Most robots, especially autonomous robots, need to be clever enough to avoid bumping into obstacles. To do this they need sensors to investigate the environment around them, they need to process this data and identify obstacles in their vicinity. Finally, they need to be able to generate motor commands that steer them clear of any obstacles around them. This is the simplest form of obstacle avoidance and there are tons of examples on the internet, with plenty of little robots that can do this quite nicely.
With this sort of rudimentary obstacle avoidance algorithm, a robot could keep clear of obstacles but it would most likely wander around aimlessly while doing so. Sometimes robots need to be a little more intelligent. They need to reach a goal…perhaps they are chasing a target, perhaps they need to reach one of many way-points along a pre-determined path, perhaps they are headed toward a battery charging point or a position of interest, maybe they are meeting up with a friend, perhaps they need to duck under enemy radar cover while approaching a target!! Whatever the case, these kinds of robots…robots that can navigate intelligently, need to have a slightly more robust obstacle avoidance behaviour built into them.
Clever Robots can avoid obstacles as they head towards a goal
It’s now fairly easy for me to build a robot that can do stuff. Drive around, balance on two wheels, pick up things, drive around some more, transmit video, obey orders, drive some more. While learning all this stuff this has been great fun, robots that just DO things are not much challenge anymore. So I have spent the last few months learning to make my robots more clever, building robots than can observe their environment, make intelligent decisions and re-configure themselves to interact optimally with external stimuli.
For me, vision processing was an easy first choice in trying to build intelligent robotics. The OpenCV library is an incredibly powerful library that one can download for free. OpenCV makes implementing computer based vision extremely easy and once you get more familiar with image processing, you start to see that most operations are just elementary arithmetic operations on matrices. Operations like background subtraction, edge detection, blob detection, kalman filtering and the extremely useful Hungarian Algorithm are all just simple matrix operations. OpenCV is a little tricky to learn, but once you get the hang of it, it’s supremely powerful when it comes to doing interesting things with visual data. I owe thanks for much of what I know about OpenCV to Kyle Hounslow. His video tutorials are a super easy way to get started with OpenCV.
A couple of months ago I used the OpenCV library to build a webcam based vision capable robotic arm. I used Qt and OpenCV to implement the video capture and frame processing.The idea here is to get the computer to track the green ball and then send the correct spatial coordinates to the robot arm which would then follow the ball in space. I used my 6 DOF robot arm and ArduinoTalker c++ class to perform the motion following in the “real” world. The movements are shaky because I was too lazy to implement any smoothing algorithm and the very obvious parallax error is because the camera is fitted to the laptop screen and not onto the arm itself.