# The 2SEES prototype: motivation, description and early experiments

Jean Connier, Hong-Ling Shi, Christophe de Vaulx, Kun Mean Hou, Jian-Jin Li, Philippe Vaslin

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Jean Connier, Hong-Ling Shi, Christophe de Vaulx, Kun Mean Hou, Jian-Jin Li, et al.. The 2SEES prototype: motivation, description and early experiments. New Information Communication Sciences and Technology for Sustainable Development (NICST 2017), LIMOS (UMR 6158 CNRS), Jun 2017, Clermont-Ferrand, France. hal-05009870

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# The 2SEES prototype: motivation, description and early experiments

J. Connier#¹, H.L. Shi#², C. de Vaulx#³, J.J. Li#⁴, P. Vaslin#⁵ and K.M. Hou#⁶

# Laboratoire d'Informatique de Modélisation et d'Optimisation des Systèmes, UMR 6158 UCA/CNRS

1 rue de la Chébarde, Aubière, France

¹ jean.connier@uca.fr

² hongling.shi@isima.fr

³ christophe.de_vaulx@uca.fr

⁴ jian-jin.li@uca.fr

⁵ vaslin@isima.fr

⁶ kun-mean.hou@isima.fr

*Abstract*—Many Visually Impaired People (VIP) prefer using white canes to more recently developed Electronic Travel Aids (ETA), in spite of the fact that some functionalities offered by ETA are more advanced than what a white cane can offer. The Smart Environment Explorer Stick project aims to create an easy-to-use ETA; it was started in 2013 and a prototype based on a stroller was developed. After experiments, it was judged that the prototype was not sufficient to continue experiments in realistic conditions and that a new prototype with a cane form factor was required. The new prototype follows a refined version of the SEES concept, called the 2SEES, which emphasizes the global reliability of the system. This paper presents the new 2SEES features and the feasibility of a connected smart stick.

Keywords: 2SEES, VIP, ETA, eETA

## I. INTRODUCTION

The worldwide population of Visually Impaired People (VIP) is large – a report from the World Health Organization estimates that there were 285 million people in 2014, among whom 39 million were blind [1]. When one does not see well or does not see at all, going to unfamiliar places is not a mundane activity, since one’s ability to sense space, build a mental map of a location and navigate in that place is heavily dependent on one’s vision [2]. As a result, VIP suffer from restrictions of freedom: they tend to limit themselves to places they know and avoid trying new places and routes, where the chances that they will get lost are high. Another factor impeding VIP mobility is physical accidents: in a survey done by Manduchi and Kurniawan, thirteen percent of respondents reported experiencing head-level accidents at least once a month and seven percent reported falling at least once a month; as a result, thirty percent reported a loss in confidence as independent travelers [3]. It should be noted that participants in this survey were in generally more mobile, and more keen on using travel aids than most blinds and legally blinds, so the results should be accentuated for the general population.

Travel aids are tools designed to help VIP regain at least part of the ability they lose with low or no vision. These aids have traditionally been long canes and guide dogs;

furthermore, since 1960s, research has been conducted on electronic and then computerized travel aids. Many of these Electronic Travel Aids (ETA) focus on either detecting obstacles on the path of the VIP to avoid physical collisions, or on guiding the VIP from his current location to a desired destination – navigation devices. There are other devices offering further different services, such as delivering context-based information by “talking” when the user is near locations equipped with specific tags. Some devices try to pack several functionalities and to make them collaborate. Upon initial review, ETA seem more promising than at least white canes; however, the usage of white canes largely dominates that of ETA, even in developed countries where the latter are available [ref needed]. This tends to indicate that VIP do not consider ETA reliable, useful or convenient enough to use them. As developers of an ETA, our goal is to provide a tool that can be useful for VIP; we must then try to work on the aspects that could prevent people to use the tool.

Work in this direction has been made during the initial conceptualization of the Smart Environment Explorer Stick (SEES) travel aid [4]. The SEES integrates together functionalities that are often separated in other ETA inside a device connected to the internet. To keep the whole system affordable, part of the tasks are assured by a standard smartphone and low-cost sensors are used to collect environmental data. A prototype was realized, allowing us to realize experiments and progress to a second, more advanced concept we named “2SEES”. The 2SEES is an improvement of the SEES, with a particular focus on reliability. Indeed, we have identified the lack of perceived reliability of ETA as a possible major obstacle to their usage. Reliability is necessary in real-world systems dealing with sensitive tasks such as the guidance and safety of VIP. This paper will present the concept of the 2SEES and the current state of its realization, as well as early data collected by the device in experiments.

The paper will be organized as follows: Section II will present the place of the SEES in the state-of-the-art of ETA. Section III will present the 2SEES concept and the current
state of the implementation of the 2SEES. Finally, early experiments results will be showed in Section IV.

## II. THE SEES IN THE STATE-OF-THE-ART

In this section, we will present a few existing tools and prototypes and from that, introduce the SEES concept.

### A. Travel Aids

The white cane is the most widespread tool. Invented in the beginning of the 20th century, it serves two functions: it allows its user feeling objects at a distance and it makes its user recognizable as visually impaired by other people nearby – in particular, by car drivers. Variations in lengths and colours exist.

Guide dogs are another traditional, non-technologic method for assisting blind people. When in a street, they avoid obstacles, and, on request, they can identify and walk toward street crossings, letting their users cross safely. However, the costs induced by a guide dog are very high [5].

After World War II, people started to design electronic devices to help blind people. The devices were generally meant to fulfil the task of white canes and (part of the) task of guide dogs, that is, to detect obstacles and dangers ahead of the user. The devices of Russel [6] and Kay [7] were among the first. They used an ultrasonic sensor to indicate clear or unclear path (Russel) or the distance to the closest sensed obstacle (Kay). Other ETA used light to detect obstacles instead of ultrasounds. Some used laser, such as the C5 Laser Cane [8], and, later, the Teletact [9]. Others used unfocused light, usually in the infrared range; this was the case of the Tom Pouce [9]. Devices of this type generally rely on a single sensor to provide one-dimensional range information. The user orients the device in the direction she or he wants to scan and gets a distance measurement in result, usually in the form of vibrations, sound frequency or haptic feedback.

The problems associated with these devices are the necessary training required to use the device [9], their cognitive load and their limited robustness. We will not go in details about the training and the cognitive load, and will focus on the limited robustness. This lack of reliability is on distributed several levels. First, on a hardware level: these ETA tend to rely on a single sensor and a single Microcontroller Unit (MCU) to acquire data. This means that if there is a hardware component failure in the sensor, the device will become useless. The second level is informational and environmental. All sensors have strong and weak points, e.g. ultrasonic waves can be absorbed by cluttered environment while light-based sensors are less effective in high luminosity contexts. Relying on a single sensing channel means that there are situations where the device will not be performing as well as it should be.

Along with obstacle detection sensors, localization-based assistance devices are the second main class of ETA (some people differentiate make a difference between “Position Locator Devices” and “Electronic Travel Aids” [10] – we will not). The development of these gained momentum later than that of obstacle detection devices; indeed, several of their

important components emerged quite late. First, they often compute localization using a network of radio emitters whose location is known – an infrastructure. The most famous of these infrastructures is the constellation of Global Positioning System (GPS) satellites, which allows localization in environment where a high enough percentage of the sky is visible. The development of GPS during 1990s and its ubiquitousness due to the low price and availability of parts has rendered the development of localization devices easier. Another obstacle for localization systems is their dependence on geographical data. Systems need to locate the person inside a map to give him or her meaningful information about his or her position. In the early days of ETAs, embedded memory was costly and devices could not have managed a significant amount of mapping information; this became possible in the 1990s. VIP Navigation tools based on GPS are more and more common, in particular with the development of smartphones since 2010s, which typically all include a GPS receiver. Specialized applications running on smartphones provide adapted guidance to VIP. The limitations of GPS-based systems are the precision and robustness of the GPS system itself. For civilian use, without signal from additional ground stations, the accuracy of GPS positioning is limited even in good conditions and decreases when sky visibility is degraded (e.g. in urban or indoor environments). This means that systems based solely on GPS cannot robustly discriminate the position of the user between the two sides of a road, for instance. Systems based not on GPS but another radio infrastructure (e.g. RFID tags or Bluetooth beacons) can be very accurate, but are costly to install and support; the main issue for them is the deployment of the infrastructure.

A third accelerator is the progress of Microelectromechanical Systems (MEMS) that benefitted to the development of cheap, small and low-power inertial sensors (accelerometers and gyroscopes) from the 1990s that are commonly used to compute orientation and displacements. Zheng et al. [11] used such MEMS accelerometers and gyroscopes, attached to the shoe of a user, to track his or her movements. The main advantage of the usage of inertial sensors is that they generally do not depend on the environment to output measurements; they only depend on the motions of the body they are attached to. This makes them ideal with respect to the range of conditions in which they can be used: their measurements will be equally valid in all situations we are likely to encounter. On the other side, inertial sensors suffer from the way their data can be exploited; indeed, the acceleration and angular speed measurements they output need to be integrated respectively once and twice to obtain displacement and angle measures, which are the useful physical quantities we try to obtain. Unfortunately, integrating over uncertain data lead to results with unacceptably high uncertainties and errors as time goes by. MEMS are then practically unusable on their own for prolonged periods of time; one needs to “refresh” the result they give with absolute (i.e. not integrated) information from another positioning sensor.
Recent years have seen the development of ETA based on image processing. In these devices, cameras take pictures of the environment and analyse them to deduce the presence, shape, and distance of obstacles, the location of the user, to detect specific objects, etc. Such systems seem to be a natural response to loss of vision, as images contain the information that is missing to VIP. However, it is difficult to be as efficient and versatile as a human brain with respect to analysing pictures. Computer vision-based devices do not appear to be reliable in all conditions that can be encountered in an uncontrolled environment (e.g. bright sun, reflections in water, night) even when they focus on a detecting a single type of feature in images. Stereovision and Time-of-Flight imaging sensors are often used given their ability to give 3d maps of the environment in single frames. Filipe et al. used a Kinect (a RGB and depth camera) to detect obstacles and stairs with a reported accuracy of 99% [12]; Tapu et al. used a smartphone camera to feed an application detecting moving objects in real-time [13]. Although real-time image processing is now possible, it is still a very demanding activity in terms of processing resources.

Cameras are often used in devices providing what we call “environment perception” functionalities. That is, devices that are not focussed on helping to walk safely or navigating from a point A to a point B task (i.e. the two functions required for the mobility of VIP); instead, these devices provide more “contextual” information about elements of the environment. The user is able to integrate that information to build a richer mental representation of the environment. In this class of devices, we have, for instance, text-reading [14, 15] and signage detection [16].

The GuideCane is in an example of ETA using multiple sensors: it is a motorized wheeled cane which, when pushed by the user dodges obstacles it detects using an array of ultrasonic sensors. Furthermore, it computes its position using odometry and a flux compass, and can use the GPS system to further refine its estimate [17]. Another device taking advantages of several sensors is the Teletact 2 [9]. It is an evolution of the first version with an added proximeter sensor that can provide distance measurements even in conditions where the laser is reflected away.

## B. The SEES

The goal of SEES project is to overcome the limits of traditional ETA and to provide a device that can greatly help VIP. The SEES project was started in 2013 [4] in LIMOS.

One fundamental part of the SEES concept is the reunion of three functionalities in a single system: obstacle detection/walking support, navigation assistance/orientation, and environment perception. These functionalities are all important for a VIP traveling in a known or unknown environment. If they are not integrated together, the VIP needs to manage several devices, which can be a hassle. There is another major argument in favour of grouping these functionalities together: all functionalities exploit sensors and compute information; if information and sensors are shared in a single system, there is the possibility cross-use them to

provide more robustness and performance in all functionalities. This is what we call active multi-sensor context-awareness. The diversity of sensors embedded in the device allows the system to collect and compute contextual information (i.e. all relevant information relating to the situation of the user, e.g. what is around, luminosity, etc.). Active context-awareness is necessary in order to determine the performance of individual sensors. Indeed, as seen before, most sensors are context-sensitive, e.g. infrared range sensors sensitive to ambient luminosity. Active multi-sensor context awareness can also reduce the energy consumption of the device: by contextually switching off sensors that do not provide useful information, the power usage of the device is limited. Power consumption is tightly constrained, since the ETA is battery-powered.

Another important point in the SEES concept is connectivity to the Internet. We believe that internet connectivity can bring very important benefits to ETA – we define a new class of ETA connected to the internet as “eETA”. The SEES system is part of that class; it is distributed on three supports: a smart stick embedding sensors, a smartphone running a specialized application and a cloud platform (respectively called SEE-Stick or Local Mobility Smart Cane – LMSC, SEE-Phone and SEE-Cloud). The smartphone plays the role of gateway between the stick and the cloud platform: on one hand it is connected to the smart stick in a Wireless Local or Personal Area Network (e.g. WiFi or Bluetooth), and, on the other hand, it is connected to the cloud platform through mobile networks such as 3G, 4G or 5G. The first role of the Internet access is the ability for devices to download maps. That way, specialized maps can be hosted and refreshed often, following evolutions in the real environment. Furthermore, having a cloud platform supporting the system means that there is a single element linking all working SEES systems, both to the external world and together. The contributions coming from the external world can be of two sorts: first, alerting the system of specific events susceptible of impeding the travels of VIP (e.g. closed streets due to construction, bus re-routing); second, directly assisting VIP in cases they or the SEES system itself are lost or the SEES. In that case, they will guide the user by giving vocal directions, receiving a video feed from the user. Linking SEES systems together, they can share data, get more accurate statistical calibration of sensors with respect to context, and, maybe more importantly, enrich maps with data collected by the devices.

A prototype has been built for the SEES project. It is based on a baby carriage that has been equipped with sensors, as showed in Fig. 1 – two accelerometers, a gyroscope, a magnetometer, a wheel encoder, a GPS, a camera, eight ultrasonic sensors disposed in a semicircle. The sensor data are collected by a Raspberry Pi that processes their data to compute its position and send it through WiFi to a webserver. Experiments were conducted on dead-reckoning localization, fusion of dead-reckoning position and ultrasonic range measurements with a particle filter, and door and doorplate recognition [18]. These experiments drew us to the conclusion
that we need to further enhance the concept of the SEES and build a new prototype, in light of the limitations that appeared when working on the first prototype. An essential drawback of the SEES concept is that it gives too little importance to the reliability of the system. Reliability is crucial for a device that users shall trust. Other drawbacks stem from the prototype itself. First, due to its form factor (a stroller), it is difficult to conduct serious experiments with VIP; a stroller is far from a cane, even a wheeled one, and, for the testers, it is difficult to evaluate the viability of the device when the main point they notice is its form factor. The second limitation was the rigidity of the software running on the smart stick, caused by the way the prototype was designed: several people working on the project successively added their layer, independently from the others. Circulation and sharing of sensor data were not taken into account, nor were synchronisation or multi-core processing.

<figure><img src="image_2.png" /><figcaption>Figure 1. The first SEES prototype.</figcaption></figure>

### III. THE 2SEES: CONCEPT AND PROTOTYPE

In response to the shortcomings of the SEES, the concept was refined and a prototype was built, which will both be introduced in this section.

#### A. Concept

The 2SEES concept is a refinement of the SEES concept. The major points are kept; the connection to the Internet, the active multi-sensor context awareness and the grouping of walking assistance, navigation assistance and environment perception assistance functionalities together. The 2SEES concept adds dependability to that. As exposed before, most ETA do not focus on providing their service reliably at all levels and in all circumstances; most if not all devices suffer from robustness flaws at one stage at least. At the hardware level, components might fail; at the processing level, a program could have a defect, a bug; finally, at an informational level, sensors can be uninformative depending on the context. These modes of failure are summarized in Table I [19].

<table><caption>TABLE I
CLASSIFICATION OF SINGLE POINTS OF FAILURE</caption><thead><tr><th rowspan="2">Level</th><th colspan="3">Role in the system</th></tr><tr><th>Sensing</th><th>Processing</th><th>Communicating</th></tr></thead><tbody><tr><td>Physical/ hardware</td><td>Single sensor</td><td>Single processor</td><td>Single interface</td></tr><tr><td>Software implementation</td><td>Single implementation</td><td>Single implementation</td><td>Single implementation</td></tr><tr><td>Software algorithm/ informational</td><td>Single type of sensor</td><td>Single algorithm</td><td>Single communication mode</td></tr></tbody></table>

#### B. Prototype

A prototype has been built to test and to try to validate the concepts of 2SEES. It is still under development and subject to change but already able to acquire data from sensors and to send them to a smartphone.

The new 2SEES prototype, this time, takes the shape of a cane; a wheeled cane, but a cane nevertheless. At the base of the cane, a “head” (a model is visible in Fig. 2), currently manufactured in plastic with a 3D printer, is the main platform on which components – mechanical or electronic – are attached. In particular, it holds the wheel and its axis; it has been designed to keep a fixed distance of 0.5 mm to 1.8 mm between the magnets positioned at both ends of the wheel axis and the AS5040 magnetic rotary encoders facing them [Picture n+1][20]. These encoders have been chosen for their reliability: the previous prototype used optical encoders, in which a disc with a black and white pattern tied to the axis rotates in front of an optical sensor detecting transitions between black and white regions. Magnetic wheel encoders measure the direction of the magnetic field generated by a magnet tied to the axis and reports this angle. They have advantages: their measures are more accurate, and they are absolute, whereas the optical encoders usually are incremental only; more importantly, they are more immune to the effects of the environment; dust between the magnet and the sensor will not affect the readings in the way it would have if it had been between an optical encoder and the disc.

<figure><img src="image_3.png" /><figcaption>Figure 2. A model of the “head” of the 2SEES prototype.</figcaption></figure>

Two Raspberry Pi 3 boards with two uSu-Edu boards and two custom sensor connections boards are fixed on the head;
they are the embedded computing resources of LMSC. The uSu-Edu board, visible in Fig. 3, includes the following sensors: a BMP180 barometric pressure sensor [21], an LSM330 accelerometer and gyroscope module [22], an LSM303 accelerometer and magnetometer module [23], a light sensor [24], and an LM75 temperature sensor [25]. Each uSu-Edu board is further connected to the two or three Sharp GP2Y0A02YK infrared range sensors [26] and one or two Maxbotix EZ-0 ultrasonic range sensors [27]. This setup is designed for early tests of fusion of heterogeneous range data for robust obstacle detection.

<figure><img src="image_4.png" /><figcaption>Figure 3. A uSu-Edu board.</figcaption></figure>

Sensors are duplicated to provide redundancy in case of hardware failure. Furthermore, range sensors are grouped in clusters dedicated to detecting obstacles at specific positions: the lowest sensors detect low-hanging obstacles such as curbs, the intermediate sensors detect tall obstacles (e.g. walls, parked cars, furniture) and the highest sensors detect head-level or hanging obstacles, as shown in Fig. 5. These clusters mix infrared sensors and ultrasonic sensors to maximize the invariance of obstacle detection to context changes (Fig. 4).

<figure><img src="image_5.png" /><figcaption>Figure 4. An obstacle-sensing cluster, with infrared and ultrasonic range sensors.</figcaption></figure>

Due to the shape of their detection cone, ultrasonic sensors are not used at the lowest level, as they could not clearly discriminate the presence or absence of a curb since the ground could be detected at a close distance most of the time.

Infrared range sensors, thanks to their focused beam, can fulfil the task. However, the beam needs to remain parallel to the ground to be able to reliably detect its target. To ensure that the beam keeps the right orientation despite unavoidable variations of the angle at which the user holds the cane (Fig. 5), the sensors are fixed on a tilting ring that keeps the right orientation thanks to a weight; the mechanism is visible in Fig. 6.

<figure><img src="image_6.png" /><figcaption>Figure 5. Obstacle detection beams for different class of obstacles adapt to the angle of the stick.</figcaption></figure>

A key point of the prototype is hardware redundancy. All sensors and boards are duplicated so that fault-tolerant capabilities can be tested. The uSu-Edu boards have interesting capabilities in that respect. They can be connected together through General Purpose Input Outputs (GPIO) and 802.15.4; they are linked through a serial port to the Raspberry Pi's, of which they also control the power supply. That way, a watchdog reset timer functionality can be implemented; when a uSu-Edu board detects that its Raspberry Pi is defective, it can send a special message to the other uSu-Edu board, which will then start or wake up its own Raspberry Pi and take on making measurements.

In the first prototype of SEES, the Raspberry Pi was responsible for all data collection and processing. With the new prototype, the uSu-Edu manage data collection; later,
other low-power microcontrollers will be added in order for
low-level data processing in order to keep power consumption
within a reasonable range. Part of the information processing
will also be done on the smartphone. This represents a shift
from a centralized computing device to a distributed and
parallel device. Multi-core functionalities can be implemented
in the custom operating system running on the uSu-Edu
boards.

In its current state, the prototype is dedicated to
measurements and collecting data for analysis. The sensors
are connected to uSu-Edu boards, which sample them. They
then send the data to the Raspberry Pi's using serial port,
which decodes them and then forwards them to the
smartphone through a Bluetooth connection. The highest
range cluster, the one in charge of detecting hanging
obstacles, has not been attached to the prototype yet.

<figure><img src="image_7.png" /><figcaption>Figure 6. Tilting ring holding the sensors at the right angle.</figcaption></figure>

IV. EXPERIMENTS WITH THE 2SEES PROTOTYPE

In this section the objectives of the experiments (i.e. what information we want to learn) will be presented first; we will then discuss about the experiments' setups and how they can help us achieve the objectives. In a second part, the actual results and early analysis will be presented. In the third and last part,

A. *Objectives and setup of the experiments*

The first objective is to test obstacle detection capabilities.
The first experiments should bring us information on the way
range signals should be conditioned and how to calibrate the
sensors for optimal accuracy. It is the first step to detecting
obstacles. Furthermore, the influence of illumination on
infrared range sensors should be assessed. This way, we will
be able to start experimenting with active multi-sensor
context-awareness in the future.

The setup of the experiment is the following: a sensor
cluster (two infrared sensors and two ultrasonic sensors) was
placed in front of an obstacle (a cardboard box covered with a
sheet of white paper), at a known distance (+/- 1 cm). Both
infrared sensors were used, while only a single ultrasonic
sensor was used, to avoid interferences. The distance was
measured in a dark setting for approximately five seconds, and

then the scene was lit from the side, close to the axis of the
detection beam, by a naked 19W halogen light bulb (Fig. 7),
and the distance was measured for approximately five seconds
again.

<figure><img src="image_8.png" /><figcaption>Figure 7. Setup of the range sensors experiment.</figcaption></figure>

B. *Results and discussion*

A sample of the range measurement experiment (the 100
cm distance measurement) is visible in Fig. 8.

<figure><img src="image_9.png" /><figcaption>Figure 8. Sample collected at 100 cm distance (purple: infrared 1; yellow: infrared 2; black: ultrasonic; red: luminosity).</figcaption></figure>

The top, pink signals is the first infrared sensor, while the
yellow signal is from the second infrared sensor. The black
signal is that of the ultrasonic sensor and the red signal is from
the light sensor. We can see that the infrared signals contain
noise with stable frequencies. If we abstract from this noise
we can see that one sensor is outputting a value of about 114
from the ADC while the other is outputting around 73; the
ultrasonic sensor approximately outputs a value of 84. Such
"expert" (i.e. derived from human judgment) data have been
collected at different ranges. Fig. 9 shows the sensor response
with respect to the distance to the target, using the "expert"
data; the shapes of the curves do fit the sensors respective
datasheets. These curves, reciprocated, form the basic
calibration of the sensors.
<figure><img src="image_10.png" /><figcaption>Figure 9. The responses of the three range sensors.</figcaption></figure>

Fig. 10 shows a comparison of the signals at three distances. (25 cm, 100 cm, and 245 cm). For all sensors, the noise level increases with the distance; however, in the case of the ultrasonic range sensor, the effect is much more visible. This could be due to the “visible” surface of the object by the sensor: the detection cone of the sensor is wide, so, when the object is further, it only occupies a small portion of the cone, resulting in a lower “confidence” in its presence. Tests should be carried to know if this noise mainly depends on the distance of an object or on its “apparent” diameter; it could be an important characteristic of the sensor when the smart cane needs to detect thin elements, e.g. standing sign poles. The noise on the infrared sensors seems more electronic than environmental (it has one, maybe two fixed frequencies); it starts at closer distances but does not reach levels comparable to that of the ultrasonic sensor. For both cases, a human can judge the probable level of the real information (i.e. the distance measure without the noise) because there seem to be three parts composing the signal: a flat DC component containing the information, and fixed frequency noises unbalanced. In this static setup, it is easy to detect and correct; a rough quantization followed by a majority moving window should clear the spikes; but in situations where the stick is moving, more information about the noise will be necessary in order to eliminate it.

<figure><img src="image_11.png" /><figcaption>Figure 10. A comparison of the sensors signals at three distances.</figcaption></figure>

As seen in Fig. 8, there is no effect of luminosity on the ultrasonic range sensor, as one would expect (the lower range measurement is caused by the detection of the lamp by the sensor due to its wide detection cone). However, the effect on the infrared range sensors is surprising: it makes the previously unbalanced (negative only) noise signal balanced (visible in the “B” zone).

### C. Further experiments

Many tests still have to be conducted. First, in the continuity of what has been done, the influence of the position of the lighting should be evaluated, as well as the influence of the colour of the target. The effects of the target texture (e.g. solid flat, rough, fabric, etc.), in particular on ultrasonic sensors, should be studied as well. For both types of sensors, the influence of the angle at which the beam hits the surface and the shape of the surface shall be checked. Experiments should be conducted to learn about the ability of the system to discriminate between different types of obstacles (low obstacles, e.g. curbs, standing obstacles, e.g. furniture and walls, and hanging obstacles, e.g. open windows).

### V. CONCLUSION

In this paper, a new prototype of eETA that will be used to implement the 2SEES concept was introduced. The 2SEES concept refines the SEES concept by taking into account the reliability of the whole system, which, we believe, is a key point for ETA adoption and usage. This prototype is an improvement over the former SEES prototype, for two reasons: first, by using redundant components, it can be a basis for the development of a fault-tolerant device; then, its cane form factor allows for experiments involving VIP in realistic conditions, which the previous stroller-based prototype did not.

Early experiments have been conducted on the range sensors taking into account different lighting conditions; further experiments are needed to design a fusion scheme that will be used for reliable obstacle detection.

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