MachineLearningProjectReport

In our implementation we did not consider the paths

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Unformatted text preview: ithm. The algorithm involves finding the shortest distance from source to destination by viewing the entire map in the form of equally divided grids. We begin by marking the start and end positions by capturing an image of the table with the robot and obstacles. The algorithm first tries to search the shortest path by taking the travelled distance into account as it uses the Euclidean distance which gives the real cost between the current and goal states. In our implementation, we did not consider the paths on the boundaries, corners or edges, as it may make the robot fall off the table when it executes a turn. The path is computed such that it always takes the nearest grid to complete the shortest path to the goal state. To make it work more efficiently, valid heuristics are considered which improves the search performance. The entire algorithm is computed before the robot actually starts moving as there is no update on the go as it may lower the search algorithm efficiency when the camera link breaks. ­Guiding the robot: Given a state we define each possible case and a command to achieve it. ­ we decided to firstly take the whole scenario, solve the problem and then tell the commands to robot (maybe because of potential camera failures); ­ Conclusion: This work showed us that it is possible to solve a problem that seemed complicated at first sight with cheap hardware, like a low­cost camera and a lego robot, and simple functions, as convolution and linear regression. Something that we think that might be changed in future works is the fact that we do not correct the robot path iteratively, but instead we calculate the path, send all the commands to the robot and do not update it anymore. The path could be monitored and corrected from time to time if we had a more reliable camera, it would certainly improve the results. We believe that the purpose of this project was achieved, since the robot reached the goal and we were able to learn about the tools we used along the way....
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This document was uploaded on 02/14/2014 for the course CSCI 6505 at Dalhousie.

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