Many machine intelligence algorithms have been developed in support of using a UAV to assist Wilderness Search and Rescue operations. Examples include autonomous UAV flights (launch, land, maintaining height-above-ground, automatic return, etc.), predicting likely places to find the missing person, automatic path planning, anomaly detection, and etc. Due to the complexity of the algorithms and the special knowledge involved, we cannot expect real searchers to understand how autonomous components actually work. However, in order to really make these technology useful in WiSAR operations, the real searchers need to be able to manage autonomy and incorporate their domain knowledge and expertise with the autonomous components of the UAV system.

My research focuses on how real searchers can manage autonomy by managing information provided to the intelligent UAV system at different resolutions and phases of the operation. User interface elements at different resolutions and phases allow the real searcher to evaluate and manage information provided to autonomous components and easily understand how the autonomous behaviors of the system will pan out following easily identifiable and understandable causal effects.

• Modeling Terrain-based Lost Person Behavior – Using publicly available terrain data, we built a Bayesian model to predict how terrain features (such as topography, vegetation density and elevation difference) might affect a lost person's behavior. This model allows the searcher to incorporate his/her uncertainly in the prior estimates of lost person's transitional probabilities between two terrain features. It also allows the searcher to incorporate past human behavior data in the form of GPS track logs to generate posterior predictive probability distribution. We are presently working on extending this model to include intended destination and trail following factors and then use geocacher GPS track logs as observed human behavior data in our model. We are also developing user interfaces that allow the searcher to easily view and modify transitional probabilities in the form of Beta Distributions. — TBMod ParamMod

• Gesture-based Probability Distribution Modification Tool – Once the system produces a probability distribution of the likely places to find the missing person based on general trend prediction, the searcher needs to be able to modify the probability distribution based on the very specific case scenario at hand. The searcher would have better information about the profile of the missing person and other additional information such as the season of the year, time of the day, and weather conditions. The tool we are building would allow the searcher to use simple gestures (eventually using a touch-screen monitor) to modify the suggested probability distribution by raising, lowering, and erasing probability distribution hills. This tool is not only useful in the planning phase, it can also be useful in the execution phase when new evidences are found that might change the probability distribution. — DistMod

• Probability of Detection Map Painting Tool – Although the UAV can provide aerial video support, if an object is in the video frames, it doesn't mean the searcher will always spot it due to various factors such as vegetation density, weather conditions, and fatigue. This tool allows the searcher to paint a probability of detection map for the search region marking areas where probability of detection might be low. This way the searcher can provide more information to the intelligent system, and the intelligent path planning algorithms can take the probability of detection factor into account accordingly when planning flight paths for the UAV. — DiffMod

• Intelligent Path Planning Algorithms – Given a probability distribution of the likely places of finding the missing person, what path should a UAV follow to maximize the amount of probability collected? We have developed path planning algorithms that combine various techniques (such as Local Hill Climbing, Potential Fields, Global Warming Effect, and Evolutionary Algorithms) to produce a path from specified starting position (to an optional ending position) for a fixed flight duration. We tested the algorithms on various type of probability distributions (uni-modal, bi-modal, multi-modal, uniform, and path) and were able to plan paths that exceed 95% efficiency. We are currently working on speeding up the path planning by performing coarse-to-fine search in the Global Warming dimension. We are also investigating how well our algorithms work with partial detection rate and also the additional time dimension (planning path for a moving object/changing distribution). — IPPA

• Sliding Autonomy Intensity Control – During the execution phase when using a UAV to support Wilderness Search and Rescue, the searcher can plan the UAV search path strategically by specify way points in different region of the search area. Intelligent path planning algorithms are then used to plan flying path tactically between two consecutive way points based on the given probability distribution of the likely places to find the missing person. The searcher can using a slider to control how much time to grant the UAV between two consecutive way points. This controlling mechanism enables the searcher to control the search intensity at the local region. Real-time feedback allows the searcher to see what search paths are generated and then select the path he/she likes the most. — SlideMod

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• TBMod
• ParamMod
• DiffCreate
• DistMod
• DiffMod
• IPPA
• SlideMod
• Other

I have too many hobbies. Some of the leading ones are soccer, billiards, music improvisation, and martial arts. In my spare time (if I can squeeze out any), I translate Chinese martial arts novels into English and also maintain a personal blog because I believe that good things should be shared. Welcome to check them out:

• Translations: http://www.lannyland.com
• Lanny's Blog: http://blog.lannyland.com

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L. Lin and M. A. Goodrich. A Bayesian Approach to Modeling Lost Person Behaviors Based on Terrain Features in Wilderness Search and Rescue. In Computational and Mathematical Organization Theory 2010 Special Issue.

L. Lin, M. Roscheck, M. A. Goodrich, and B. S. Morse. Supporting Wilderness Search and Rescue with Integrated Intelligence: Autonomy and Information at the Right Time and the Right Place . In Twenty-Fourth AAAI Conference on Artificial Intelligence, Special Track on Integrated Intelligence. July, 2010, Atlanta, Georgia, USA.

L. Lin and M. A. Goodrich. A Behavior-based Interactive Learning Approach for HRI: Teaching a Robot New Tricks. In Technical Report of the HRI 2010 Young Pioneers Workshop. Osaka, Japan. March, 2010.

L. Lin and M. A. Goodrich. UAV Intelligent Path Planning for Wilderness Search and Rescue, In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Oct, 2009 St. Louis, Missouri, USA.

L. Lin and M. A. Goodrich. A Bayesian Approach to Modeling Lost Person Behaviors Based on Terrain Features in Wilderness Search and Rescue. In Proceedings of the 18th Conference on Behavior Representation in Modeling and Simulation. Sundance, Utah, USA. March 31-April 2, 2009. pp. 49-56.

J. W. Crandall, M. A. Goodrich, and L. Lin. Encoding Intelligent Agents for Uncertain, Unknown, and Dynamic Tasks: From Programming to Interactive Artificial Learning. In Proceedings of AAAI Spring Symposium: Agents that Learn from Human Teachers. March, 2009. Stanford, California

L. Lin UAV Intelligent Path Planning for Wilderness Search and Rescue. Master Thesis, Brigham Young University, 2009, Provo, Utah, USA. AND RESCUE