GoGo Mapper
Model was written in NetLogo 5.0.3
•
Viewed 513 times
•
Downloaded 35 times
•
Run 0 times
Do you have questions or comments about this model? Ask them here! (You'll first need to log in.)
Comments and Questions
Please start the discussion about this model!
(You'll first need to log in.)
Click to Run Model
;; There are two main problems that the robot must solve ;; 1) Figure out the layout of the room -- that is, what patches are solid, what aren't ;; 2) Calibrate itself the sensor range and turn speed, as well as allow for imprecise movements ;; This algorithm attempts to solve both problems at once. ;; It works by having many potential robots, each with somewhat characteristics and position. ;; Each potential robot checks it's accuracy against the sensor data. The confidence in each ;; potential robot is adjusted accordingly. ;; Then, each robot basically votes on which patches are solid and which are vacant, based on the ;; sensor data and the potential robot's attributes. ;; The best potential robots are then selected and the rest are removed. ;; The best robots reproduce with small variations. ;; The real robot and potential robots then move in an ideally similar fashion and the cycle repeats. ;; ;; Hopefully the robots conception of it's environment and itself converge. ;; The nice part of this algorithm is that it makes no assumptions about the sensitivity of the robot's ;; sensors, power of its motors, or the environment. It also requires no deduction, complex math, ;; or anything. ;; ;; It is loosely based on the particle filter algorithm used to orient robots in a known environment ;; while allowing for error in movements. This algorithm doesn't use any actual probability theory though. extensions [ gogo ] globals [ serial-port test? controlable-srange ;; If the sensor reads > max-dist, we say it didn't hit anything time-step ;; how long actions are done for max-confidence ;; A patch can't have solidity > max-confidence; a robot can have confidence > max-confidence, but it only helps in selection, not reproduction previous-hit? ] breed [ robots robot ] breed [ fauxgos fauxgo ] ;; fake gogo board robot for testing breed [ echoes echo ] robots-own [ sensor-range ;; The sensors range in units of the distance the robot travels in time-step confidence ;; Represents how confident we are in the bot turn-speed ;; degrees the robot thinks it moves at a time sensor-position ;; how far in front of the center of rotation the sensor is ] fauxgos-own [ sensor-range turn-speed sensor-position ] echoes-own [ sensor-range sensor-position parent hit-patch ] patches-own [ solidity ;; Represents how likely it is that the patch is solid solid? ;; used for testing ] to set-auton set autonomous-mode? true end to set-auton-off set autonomous-mode? false end to setup if not gogo:open? [ setup-gogo ] setup-brain set test? false end to setup-gogo ifelse length (gogo:ports) > 0 [ set serial-port user-one-of "Select a port:" gogo:ports ] [ user-message "There is a problem with the connection. Check if the board is on, and if the cable is connected. Otherwise, try to quit NetLogo, power cycle the GoGo Board, and open NetLogo again. For more information on how to fix connection issues, refer to the NetLogo documentation or the info tab of this model" stop ] gogo:open serial-port repeat 5 [ if not gogo:ping [ user-message "There is a problem with the connection. Check if the board is on, and if the cable is connected. Otherwise, try to quit NetLogo, power cycle the GoGo Board, and open NetLogo again. For more information on how to fix connection issues, refer to the NetLogo documentation or the info tab of this model"] ] gogo:talk-to-output-ports [ "a" "b" "c" "d"] end to setup-test setup-brain create-fauxgos 1 [ set heading 0 set turn-speed 3 set sensor-position 3 set shape "turtle" set size 5 set sensor-range 3 ] ask patches [ set solid? pxcor = min-pxcor or pycor = min-pycor or pxcor = max-pxcor or pycor = max-pycor or (pxcor = 25 and pycor > -1) or (pxcor > -1 and pycor = 25) or (pxcor < 0 and pycor < 0 and (25 > abs (pxcor * pxcor + pycor * pycor - 625) )) ] ask patches with [solid?] [set pcolor white] set test? true end to setup-brain ca set time-step 0.05 set max-confidence 1000 set previous-hit? false create-robots 1 [ set heading 0 set size 5 set confidence max-confidence set turn-speed init-turn-speed set sensor-range init-sensor-range set sensor-position init-sensor-position ] end to go sensor-check select-robots ask robots [reproduce] ask robots [set confidence 0] if autonomous-mode? = true [autonomous-move-behavior] end to autonomous-move-behavior ifelse sense-dist > max-dist [robots-fd set previous-hit? false] [robots-rt robots-bk set previous-hit? true ] end to autonomous-move-behavior1 ifelse previous-hit? = false [ifelse sense-dist > max-dist [robots-fd set previous-hit? false] [robots-rt robots-bk set previous-hit? true ]] [ifelse sense-dist > max-dist [repeat 10 [robots-bk robots-lt sensor-check] set previous-hit? false] [robots-rt robots-bk set previous-hit? true ]] end ;; observer procedure ;; Adjusts robots' confidences and patches' solidities based on sensor reading. to sensor-check ask robots [set confidence confidence - 5 * [solidity] of patch-here] ask robots [fire-echo] ask robots [retract-echo] ask patches [ if solidity < 0 [set solidity 0] if solidity > max-confidence [set solidity max-confidence] patch-recolor ] cd end to-report sense-dist ifelse test? [ let dist 0 ask fauxgos [ hatch-echoes 1 [ set parent myself fd sensor-position while [distance myself < (sensor-range + sensor-position) and not [solid?] of patch-here and can-move? echo-speed] [ fd echo-speed ] set dist distance myself - sensor-position die ] ] report 1.1 * max-dist * (random-normal 0 .1 + dist) / [sensor-range] of one-of fauxgos ] [report gogo:sensor 1] end ;; robot procedure ;; Gets the sensor's output in patches based on what this robot think's the sensor's range is to-report get-dist report ifelse-value learn-sensor-range? [sensor-range][init-sensor-range] * sense-dist / max-dist end ;; robot procedure ;; Shoots an echo to the spot where the sensor sensed something (according to the robot) ;; The robots confidence level is adjusted on the way: ;; - The echo passing over a patch that the robot thinks is solid penalizes the robot ;; - The echo passing over a patch that the robot thinks is vacant rewards the robot slightly ;; - At the end, if the robot correctly predicted whether or not an obstacle was there, the robot is rewarded ;; - At the end, if the robot incorrectly predicted, the robot is penalized ;; All penalties/rewards are adjusted based on the solidity of the patches (as solidity sort of corresponds to confidence) to fire-echo let dist get-dist hatch-echoes 1 [ set size 1 fd sensor-position pd set parent myself ] ask echoes with [parent = myself] [ while [distance myself < (dist + sensor-position) and can-move? echo-speed] [ let cur-patch patch-here ask parent [set confidence confidence + vacant-patch-reward-factor * (5 - [solidity] of cur-patch) / 10] fd echo-speed ] ifelse dist < sensor-range [ set hit-patch patch-here ask parent [set confidence confidence + obstacle-reward-factor * ([solidity] of [hit-patch] of myself - (max-confidence / 3)) / 10] ] [ set hit-patch nobody ] ] end ;; robot procedure ;; Like fire-echo, but adjusts solidity of the patches as the echo travels back to the robot. ;; The robots are essentially voting on what patches they think are solid to retract-echo ask echoes with [parent = myself] [ if hit-patch != nobody [ask hit-patch [ set solidity solidity + 5 ]] while [distance parent >= sensor-position] [ face parent fd echo-speed if patch-here != hit-patch [ ask patch-here [ set solidity solidity - 1] ] ] die ] end ;; robot procedure ;; Each echo travels the same number of steps, otherwise it's biased to bigger sensor-ranges to-report echo-speed report [sensor-range] of parent / 20 end ;; observer procedure ;; Natural selection - best keep-robots robots are kept, rest die to select-robots if count robots > keep-robots [ ask min-n-of (count robots - keep-robots) robots [confidence] [die] ] end ;; robot procedure ;; Robots reproduce based on their confidence to reproduce hatch-robots reproduce-amount [ rt random-normal 0 .1 if learn-sensor-range? [ set sensor-range sensor-range + random-normal 0 sensor-range-mut-rate ] set color 5 + 10 * random 14 if learn-turn-speed? [ set turn-speed turn-speed + random-normal 0 turn-speed-mut-rate ] if learn-sensor-position? [ set sensor-position sensor-position + random-normal 0 sensor-position-mut-rate ] ] end to robots-fd ask robots [fd random-normal 1 vary-move] ifelse test? [ ask fauxgos [ let dist random-normal 1 0.1 fd dist if [solid?] of patch-here [bk dist] ] ] [ gogo:talk-to-output-ports ["a" "b"] gogo:set-output-port-power 7 gogo:output-port-on wait time-step gogo:output-port-off ] end to robots-bk ask robots [bk random-normal 1 vary-move] ifelse test? [ ask fauxgos [ let dist random-normal 1 0.1 bk dist if [solid?] of patch-here [fd dist] ] ] [ gogo:talk-to-output-ports ["a" "b"] gogo:set-output-port-power 7 gogo:output-port-reverse gogo:output-port-on wait time-step gogo:output-port-off gogo:output-port-reverse ] end to robots-rt ask robots [ fd random-normal 0 0.01 rt random-normal (ifelse-value learn-turn-speed? [turn-speed] [init-turn-speed]) vary-turn ] gogo-rt end to gogo-rt ifelse test? [ ask fauxgos [rt turn-speed] ] [ gogo:talk-to-output-ports ["a" "b"] gogo:set-output-port-power 7 gogo:output-port-on gogo:talk-to-output-ports ["a"] gogo:output-port-reverse wait time-step gogo:talk-to-output-ports ["b"] gogo:output-port-off gogo:talk-to-output-ports ["a"] gogo:output-port-off gogo:output-port-reverse ] end to robots-lt ask robots [ fd random-normal 0 0.01 lt random-normal (ifelse-value learn-turn-speed? [turn-speed] [init-turn-speed]) vary-turn ] gogo-lt end to gogo-lt ifelse test? [ ask fauxgos [lt turn-speed] ][ gogo:talk-to-output-ports ["a" "b"] gogo:set-output-port-power 7 gogo:output-port-on gogo:talk-to-output-ports ["b"] gogo:output-port-reverse wait time-step gogo:talk-to-output-ports ["a"] gogo:output-port-off gogo:talk-to-output-ports ["b"] gogo:output-port-off gogo:output-port-reverse ] end to patch-recolor set pcolor scale-color red solidity 0 max-confidence end to robot-recolor set color scale-color color confidence 0 max-confidence end
There is only one version of this model, created almost 12 years ago by Bryan Head.
Attached files
File | Type | Description | Last updated | |
---|---|---|---|---|
GoGo Mapper.png | preview | Preview for 'GoGo Mapper' | over 11 years ago, by Bryan Head | Download |
This model does not have any ancestors.
This model does not have any descendants.