Category Archives: Robotics

Pedestrian Detection using Histogram of Gradients (HoG) and a Random Forests Classifier

This entry is part 1 of 1 in the series Pedestrian Detection

1,178 64×128 sized images of pedestrians (positive samples) and 4,530 of the same sized negative samples were extracted from the INRIA dataset and divided randomly into two to give a training set and a validation set. HoG features on the … Continue reading

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100 K: Visual Localization along Melbourne’s Highways at 100 km/hr

This entry is part 1 of 2 in the series Visual Localization Along Highways
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Enclosure for Bug’s uc + Environment Sensing

This entry is part 10 of 10 in the series Android Robot

Printed out an enclosure for Bug’s circuit boards. Also connected the 2 IR sensors on the bumper to the IOIO board for environment sensing. I’ve got the phone’s display to change colour based on whether or not something is detected … Continue reading

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Bumper Design For Bug (the Android Robot)

This entry is part 7 of 10 in the series Android Robot

This is my new bumper design for Bug. Designed in Google sketchup, it includes two IR sensors (2x Sharp GP2Y0A21YK) and two flex sensors. Waiting on my 3D printer, the Makerbot Replicator 2, which, hopefully will arrive soon so I … Continue reading

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There She Goes

This entry is part 4 of 10 in the series Android Robot
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Connecting DC motors to the IOIO through the TB6612FNG, a motor driver break out board

This entry is part 3 of 10 in the series Android Robot

The IOIO has the capability to output Pulse Width Modulated (PWM) signals on its pins. You can vary the duty cycle of PWM signals to control the speed of DC motors. It’s recommended that you don’t connect the IOIO directly … Continue reading

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Occupancy Grid

This entry is part 3 of 3 in the series Python Robotics Simulator

The easiest way for a robot to map its environment is using an occupancy grid. As the robot moves through its environment, the readings returned from its laser range finder (LRF) at different angles around it are marked off on … Continue reading

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Particle Filter Localization for a Simulated Mobile Robot

This entry is part 2 of 3 in the series Python Robotics Simulator

This simulation shows a robot (red) moving through a simulated environment and a Particle Filter (PF) trying to localize it in that environment. Initially, the particles (shown in different shades of grey indicating their probabilities) do not know the location … Continue reading

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Simulated Laser Range Finder Readings Using Python & OpenCV

This entry is part 1 of 3 in the series Python Robotics Simulator

This is my first simulation using Python + OpenCV. It rotates a robot in a SLAM Map to get 9 simulated range (laser range finder) readings that it can then use to localize itself on that map. Watch the video … Continue reading

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