A Bio-inspired Auto-velocity Detection Algorithm for

Autonomous Robots

Erhard Gwerder and Hans Dermot Doran

Institute of Embedded Systems, Zürich University of Applied Science, Technikumstrasse 9, 8401 Winterthur, Switzerland

Keywords: Robotics, Photoreceptors, Visualization, Robot Vision, Biological System Modeling, Optical Sensors,

Autonomous Robots, Speed Sensor.

Abstract: As examples of computationally cheap and robust sensors the eyes of a fly are well known in literature.

Attempts to replicate the function of the eye in electronic technology has resulted in several algorithms and

implementations based on those algorithms. These implementations are either impractical for industrial use

or use costly Application Specific Integrated Circuits. In an attempt to use a low-cost Commercial off the

Shelf camera as a sensor in a real-world robot navigation use-case we investigate two commonly used

algorithms and find them unsuitable. We develop a new algorithm – the Distance of Travel algorithm – show

its suitability and investigate its properties in both simulation and practical experiments.

1 INTRODUCTION

A Motivation

Mobile robots working in semi-structured

environments co-habited with humans will need to

travel at variable speeds and trajectories and perform

collision avoidance including subsequent re-routing

should the chose path be blocked. In other words they

must be capable of (micro-) autonomous activity

within the scope of a defined mission (macro-activity)

within set deadlines. The body of work presented here

belongs firmly in the domain of bio-inspired

engineering where we seek to achieve lower

computational cost and higher robustness by the

emulation of biological examples as opposed to more

traditional state-space oriented engineering. We do

this in the knowledge that the performance of the robot

may not be as efficient as a state-space based design

and that work is required to make robot behaviour

predictable so that humans know when and how to

respond to an approaching robot.

The motivation for this body of work was to

explore the practical ramifications of using biological

precedence, the eye of a fly, in autonomous robots

designed for practical purposes – in this case delivery

along an office corridor. The fly eye has been well-

studied over the last sixty years and can boast multiple

implementations is thus well-understood and hence a

good example of a bio-inspired algorithm.

Much of the implementation work replicates the

fly eye in a low-pixel count ASIC (Application

Specific Integrated Circuit) and often in the context of

drones. Our understanding of the use-case includes

recognition of the fact that mobile robots will use a

camera not only to perform low level navigational

duties but also for high-level image processing tasks,

a scenario precluded by single-function ASIC-based

sensors. We are unaware of any fly-eye

implementations on low-cost COTS (Commercial off

the Shelf) camera so the novelty of our paper is

therefore to analyse and implement a fly-eye

algorithm on this class of platform.

Fly-eye algorithms can be categorised into four

general types which we analysed and found wanting

in various aspects. Response linearity at higher (robot)

velocities is a common factor so we developed,

simulated and implemented a new algorithm which we

called the Distance of Travel Algorithm. It can be

shown that the algorithm functions with better

linearity of response at lower sampling rates that the

reference algorithms and is better suited for use in the

use-case we are working towards.

This paper is structured accordingly. We finish this

section by discussing the related work, methodology

and algorithms. The next section describes the

simulation of standard algorithms we chose to

implement. Section III discusses the Distance of

Gwerder, E. and Doran, H.

A Bio-inspired Auto-velocity Detection Algorithm for Autonomous Robots.

DOI: 10.5220/0006430200560065

In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017) - Volume 2, pages 56-65

ISBN: Not Available

Travel (DoT) algorithm and simulations on it. Section

IV discusses the practical implementation of the DoT

algorithm and the final section draws conclusions and

proposes future work.

B Related Work

Scientists have been examining the perception and

navigation of insects in general and the fly in

particular, for some 60 years and this has led to much

work in replicating capabilities in technology. There

is a substantial body of literature, including summaries

to be found in this area of which Franceschini

(Franceschini, 2014) is a leading example. Literature

summarises the algorithms used in replicating the

vision system in technology (Sirnivasan, 1999) and

summaries of published work detailing future trends /

Orchard, 2014.) The field is divided into several

distinct strands of research. Biological research

generating replicable models was notably achieved by

Hassenstein and Reichardt (Hassenstein, 1961) with

their description of the Elementary Motion Detector

(EMD - Figure 1) and continued with various

elaborations or contrasting models such as the Barlow

and Levick model (1965) or the Watson and Ahumada

model (1985.) Engineering research has replicated

these models using a variety of algorithms, largely

using an analogue electronics approach. Initial work

in this area (Tanner, 1986) set the tone for a largely

analogue replication of the biological models

(Harrison, 1999), (Pant, 2004), and (Roubieu, 2013.)

However engineering research has been by and

large unable to look past the EMD and the attempt to

optimally replicate it has resulted in the attempt to

build neurological detectors (Higgins, 2000.) In

contrast there has been little work done to explicitly

apply the principles to CMOS detectors. Arreguit

(1996) builds of a pointing device on a CMOS chip

whereas Basch (2010, 2011) use of standard imagers

to build Hassenstein-Reichardt based collision

detectors stand out in an otherwise sparsely occupied

literature canon.

C Methodology

Current published (research) solutions use anywhere

from 8 to 254 pixels to build EMDs and it is perfectly

acceptable to predict that these devices are capable of

being industrialised at prices amenable to them

becoming a standard low-cost, low-complexity sensor

used in robot-construction.

Given that robot vision is still research-in-

progress, especially in the higher strata beyond object

recognition towards perception, it is also reasonable to

expect that CMOS-based vision systems will be

expected to provide the data to perform several tasks,

quite probably in parallel and possibly hardware off-

loaded to ASIC or Field Programmable Gate Arrays

(FPGA) devices. Auto-speed detection using a fly-eye

algorithm of low computational cost and promising

high robustness is a low-level task that can intuitively

be mapped to a CMOS camera. This idea is

strengthened by the fact that a CMOS camera can

deliver the data for 3D speed detection, something we

have not yet seen in ASICs produced by research.

From this background an appropriate methodology

would be to choose an algorithm to implement, prove

the use-case in software and measure the real-time

properties of the solution to decide whether hardware

offloading is necessary or beneficial.

D Algorithms

The EMD is a unidirectional unit that measures the

time taken from the detection of an intensity-

recognised feature by the first receptor to its detection

by the second receptor. This time represents a metric

for the speed of movement.

Figure 1: Elementary Motion Detector (EMD) according to

Harrison (Basch et al., 2011).

The mechanisms whereby this is achieved in flies

and modelled for replication in technology have been

extensively researched to the extent that the technical

implementations (Floreano, 2009) have been divided

into two methodologies of each two categories (Figure

2.) The methodologies are intensity based and token

based and subdivided into categories of gradient and

correlation methodologies and the correlation and

time-of-travel based methodologies respectively.

We place value on low computational and resource

expense and used this as the primary criteria

A Bio-inspired Auto-velocity Detection Algorithm for Autonomous Robots

Figure 2: Categorisation of fly-eye based navigation

algorithms (Barlow and Levick, 1965).

for examining the different algorithms. The gradient

methodology was deemed to be computationally

expensive whereas the published correlation/token-

based methodologies require substantial memory

resources and so were also excluded from further

consideration. We chose the publications from

Harrison and Koch (1999) who applied intensity based

correlation using the Hassenstein-Reichardt principle

and from Roubieu et al., (2013) who applied token

based methods, as the most promising role-models and

used them in further evaluation.

2 SIMULATION OF KNOWN

ALGORITHMS

Further evaluation consisted of implementing and

performing tests on these two algorithms in Matlab.

We applied the general motion detection model, the

EMD defined as two photoreceptors, as the input

source. The outputs of the algorithms are based on the

response measured between two photoreceptors –

with a preferred direction. The optical space covered

by a single photoreceptor is generally quite large so

in the simulations these receptors are assembled from

a number of pixels of the image/camera as shown in

Figure 3 where a receptor was made up of an averaged

5*5 array of pixels.

Figure 3: Assembling receptors from camera pixels.

Testing real-time response of vision-algorithms is

difficult at the best of times and generally achieved

by using patterns of some sort. Regular patterns such

as square waves (black and white bars) and sinusoidal

(white to black via a grey scale) are commonly used

and are useful for matching actual to predicted

behaviour. For our use-case the material used for

walls must also be considered so several different

patterns were used. Results shown in this paper are

generally derived from black-white vertical bars

(square wave) and a randomly chosen concrete

pattern (Figure 4,) which represents a stochastic

signal.

We tested the algorithms by passing the patterns

in front of the receptors at a number of constant

velocities. To achieve this a script was programmed

to produce 500 copies of a test pattern with each copy

differing from the previous by a shift equivalent to the

velocity the receptor is intended to be subjected to. By

passing these pattern sequences at discrete intervals

in front of the receptors – and allowing the algorithm

to perform after being exposed to one shifted image –

the speed of passing a pattern could be precisely

simulated free of any real-time and computing-time

restraints imposed by the test platform.

Figure 4: Black and white (left) and concrete (right) patterns

for simulation of tokens.

A Hassenstein – Reichardt Detection

For the Hassenstein-Reichardt detector the

configuration of the fly-eye assembled from camera

pixels was determined as follows:

Table 1: Fly-Eye Parameters for the Hassenstein-Reichardt

detector.

Parameter Value

Distance between

receptors (α

)

4°

Mask Size

43 Pixels

Number of receptors

12 Receptors

After implementation in Matlab the first

simulation was carried through with a sample time of

20 ms and it can be seen that good linear response of

estimated velocity can be achieved up to an actual

velocity of about 3 rad/s when the measured and

actual velocities diverge (Figure 5.) This simulation

was repeated at the faster sampling rate of 5 ms

ICINCO 2017 - 14th International Conference on Informatics in Control, Automation and Robotics

(Figure 6) and the linearity of the response to the

actual velocity is considerably better. Low cost

cameras generally deliver images at frame rates of up

to 60 fps, equivalent to a sampling rate of 16 ms, so

we surmise the Hassenstein-Richardt does not appear

to be suited for implementation on such a camera.

B Time of Travel Algorithm

The same procedure was repeated for the time of

travel algorithm (ToT) proposed by Roubieu (2013.)

The parameters for this simulation were set at the

values given in Table 2.

Table 2: Fly-Eye Parameters for the Time of Travel

detector.

Parameter Value

Distance between

receptors (α

)

7°

Mask Size

77 Pixels

Number of receptors

6 Receptors

In (Figure 7) and (Figure 8) the results of

sampling rate of 10 and 20 ms are shown. As with the

Hassenstein-Richardt algorithm the linearity of the

response of the algorithm to the actual velocity

suffers at higher velocities, dramatically so at

velocities over ~3 rad/s and sampling rates of 20 m/s.

Figure 5: Performance of the Hassenstein-Richardt

algorithm simulation at sampling rates of 50 Hz.

C Conclusion

Clearly the linearity is unsatisfying for low sampling

rates at higher velocities which convinced us that

investigation of a new algorithm better suited to the

use case of a low-cost camera would be justified.

3 DISTANCE OF TRAVEL

ALGORITHM AND

SIMULATION RESULTS

A Distance of Travel Algorithm

We therefore propose a new algorithm which we call

Distance of Travel (DoT) which bears close

relationship to the time of travel algorithm. We

actually break the concept of the EMD by extending

the detection of tokens across the entire line of

photoreceptors, and in principle across the entire line-

width of the camera and treat the camera pixels as a

coherent array.

Figure 6: Performance of the Hassenstein-Richardt

algorithm simulation at sampling rates of 200 Hz.

Figure 7: Performance of the Time of Travel Algorithm at

sampling rates of 100 Hz.

The sampling time can be reduced by decreasing

the distance between receptors but simultaneously

retaining the precision of measurement as the

algorithm measures the number of receptors the token

traverses during a set sample time. The

algorithm’s principle of operation is shown in Figure

9 below.

A Bio-inspired Auto-velocity Detection Algorithm for Autonomous Robots

Figure 8: Performance of the Time of Travel algorithm at

sampling rates of 50 Hz.

Figure 9: Principle of operation: Distance of Travel

Algorithm.

From right to left, at t

a token is detected at the

second receptor, at t

the seventh receptor detects the

same token. From the travelled distance the angular

velocity can be calculated using (1.)

ω = Δα/Δt = (η

- η

) • α

/ (t

– t

) (1)

The measured value may be refined by repeated

detections of the (same) token across the

pixels/receptors. Once the token reaches near the end

of the pixel/receptor array the algorithm stops seeking

another detection and returns to the beginning of the

pixel/receptor array to search for a new token. If there

is a new token the algorithm will be able to produce a

stream of measurement values (Figure 10.)

The receptor’s macro-construction is similar to

classical elementary motion detectors and function as

shown in Figure 11: A high pass filter (HP) eliminates

offset components and heightens contrast. The next

block applies an intensity hysteresis. The

determination of this hysteresis level was not without

difficulty. If the value is too high a value then low

contrast tokens won’t be detected. If the level is too

low the SNR degrades. For this reason we propose a

method adaptive to the signal strength of the

receptors. If a new token has been detected then,

using the first 2/3 of the receptors (r), a minimum and

maximum value is found and from this a high and low

threshold value (T

min

, T

max

) calculated according to

(2) and (3.)

Figure 10: Principle of operation: Distance of Travel

Algorithm - case of token leaving field of vision.

Figure 11: Architecture of threshold detection.

min

= min(r

si-

) + min(r

si-

) / 5 (2)

max

= max(r

si-

) + max(r

si-

) / 5 (3)

B Simulation Results

The Distance of Travel implementation was exposed

to the same simulation benchmarks as the other

algorithms. In this case the receptors were built from

an empirically determined 5x5 pixel mask with one

overlapping column of pixels with the neighbouring

ICINCO 2017 - 14th International Conference on Informatics in Control, Automation and Robotics

receptor (Figure 12.) Table 3 shows the parameters

which were used for the simulations.

Figure 12 Assembling receptors from camera pixels for the

Distance of Travel Algorithm.

Table 3: Fly-Eye Parameters for the Distance of Travel

detector.

Parameter Value

Distance between receptors (α

) 0.36 °

Receptor size 5 * 5

Receptor Overlap 1 * 5

Number of Receptors 100

The first test was to trigger the algorithm with a

simulation pattern at a constant angular velocity of

0.63 rad/s. and the results proved encouraging. Unlike

the Hassenstein-Reichardt or ToT detectors the

measured velocity exhibited no oscillations. The

velocity was then varied to produce a set of

measurements, analogous to Figure 5– Figure 8, the

results of which are replicated in Figure 13. The

linearity is far better across the entire test range, at

sampling times of 20 ms, than both the Hassenstein-

Richardt and time of travel algorithms.

Figure 13: Performance of the Distance of Travel

Algorithm simulation at sampling rates of 50 Hz.

The algorithm was further examined, also using

different test patterns, specifically wood, brick and

concrete (Figure 4.) Good results were achieved with

the first two only on the concrete pattern did the

linearity of the detector degrade at angular velocity

above 3.5 rad/s (Figure 14).

4 DISTANCE OF TRAVEL

ALGORITHM

IMPLEMENTATION AND

TESTS

A Implementation

We considered the principle of the algorithm to be

confirmed by these simulations and implemented the

algorithm on the low-cost, COTS, leanXcam from

SCS. This camera, is based on a 1/3” CMOS colour

sensor and features a 500MHz Blackfin under

uClinux, Unfortunately camera production has now

been discontinued but the data sheet is still available

(SCS 2016.)

Figure 14: Performance of the Distance of Travel algorithm

simulation at sampling rates of 50 Hz. and using the

concrete pattern.

The implementation top-level code runs in an

endless loop with both the run time of the software

and the image capturing/transfer time determining the

(real-time) timing characteristics. This allowed us to

measure the run-time of the tasks and algorithms and

determine whether further optimisations were

necessary to ensure the system was capable of

processing an adequate frame rate. The code flow is

shown in Figure 15.

The camera chip is polled, using code supplied as

libraries with the camera, in order to detect whether a

new image is available and proceeds to further

processing if this is the case. Set-up of parameters is

achieved via an implemented web-interface, exposure

time and max/min speeds for the search window are,

amongst others, parameters that can be set. The

velocity value output is via a simple printf on the

camera’s Ethernet console output.

The image processing (block “Processing Image”

in Figure 15) is depicted in Figure 16.

The implementation, in contrast to the Matlab

simulations, performs an intensity check exiting with

A Bio-inspired Auto-velocity Detection Algorithm for Autonomous Robots

an appropriate return value if the intensity is not

sufficient to detect a token. If there is sufficient

intensity then the code searches for a second instance

of the token under the assumption that a first instance

is already available. If this is found then the angular

velocity can be calculated. Should a first instance not

be available, then it branches and searches for a first

token – which will be the case when f.i. the system

comes out of reset.

B Measurements

In order to get a direct correlation between the

simulation and the implementation it was desirable to

use exactly the same patterns in both environments. It

might have been possible to run the test images on a

computer screen but the PC screen generates light as

opposed to reflecting it and it is difficult to ensure that

a standard PC adheres to the real-time constraints

necessary to ensure a constant streaming of images at

the required rate/velocity. For this reason a test-jig

based around a conveyer belt was built (Figure 17.)

The images previously generated were printed and

stuck onto the conveyer belt (black and white image

on the conveyer belt in Figure 17) and the speed of

the conveyer belt could be adjusted.

Figure 15: Flow Chart for the General Operation of the Fly-

Eye Camera.

Figure 16: Flow Chart for the operation of the Distance of

Travel Algorithm Implementation on the LeanXCam.

Figure 17: Picture of test-jig.

The schematic in Figure 18 shows the jig

parameters used in the tests and in most of the tests

the parameters noted in Table 4 were used.

ICINCO 2017 - 14th International Conference on Informatics in Control, Automation and Robotics

Figure 18: Schematic of test jig.

Table 4: Parameter Settings of Test-Jig.

Parameter Value

Distance camera to the belt

(d)

0.2 m

Angle of Camera to belt (η)

90°

Velocity of belt (vt)

0.2 – 0.8 m/s

Illumination

room

Illumination time

0.05 s

Test pattern

black/white stripe

The first results received a mixed reception.

Encouraging was the closeness of velocity

measurement to that of the belt but substantial

oscillations were also visible in the output of the DoT

sensor. Further investigation resulted in an attempt to

provide the motor with better gears as it appeared that

the motor wasn’t rotating at constant speed. That

brought no resolution but it was noticed that the motor

was periodically sticking. A test run where the motor

current and voltage were also measured, albeit not

synchronised with the conveyer-belt or DoT/camera

sensor, was made. The motor voltage and current

subsequently graphed against the velocity measured

by the DoT implementation is shown in Figure 19.

The estimated angular velocity tracks the “stickiness”

of the motor/belt, as measured by motor

voltage/current, very well and we consider this to be

a sign of the quality of the DoT algorithm and its

implementation.

Evidenced by further test runs with the belt

moving at different velocities and the camera

mounted at distance of 0.3 meters from the belt, the

DoT algorithm shows an excellent correlation

between expected and measured velocity albeit, there

is a deviation observable at speeds greater 0.75 m/s

which requires further investigation. Unfortunately a

different test-jig is required as the maximum velocity

of the conveyer belt is 0.85 m/s.

Figure 19: Correlation of Distance of Travel algorithm with

motor current.

Figure 20: Performance of the Distance of Travel algorithm

implementation at sampling rates of 50 Hz. and using the

stripes pattern.

1) Wall Pattern

The DoT algorithm depends on contrast changes in its

line of sight to determine velocity. As in the

simulations several other patterns were taped onto the

conveyer belt and the velocity measured, the results

being shown in Table 5.

Table 5: Measurement error of Distance of Travel

implementation using various test patterns.

Test Pattern Average

measured

angular

velocity (rad/s)

Average

estimated

angular

velocity (rad/s)

Error %

Stripes 0.67 0.66 1.5

Brick 0.67 0.73 9.0

Concrete 0.67 0.72 7.5

Wood 0.67 0.74 10.5

2) Camera Orientation

The use case is a mobile robot moving in a straight

line down a corridor under closed loop control with

the output of this algorithm/sensor as an input value.

A Bio-inspired Auto-velocity Detection Algorithm for Autonomous Robots

The robot is bound to turn appreciably towards and

from the wall so the angle of the camera to the wall

will change and thus the measured angular velocity.

The effect of camera rotation on the measured

velocity can be seen in Figure 21.

3) Real-Time Constraints

We assume that images will be streamed at a constant

rate from the CPTS camera so algorithm code must

adhere to real-time deadlines. We measured the time

for the algorithm to determine velocity on a per

captured frame basis over a time period of 60 seconds

for which an average CPU time of 171 μs was

determined (Figure 22.) The operating system

interfering with the execution of the tasks is

responsible for the peaks seen > 200 μs. The

measurement was repeated for the de-Bayering

algorithm and an average of 514 μs was determined

for the execution time.

5 CONCLUSION, DISCUSSION

AND FURTHER WORK

A Conclusion

We have shown by simulation that current

implementations of the fly-eye algorithm for auto-

velocity detection are unsuitable for implementation on

low-cost commercially available cameras. This is

largely due to the fact that the usual technical

construction of the elementary motion detector is such

that sampling times higher than that deliverable by

these cameras are required for accurate measurement.

We have proposed a new algorithm, called the

Distance of Travel algorithm, which is suitable for

implementation on low-cost commercial cameras

operating at low sampling rates. We showed that the

algorithm exhibits better linearity at high angular

velocities than two well-established algorithms and has

potential for real-world application. We implemented

this algorithm and showed through tests that the

promise shown through simulation is reflected in real-

world measurements. The real-time characteristics of

the implementation are also attractive.

B Discussion

The Distance of Travel algorithm was also examined

with the explicit aim of enabling a robot to orientate

and navigate down a corridor and hence both regular

(square wave) and irregular real-world patterns, brick

and concrete, were used during tests.

Whilst the results are good, as the figures for the

square-wave patterns show, some of the simulation

results can not be reproduced in the real-world.

Figure 21: Performance of the Distance of Travel algorithm

implementation at different camera angles.

Figure 22: Processing time distribution Distance of Travel

implementation.

In the case of concrete up to 7.5% error can be

observed in the velocity range tested. Further work is

required to understand and evaluate these

discrepancies. In the real-world it cannot be expected

that regular patterns will be painted on walls to help

mobile robots with their orientation so the algorithms

used need to be robust to variations in wall patterns.

We don’t expect a bio-inspired robot, or one using

bio-inspired sensors, to drive a perfectly parallel to a

corridor wall so we must get some feeling for how

much the robot will oscillate around the line of

direction and, of course, whether this is acceptable in

a real-world environment.

In the case of the concrete pattern, a stochastic

pattern that we can find in our own institutional

buildings, we observe an error of ~7.5%, we don’t

know whether this is acceptable in a real world case.

Neither do we know what the minimum pattern is

necessary for a wall to have so that an acceptable

ICINCO 2017 - 14th International Conference on Informatics in Control, Automation and Robotics

robot trajectory emerges. These are all issues for

further research.

C Further Work

The first focus for further research is mounting the

camera(s) on a mobile robot and allowing it to drive

semi-autonomously down a corridor at speeds of up

to 1 m/s. The final aim is for the robot to liaise with

“pheromone” carrying RFID tags placed at discrete

and longer intervals in the corridor as described by

Doran (2011.) This body of work will include

investigating an extension of the algorithm for the 2D

case and whether and how the robot should be

allowed to move in reverse. General robustness is also

an issue.

The suitability for offloading the algorithm into an

FPGA is also to be examined as we believe that the

combination camera, CPU and FPGA – as opposed to

the use of GPUs - to be the most cost efficient for

mobile robotics. This idea is supported by the

increasing number of SoC FPGA devices with multi-

(hard) cores being offered on the market. A second

reason is that Lichtensteiger (2004) showed that an

optimal fly-eye facet pattern (i.e physical

arrangement and size of photoreceptors) could be

derived for specific tasks. By using a learning

algorithm Lichtensteiger generated one pattern for

navigation along a wall and a second one for

optimised obstacle detection, both in the direction of

travel. By streaming images through an FPGA it is

possible to apply the facet principle multiple times,

like a filter, on different physical locations of the

image. It should therefore be possible to generate

hybrid fly-eyes that achieve different aims at very low

computation expense. Work is needed to show the

viability of this approach.

ACKNOWLEDGMENTS

Thanks are due to Erich Ruff of InES for his kind

support in building the measurement and test systems.

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A Bio-inspired Auto-velocity Detection Algorithm for Autonomous Robots