Autonomous Robots: Frontier Searching Alex Morales, Computer Science, Joey Durham, Mechanical Engineering, Francesco Bullo, Mechanical Engineering UC Santa Barbara Introduction/Background Robot and sensor hardware Player/Stage interface In searching problems a robot is looking for a target in a Player/Stage is an interface which we use to test and The robots we use are Videre ERA-MOBI model. Each simulate our robot algorithms. Stage simulates mobile robots potentially unknown domain. To locate the target it is contains an on board Linux computer and a laser distance important that the robot traverses the map efficiently. in a two dimensional environment. Using Player/Stage is sensor. More details are listed below. convenient especially because our algorithms can be tested My searching algorithm for locating the stationary target, focused on creating a frontier between explored and Robot specs: on either the simulated environment or the real robots and unexplored regions. The robot iteratively chooses the best can easily move from one to the other to make position on the frontier which maximizes frontier coverage. ● Size: 40cm(L) x 41cm(W) x 15 cm improvements. (H) ● Batteries: 4-5 Hours with normal movement. ● Encoder accuracy: 500 counts/ rev ● Speed: up to 2 m/s ● Capacity 20kg ( 44lbs) Laser specs: ● Model: Hokuyo URG laser Rangefinder ● Range: 5 meters ● Scan rate: 10 Hz ● Resolution 0.36 degrees This is an example of the simulated environment, with the robot (red) and a stationary target (blue). The Algorithm: The flowchart bellow explains the steps of the searching algorithm. Robot (red dot) gets distance The new visibility polygon data from laser rangefinder. merges with the old visibility polygon, creating The frontier and the visibility a new frontier. Receive laser data polygon are created (green area). The robot saves the new position and local frontier. Target Found Check if Target was found Objective Finished Target not found. Create local The robot chooses the next best As the robot moves to new visibility Polygon position to search. positions in the map, it build a position graph, creating a The best position is a point on map of the explored region Update Global the frontier that maximize frontier Frontier. coverage. All frontiers have Decide next move been searched. Move to position chosen The robot can then use this Once at new position, the position graph to navigate the No Target robot again constructs a environment and checking for In the environment. visibility polygon. shortest distances to unexplored regions of the environment. Finished searching Properties of Algorithm Efficiency Future Research This algorithm is also a foundation for solving This search algorithm is complete meaning that it will By weighting the best next position based on the lengths of the more complex variation on this search problem. For find a target when one exists, or determine there is no frontier segments covered, we believe that the algorithm target after exploring the entire environment. This claim maximizes the expected exposure of unexplored area. By max example, this algorithm can be extended to multiple holds with appropriate assumptions on the topology of the exposure on each iteration the algorithm minimizes the expected searchers or a moving target. environment. time to detection. Some of the limitations on the algorithm could come In addition, depth-first traversal of the position graph helps from the accuracy of the sensor data, and the problem of minimize worst-case time by relating the amount of time the robot odometry could effect how the robot navigates the spends traversing already explored regions. environment Acknowledgements: Fancesco Bullo, Joey Durham, UCLEAD coordinators and sponsors References: Some images found on Google Images.
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