jharwell
research/652-windowed-perception-models
Lerman2003 defines a MC amenable model for robots WITH memory, which could then be used in discrete different equations to help model the average swarm state for more complex controllers like DPO. So, new perception models should be created:
-
Add a new perception variant where robots can remember the last N objects they have seen, and do not ascribe a probability to their continued existence after they have left the robot's line of sight. Perform experiments to compare this to pheromone decay variant.
-
Add a new perception variant where robots track all objects they have seen for the last N timesteps. Perform experiments to compare this to the pheromone decay variant.
-
Add a new perception variant where robots can remember the last N sites they have visited and successfully picked up blocks from. If N=1, this is site fidelity (Lu2018,Just2017). Robots make stochastic decision each time they pick up a block on whether to overwrite their old remembered site with the current one, according to some criteria (density of nearby robots for example).
-
Add a new perception variant where robots track object density via decaying poisson pheromone waypoints. Robots create a waypoint if they see more than R resources at/near a given site. Robots can exchange waypoints/access a shared pool of waypoints upon entering the nest, or not. From Hecker2015.
After defining the new perception models, they should be compared with the DPO perception model to see where they are better/worse.
