Aerial Systems

Ueunten

Lum

Creigh

et al. 2015

2015 IEEE Aerospace Conference

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As the Federal Aviation Administration (FAA) prepares to integrate Unmanned Aerial Systems (UAS) into the National Airspace System (NAS), developing technologies that mitigate the risk associated with UAS collisions have become a top priority. Despite advances in detect and avoid technologies, the UAS operator remains the primary controller responsible for maintaining inter-vehicle separation and ensuring conflicts do not occur. This paper examines a collision awareness system which increases the operator's situational awareness by spatially and temporally predicting conflicts between the UAS and entities such as other aviation traffic or restricted airspaces. By modeling entities as 3D point masses, the system can be implemented for various, dissimilar UASs. Furthermore, the system supports aircraft engaged in different flight modes such as free flight, following a flight path, and orbit/loiter behavior. Mixed Gaussian distributions model each entity's future position, where the mean is determined by 3D kinematic motion and the covariance is determined by a continuous time error propagation model. Convolving these mixed distribution with another entity or airspace yields mathematically conservative future conflict estimates. Scenarios are presented to demonstrate the algorithm's capabilities.

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A Modular Algorithm for Exhaustive Map Searching Using Occupancy Based Maps

Cited by 14 publications

References 26 publications

Partitioned searching and deconfliction: Analysis and flight tests

Partitioned searching and deconfliction: Analysis and flight tests

Expanding Diversity in STEM: Developing International Education and Research Partnerships in a Global Society

Conservative algorithms for automated collision awareness for multiple Unmanned Aerial Systems

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