Initially I will use two different sensors for obstacle avoidance. lidar for closer range 2D image of space (obscale avoidance, scanning ground before landing, maybe SLAM)
and laser rangefinder for long range 1D distance sensing (looking for high buildings, maintaining altitude precisely, maybe high speed obstacle avoidance)
I went with cheapo robopeak hobby lidar, which seems to work well, here is a git repo for the library (node and beaglebone) (I also considered a lightweight hokuyo lidar) and for laser range finder I'll go with something like SF02 from paralax
these might be used in combination with some simple IR or just cameras (need more CPU but could double as optical flow sensors) used to locate becaons for landing or grabbing things
Ideally, both sensors should have an unobstructed front view on pitch dimension, with aditional unobstructed jaw dimension for LIDAR. Both sensors need to be able to independantly rotate on pitch axis for compensation of the angle of the drone when flying, and for scanning up and down (going in through windows, looking down when landing, etc) maybe rangefinder should also rotate on jaw axis on some fast servos for looking around independant of the drone orientation. not sure yet.
IR and ultrasound were also options, they are cheap but tricky and unreliable, (ultrasound behaves extra badly due to propeller noise on drones) These things can be compensated for, but laser rangefinding will always afaik give more accuracy and reliability at a price.
After looking at the rorschach test, I wanted more. supirisingly, I couldn't find any rorschach generators online, so, python, tkinter and randomwalk with random size blobs and simmetrical reflection, I thought that some tweaking or more complicated algorithm would be needed but this works great.
click on a canvas for (re)generation.
a bunch of selfish prisoners having sex and mutating. WHOA. a very basic evolutionary algorithm, a last night's play.
Prisoner's dilemma is a scenario in a game theory (check it out if you don't know about it) its interesting to me in part because it explains human moral intuitions through a selfish evolutionary perspective, in other words, why people are generally nice to each other.
In the iterated prisoners dilemma the game is played repeatedly thus each agent has an opportunity to learn about other agents behaviours and potentially punish noncooperation. Cooperation may then arise as an equilibrium outcome.
I didn't get tit for tat which I originaly expected, nor did I manage to get any other cooperative equilibrium. cooperation does evolve, but it doesn't last, strange, I expected that cooperative species will raise and stay dominant as I enlarge the number of interactions before a step in the evolution. (its rational to be nice if you need to stick around) need to investigate and think about this some more.
I'll post more info and code later or never. screenshot and someone with a different approach and a spacial dimension.