Author Brad Rogers, Andrew Dalia The Center for Self-Organizing and Intelligent Systems (CSOIS)

1. Algorithmic Automation of Spy-bot Infiltration on Pervasive Multiple Agent Cyber Physical Intruder Detection and Defense System Configurations Author Brad Rogers, Andrew Dalia The Center for Self-Organizing and Intelligent Systems (CSOIS) Utah State University, Logan, UT, USA Phone: (801)884-9235 Rogerb1@spu.edu, dalia@cooper.edu Path Safe Abstract We establish an algorithmic solution to the problem of bypassing multiple agent intruder defense and detection systems. Mathematical models were utilized in developing the automated behavior of a single intelligent agent (spy-bot) to behave with stealth in the presence multiple antagonizing agents. Immersed in a field containing a collection of mobile and stationary IDS agents, the spy-bot was capable of reaching a predetermined goal while remaining undetected in the presence of the IDS given that the IDS configuration did not yield an impossible path to the goal. Encoder logic and measurements were carefully analyzed and utilized in conjunction with the mathematical movement models to optimize movement along a safe path with reliable accuracy and velocity. Key terms—Intelligent system, Improved Controls, Intruder Defense, Multi-Agent, Detection Avoidance • This displays a general setup with the position and variables. Based on these the expected values for r in the range from the starting point ra to the point at which the antagonizing IDS agent’s line of view is at a closest intersection with the spy-bot’s line of travel (rc). For our setup r was specifically chosen to be a percentage of the path traveled in the presence of such a defender agent such that it would be positive before reaching the point of intersection (rc). While traveling along the path it would decrease such that r would be 0 at rc. Encoders To demonstrate the effectiveness of the improved encoder model over the older model, we obtained a snapshot from a video capture in which we ran two agents with the same configurations and chassis type side by side Old Encoder Model: This r value was chosen because it allowed us to quickly categorize the defender bots. This allowed us to focus on the closest defenders that were between the Spy-bot and the goal. ra > r > 0 New Encoder Model: where α – constantη - encoder pulse valueσ - width of the chassisdθ - chassis angle change Path Logic 0 > r > rb The distance from the point of the defender bot to the closest point on the spy-bot’s line of travel was determined using: The green area represents the error of agent’s path from its intended path and from the figure it is clear that the improved encoder model resulted in much less error than the older encoder model. For a quantitative comparison, we performed a pixel count evaluation of a binary bitmap version of the above image using the ImageJ open-source software. These pixel counts are in the table above. The error was reduced by more than half. This is the program flowchart used for the spy bots navigation. Using this the Spy-bot is capable of infiltrating most intruder defense configurations without being detected. Controls The rc point (xc, yc) determined from using equations below were used to find the angle from the defender agent’s line of sight to the rc point as calculated in the equation below. This was significant in determining if the spy-bot would be approaching a zone of detection since a small angle would mean that the defender agent is directly facing rc whereas a large angle would mean that the path is safe since the zone of detection is not along the path. Using the improved encoders we were able to tune the controls of the chassis, resulting in the improved travel. This improvement was crucial for the faster chassis that the Spy-bot would use. With the improved encoders we were able to increase the PI parameters without compromising stability. Original : Kp= 25 Ki = .08 Optimized : Kp= 100 Ki = .9 Motor Duty Output Speed Control: Optimized Original If we allow θ to be the angle from the defender agent’s line of view to rc and φ to be the defender agent’s angle as determined on the pGPS Cartesian coordinate system, the resulting equation for θ is: Intruder Defense System (IDS) When put together this allows us to quickly analyze a straight path and determine if any defenders would interfere, and which defender would interfere first. Goal: To detect and capture spy-bot while avoiding collision using IR sensors An intelligent IDS was implemented using way pointing based behavior with specific avoidance reaction mechanisms, yielding a psuedo stochastic mobilization process Capture Mode Process: 1. IDS witness stops upon detecting spy-bot with IR sensors2. IDS witness sends message to stop spy-bot and signal other IDS agents 3. Other IDS agents approach spy-bot and stop when in proximity Applications and Future Work Spy-bot allows for a low-cost, efficient, and unmanned approach to surveillance, surveying, and intrusion. Some possible applications: - Military: stealth, surveillance, communications - Self-parking garage: obstacle avoidance and mobile agent detection ability - Police: centralized patrol coordination, stealth-based tracking algorithms MAS-net Platform (Agent Environment) Programming the agents was performed through 2.4 GHz MICAz microcontroller motes Robots perform tasks on Plexiglas platform pGPS camera monitors robot position Future work with the spy-bot could involve: -The application of potential field based analysis and generation of paths -Using Voronoi tessellations to find mass centers and avoid areas of high density cell localization in multiple agent fields Spy-bot agent (1) is on path to the goal, but since the path safety check failed due to the presence of IDS agent (2), it checks if the area behind agent (2) is safe. The check fails due to the presence of IDS agent (3). Spy-bot agent (1) performs its function to check if the opposite path side is safe. Since this check passes it sets a temporary destination far enough in front of agent (2) to avoid detection Camera info is relayed to Base Station computer and displayed in Robot Commander The pGPS relays messages with the base station. Poster based on a research paper by Andrew Dalia and Brad Rogers under thedirection of Dr. YangQuan Chen. This research, which was supported by the NSF Grant Award # 0851709. Base Station sends desired commands and pGPS coordinates to agents This includes coordinates, agent tasks, and signals 1. Shelley Rounds for poster template2. http://www.codeguru.com/forum/showthread.php?t=194400 3. Jordan Wirth and Angel M. Jimenez Cortes. Generation II MAS-Motes Construction Manual4. CamStudio. http://camstudio.org The MAS-net platform uses MAS-mote Gen II chassis