Rat Head Algorithm

8/16/2019 Rat Head Algorithm

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RAT HEAD ALGORITHM


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Abstract

Cyborg intelligence is an emerging kind of intelligence paradigm.

It aims to deeply integrate machine intelligence with biological

intelligence by connecting machines and living beings via neural

interfaces, enhancing strength by combining the biological

cognition capability with the machine computational capability.

Cyborg intelligence is considered to be a new way to augment

living beings with machine intelligence. In this paper, we build rat

cyborgs to demonstrate how they can expedite the maze escape

task with integration of machine intelligence. We compare the

performance of maze solving by computer, by individual rats, and

by computer-aided rats i.e. rat cyborgs!. "hey were asked to #nd

their way from a constant entrance to a constant exit in fourteen

diverse mazes. $erformance of maze solving was measured by

steps, coverage rates, and time spent. "he experimental results

with six rats and their intelligence-augmented rat cyborgs show

that rat cyborgs have the best performance in escaping from

mazes.


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I%"&'()C"I'%

Within the past two decades, bio-robots have been realized on

di*erent kinds of creatures, such as cockroaches, moths, beetles,

and rats. "hey are expected to be superior to traditional

mechanical robots in mobility, perceptivity, adaptability, and

energy consumption. +mong them, rat robots are becoming

popular for their good maneuverability. "o make a rat robot, a pair

of micro electrodes are implanted into the medial forebrain

bundle ! of the rat/s brain, and the other two pairs are

implanted into the whisker barrel #elds of left and right

somatosensory cortices. +fter the rat recovers from the surgery, a

wireless micro-stimulator is mounted on the back of the rat to

deliver electric stimuli into the brain through the implanted

electrodes. "his allows a user, using a computer, to deliver

stimulus pulses to any of the implanted brain sites remotely.

0timulation in can excite the rat robot by increasing the level

of dopamine in its brain, and stimulation in the left or right 0I

makes the rat robot feel as if its whiskers are touching a barrier.

efore a rat robot is used for navigation, a training process is

usually needed to reinforce the desired behaviors i.e. moving


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ahead, turning left and turning right!. In this process, the

stimulation acts as a reward as well as a cue to move ahead, and

the left and right 0I stimulation act as the cues to turn left and

turn right respectively. In order to get the reward, the rat robot

will learn to do the correct behaviors corresponding to the cues.

+fter su1cient navigation training, the rat robot will move ahead

in response to the orward cue, turn left in response to the 2eft

cue, and turn right in response to the &ight cue. In this paper, the

rat robot is referred to as rat cyborg.

'ne of the reasons that researchers take interest in developing

rat cyborgs is that rats have outstanding spatial localization

abilities. "hey can #nd a way in an environment by orienting

themselves in relation to a wide variety of cues, including distal

cues, typically provided by vision, audition, and olfaction, as well

as proximal cues, typically provided by tactile, kinesthetic, and

inertial systems. &ats even have a well-developed magnetic

compass sense for spatial orientation. &ats do not build detailed

geometrical representations of the environment. "hey rely on the

learnt associations between external perception and the pose


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belief created from the self-motion cues. 0tudies of spatial

orientation posit that rats use an 3inner 4$05 in hippocampus and

entorhinal cortex to create a cognitive map of the environment.

urthermore, &at 02+, a navigation model which is inspired by

rat/s brain, can perform simultaneous localization and mapping in

real time on a mechanical robot.

'n the other hand, machines or computers! are e1cient in

numerical computation, information retrieval, statistical

reasoning, and have almost unlimited storage. (i*erent to rats,

machines have their own methods to explore and learn about the

environment in spatial navigation problems. "hey can capture

many categories of information from the environment through

various sensors, such as range sensors, visual sensors, vibration

sensors, acoustic sensors, and location sensors. "he information

is saved and converted into a discrete and digital form. "hen the

processed information will be synthesized to map the

environment by di*erent paradigms. 6ventually, machines can

choose various searching algorithms in path planning, for

example, 7ood-#ll method, (i8kstra/s algorithm, +9 search


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algorithm, rapidly exploring random tree, probabilistic roadmap.

achines run the sense process, map process, and decision

process in real time and in parallel. + typical demonstration of

machine/s navigation abilities is the micromouse competition, in

which a small rat-like mechanical robot explores a :; can biological intelligence be augmented with the

help of machine intelligence? "o explore the =uestion, a maze

solving task was introduced, in which three kinds of sub8ects

computer, rats and rat cyborgs! were asked to #nd their ways

from a predetermined starting position to a predetermined target

position. It is a challenge to embed the machine/s maze solving

capability into their navigation. In our experiments, the computer

traversed the mazes based on an improved wall follower

approach, six rats traversed :@ mazes one by one all by

themselves, and then traversed the :@ mazes again in the same

order with the assistance of the computer. $erformance was


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measured by steps, coverage rates, and time spent, allowing for

comparisons.


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aterials and ethods

0ub8ects

0ix rats adult 0prague (awley ratsA BD EFD g! took part in the

experiments over the course of one month. +ll of the six rats

named (G:B, 0H:F, 0H:, H:B, D: and DE! had already

gone through a su1cient navigation training process, which

means they would turn left in response to the 2eft stimulus, turn

right in response to the &ight stimulus, and move ahead in

response to the orward stimulus. %ote that in this study, 2eft and

&ight stimuli are used to instruct the rat cyborgs to turn left and

right, as well as to prevent them from moving into dead roads.

orward stimulus is not used to instruct them to move ahead, but

to reward them. In our experiments, the six rats and the six rat

cyborgs are the same rats. + rat solving mazes by itself is called a

rat, while the same rat solving mazes with the assistance of

computer via its backpack stimulator is called a rat cyborg.

Maze Solving by Computer.

0imilar to rats, the computer had to #nd a path from the start to

the target with no knowledge of the mazes. "here are a number of


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di*erent maze solving algorithms, such as random mouse, wall

follower, $ledge, and "remaux/s algorithm. In this study, on the

basis of dead road detection and uni=ue road detection, two wall

follower rules left-hand and right-hand! were adopted to traverse

the :@ mazes. (ead roads refer to roads that cannot lead to the

target. "here are two kinds of dead roads> visited dead road and

non-visited dead road. "he dead road detection algorithm is listed

in Algorithm A. + uni=ue road refers to the only way to the

target. "he detailed uni=ue road detection algorithm is listed

in Algorithm B. "he complete maze solving algorithm is listed in

+lgorithm :. or left-hand rule, the traversal se=uence is

clockwise leftJfrontJrightJback!, and for right-hand rule, the

traversal se=uence is anticlockwise rightJfrontJleftJback!. ig :.

0hows two maze solving processes by our algorithm. 0teps and

coverage rates of maze solving in the :@ mazes were recorded.

We took the average steps and average coverage rates of the left-

hand rule and right-hand rule as the performance measures.


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Fig 1. Maze solvig b! o"r algorithm.

"he yellow cell is the current position of the explorer. lue cells

indicate a uni=ue road to the target. Cyan cells are cells which

have not been explored, and walls of these cells now are unknown

to the explorer. $ink slash 3K5! denotes the non-visited dead

road, &ed Cross 3 aze solving by our algorithm left-hand!.

: #hile the current cell C is not the target cell $o


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B i% there is a uni=ue cell ) which is accessible in the four

ad8acent cells the

E move to )A

@ e$

F else i% the left cell of C is accessible and not a dead cell the

; move to the left cellA

e$

M else i% the front cell of C is accessible and not a dead

cell the

move to the front cellA

:D e$

:: else i% the right cell of C is accessible and not a dead

cell the

:B move to the right cellA

:E e$

:@ else i% the back cell of C is accessible and not a dead

cell the

:F move to the back cellA

:; e$

: e$

"o ensure that our algorithm can re7ect the computer/s maze

solving capacity, we compare its performance in B@ mazes maze


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: to maze B@! with that of three classical maze solving algorithms

i.e. wall follower, $ledge and "remaux/s algorithm!. "he

experimental results are presented. We can see from the left

column that both the steps and coverage rates of our algorithm in

most mazes are less than those of the other three algorithms.

rom the right column, the average steps and average coverage

rates of our algorithm are less than those of the other three

algorithms. "he results indicate that our algorithm outperforms

the other three algorithms in maze solving. "hus we take the

performance of our algorithm as computer/s performance in maze

solving, and compare it with that of rats and rat cyborgs.


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Maze Solving by Rats.

+fter the maze solving training, the entire maze was washed and

dried, and then the six rats proceeded to this formal maze solving

procedure. In this procedure, the rats solved :@ mazes one by one

using their own spatial learning abilities. (etails of this procedure

are similar to the maze solving training procedure. "he

experimental time in each day was M-:B a.m. and B-; p.m. 6ach

rat ran each maze only one time. In each maze, at #rst the rat

was placed in the constant starting cell. +fter the rat arrived at

the target cell and drank a drop of water D.:F ml! in a dish, #ve

consecutive rewards were sent. 'nce all of the six rats

#nished a maze, they proceeded to solve the next maze. 6ach rat

was o*ered ; ml water at the end of one day/s experiment. 0teps,

coverage rates and time spent of maze solving by the rats in the

:@ mazes were analysed. +dditionally, we observed that the

strategy rats took to solve the mazes was not easily understood

in other words, it was not perfect!. "hey revisited the visited

cells, explored the dead roads which could be easily detected by

the computer, and even returned to the starting cell when they


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were confused. "his o*ered an opportunity to the computer to

help rats in maze solving.

Maze Solving by Rat Cyborgs.

+ rat cyborg is a computer-aided rat, which is a combination of a

rat and machines. +fter the procedure of maze solving by rats, the

entire maze was washed and dried, and then the same six rats

traversed the :@ mazes in the same order with the computer/s

help. (etails of this procedure are similar to the procedure of

maze solving by rats, except that the rats have the help of the

computer to solve the mazes. 0hows two maze solving processes

by rat cyborgs. With the motivation to help rats solve the mazes,

the computer tracked the rats, analyzed the explored maze

information, and decided when and how to intervene. "he

computer aided the rats under three rules> :! if there was a path

to the uni=ue road, the computer would #nd the shortest path,

then 2eft and &ight commands would be sent to navigate the rat

to the uni=ue roadA B! if the rat was going to enter a dead cell,

2eft or &ight commands would be sent to prevent such a moveA

E! if the rat was in a loop


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(etected by Algorithm &, the computer would #nd the shortest

path to the current destination, then 2eft and &ight commands

would be sent to navigate the rat to follow the path. In this

experiment, the computer automatically analyzed the dead roads,

uni=ue roads, loop roads and shortest roads in real time, and

generated the guiding information to overlay on the live video,

which is playing on the screen of the computer. +t the same time,

an expert was watching the guiding information shown on the

screen and sent the 2eft and &ight commands to direct the rat

under the three rules. "he sent 2eft and &ight commands were

also shown simultaneously in the video. $lease note that the

expert was not directly involved in the maze solving, hisNher

operations to send the 2eft and &ight commands under the

guidance of the computer is 8ust a simple replacement of the

computer/s execution action!, not an augmentation or a

diminution of the intelligence, since all the path detection are

performed by the computer. +part from the above three rules,

rats solved the mazes of their own free will. We did not treat rats

as machines which were totally controlled by the computer. 0teps,


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coverage rates and time spent of maze solving by the rat cyborgs

in the :@ mazes were analysed.

ig B. aze solving by rat cyborgs.

The red point is the body position, the black point is the head position, and the blue line is the heading direction of the rat

cyborg. Our rat cyborg tracking method (see Algorithm 2) is

implemented by OpenCV. lue cells indicate a uni!ue road to the

target. "ed cells are cells #hich ha$e not been e%plored, and

#alls of these cells no# are unkno#n to the rat cyborg. The pink

slash (&') denotes the non$isited dead road, the red cross (&*)

denotes the $isited dead road, and the yello# &g denotes

entrances to the une%plored area. Consecuti$e blue points

indicate the shortest path to the current destination. The shortest

path is detected by A+ algorithm


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Algorithm '( Rat c!borg trac)ig algorithm.

: 0ave a background image to aA

B #hile the rat cyborg has not reached the target cell - $o

E pick an image b from the cameraA

@ use c$Absi/ to subtracta fromb, write the di*erence to cA

F use c$Threshold to get a binary imaged fromcA

; use c$0indContours to #nd all of the contoursCo indA

search the largest contour Cma% inCoA

M calculate the centroid of pixels in Cma% , write it to the body

position "bA

use c$-ood0eaturesToTrack to detect corners inCma% #nd a

@D


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rat cyborgs. inally, correlation between the steps and correlation

between the coverage rates are analyzed.

*TE+*

0teps are the sum of times visiting each cell. + cell can be visited

multiple times. 0teps of maze solving by the six ratsNrat cyborgs

are presented with those by the computer in ig E. We can see

from the left column that the steps of rat cyborgs in most mazes

are less than those of rats, and may be e=uivalent to those of the

computer.


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&overage Rates

Coverage rate is the number of visited cells. 6very visited cell is

counted only one time, so the maximum value of the coverage

rate is :DD. "he larger the coverage rate is, the less e1cient the

maze solving is. It can evaluate the performance of the explorer in

maze solving from another point of view. Coverage rates of maze

solving by the six ratsNrat cyborgs are presented with those by the


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computer in ig @. We can see from the left column that the

coverage rates of rat cyborgs in most mazes are less than those

of the computer and those of the rats. rom the right column, the

average coverage rates of each rat cyborg are less than that of

each corresponding rat> (G:B from FM.:O;.F to FB.B:O;.M!,

0H:F from FF.E;O;.B; to F:.:O;.M@!, 0H: from FE.FO.D;

to F:.;@O;.EF!, H:B from ;D.E;OF.BF to [email protected]!, D:

from ;F.DOF.M; to FB.:O;.B!, and DE from ;@.M;O.B to

F:.DO;.B!A and the average coverage rates of each rat cyborg

are also less than that of the computer ;D.FDOF.:E!.


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Fig ,. &overage rates o% maze solvig b! com-"ter rats

a$ rat c!borgs.

"he coverage rates of maze solving by (G:B, 0H:F, 0H:, H:B,

D: and DE are shown from the top to the bottom, respectively.


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Time *-et

"ime spent measures the period between a ratNrat cyborg starts

to move at 0 and the ratNrat cyborg reaches 4. esides steps and

coverage rates, we further compare the time spent of the rats in

the :@ mazes with those of the rat cyborgs. "ime spent of maze

solving by the six ratsNrat cyborgs are presented in ig . +s we

can see from the left column that the time spent of rat cyborgs in

most mazes are less than those of rats. rom the right column,

the average time spent of each rat cyborg is less than that of

each corresponding rat> (G:B from :;M.DO@@.F to

:B.E;OEB.@!, 0H:F from E@.@EO;M.;M to :BF.DDOE:.@M!,

0H: from EBM.FDO@D.: to BFE.@EOE:.;!, H:B from

FMM.@EO:D;.MF to EEE.DO;:.FM!, D: from :F;.:@OEE.:E to

D.FDOM.MB!, and DE from :@@.E;OE;.EF to @F.:@O:E.;!. "he

two-tailed paired t-test shows that there are four rats i.e. 0H:F,

H:B, D: and DE! that performed better with the assistance of

the computer. "hese results suggest that in maze solving, based

on the time spent, the performance of rat cyborgs is better than

that of individual rats.


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Fig /. Time s-et o% maze solvig b! rats a$ rat c!borgs.

"he time spent of maze solving by (G:B, 0H:F, 0H:, H:B, D:

and DE are shown from the top to the bottom, respectively. (ata

are presented as meanOs.e.m. on the right. 9 pPD.DF, 99 pPD.D:,

999 pPD.DD:.


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(iscussion

"he experimental results show that, in terms of steps, coverage

rates and time spent, rat cyborgs performed better than rats in

maze solving. Gowever, in terms of steps, performance of rat

cyborgs did not show remarkable advantages over that of

computer. +ctually, rats did not strive to reach the destination in

the least number of steps. "hey sometimes would revisit visited

cells again and again, and wander between ad8acent cells.

%onetheless, all of the six rat cyborgs outperformed the computer

in terms of the coverage rates. oreover, the rat cyborgs are

agile in di*erent types of terrain.

ecause the six rats and the six rat cyborgs were the same rats,

the possible interference should be carefully prevented.

&apid advance of brain-machine interfaces Is!, the connection

and interaction between the organic components and computing

components of the cyborg intelligent systems are becoming

deeper and better. ased on such kinds of symbiotic bio-machine

systems, a new type of intelligence, which we refer to as cyborg

intelligence, will play an increasingly crucial role. Cyborg


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intelligence is a convergence of machine and biological

intelligence, which is capable of integrating the two

heterogeneous intelligences at multiple levels. It has the potential

to deliver tremendous bene#ts to society, such as in search and

rescue, health care, and entertainment. "he mobility,

perceptibility and cognition capability of the rats were combined

with the sensing and computing power of the machines in the rat

cyborg system. "he experimental results of the rat cyborg system

in maze solving provide a proof-of-principle demonstration for

cyborg intelligence.


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Conclusions and uture Work

In this paper, we build intelligence-augmented rat cyborgs and

present a comparative study of maze solving by computer, by

rats, and by rat cyborgs. Computer aids rats in dead road

detection, uni=ue road detection, loop detection, and shortest

path detection.

In future work, more tasks will be introduced, and the complexity

of tasks will be =uanti#ed. "o avoid excessive intervention with

the rats, the strength of the computer/s assistance will be graded.

In addition, more practical rat cyborgs will be investigated> the

web camera will be replaced by sensors mounted on rats, such as

tiny camera, ultrasonic sensors, infrared sensors, electric

compass, and so on, to perceive the real unknown environment in

real timeA and the computer-aided algorithms can be housed on a

wireless backpack stimulator instead of in the computer.


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Rat Head Algorithm