PRE2017 4 Groep5

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Group Members

Name Study Student ID
Ahmed Ahres Software Science 0978238
Quinten Maes Psychology & Technology 0955972
Hugo Melchers Mathematics & Software Science 0994280
Christel van den Nieuwenhuizen Psychology & Technology 0940672
Frank de Veld Physics & Mathematics 1010914

Introduction

Automation is one of the biggest changes taking place in industry today. Nowadays, systems are being automated to the extent that they require almost no human intervention. Such a technology has been successful not only in manufacturing, but also in the automotive industries. Self-driving cars or even drones are examples of where automation has seen research and development in flight.

Suitcases are also an example that can benefit from automation. By making suitcases autonomously following their owner we can facilitate the traveling process by making it less tiring, especially for the elderly, disabled people and pregnant women. Moreover, this would allow a more efficient transportation of clothes and objects, which could be useful for business travelers or regular flyers. Research has shown that people were enthusiastic and trustworthy towards a prototype of a robotic suitcase after using it [1]. However, one must keep in mind aspects such as security, object detection and components to be able to fit in a plane according to airport security standards.

We are developing a smart self-following suitcase in order to make traveling and transporting objects more efficient and less tiring. This system requires different technologies: Bluetooth connection to be able to follow a device owned by a person, GPS tracking to avoid getting lost by the owner, security procedures to avoid getting stolen and computer vision for obstacle avoidance. In this project we detail a framework to build such a system, present an object detection algorithm that can be used by the suitcase for obstacle avoidance as well as a user interface for a mobile application that can be used by the owner of our system.

Problem Statement

How can we securely make traveling and transporting objects more efficient through autonomous suitcases ?

The smart system created should follow its owner within a certain of range of distance, be protected from theft and avoid getting lost.

Project Planning

Approach

We aim to design a motorised suitcase that can be configured to follow its owner. First, we will develop a vision for a nominal use case of such a motorised suitcase. From this vision, we will extract user requirements. Then, we will give a high-level architecture showing the different components that will be used (e.g. computer vision, electric drive, etc.). We will do research regarding all these components, gathering the knowledge required to combine them into one device. Finally, we will present a design for a motorised suitcase based on our vision, the user requirements, and the technicalities of the used components used.

Weekly Planning

Planning-Group5-2017Q4.jpg

Milestones

The following milestones are set for this course. They are shown in the planning aswell.

  • In the first week every group member finds and summarizes the articles for their part for the state of the art. The entire introduction and accompanying parts are written.
  • In the second week the literature study is done.
  • In the third week the requirements and UI design are finished.
  • In the fourth week the USE analysis is finished.
  • In the fifth week all components for the prototype are available
  • In the sixth week the system specifications are finished
  • in the seventh week the implementation is done and the prototype is tested
  • The last milestone are the deliverables.

Deliverables

For our end deliverables we have to do the following:

  • A presentation, which will be held in the last week. In this presentation we will discuss our findings, present a solution to the problem and if possible give a demonstration.
  • Several prototypes to show some of the basic functionalities that we want to implement in our suitcase.
  • This wiki page containing the technology mentioned earlier, taking into account the advantages, disadvantages, costs, and impact of such an implementation.

Users

In general the users for this kind of technology will be people who regularly have to cover distances with baggage and either have trouble with that or would like to have their load relieved. This mostly covers business travelers, pregnant women, elderly and disabled people. The general area of use would thus be airports, train stations, bus stations, naval docks and urban areas. It is pretty important that these technologies are used in relatively smooth and flat terrain. In the case of personal suitcases, the self following suitcases would be bought particularly by the users.

State of the Art

So far, a few companies have already tackled the case of self following suitcases. At several events such devices have been shown or demonstrated, but so far none have been sold to particulars yet. The companies currently working on a self following suitcase are:

It is important to note that all of these companies, the parent companies included, have been founded in the previous 3 years. Two of these companies, Travelmate and Cowarobot, needed crowdfunding from the crowdfunding website indiegogo.com in order to be able to produce their suitcases, Travelmate even needed two (https://www.indiegogo.com/projects/travelmate-a-fully-autonomous-suitcase-and-robot, https://www.indiegogo.com/projects/travelmate-a-fully-autonomous-suitcase-and-robot--2#/comments and https://www.indiegogo.com/projects/cowarobot-r1-the-first-and-only-robotic-suitcase). Both companies had delivery dates around end 2016 / begin 2017, but both experienced production delays and have not yet delivered the suitcases to their customers. Furtheremore, Travelmate has doubled to tripled their prices compared to their original plan (€400 to €600), which was comparable to the price of the one from Cowarobot. Nevertheless, both companies received an overwhelming amount of support from their backers; 400% of the necessary funds was received by Cowarobot and 1600% of the necessary funds were received by Travelmate. The other four companies do not give specifications on either the release date or the price, only NUA Robotics and ForwardX hope to release it at the end of this year.

Also interesting is that five of these six companies claim to have made the first self following suitcase; ForwardX is not claiming this. Additionally, NUA Robotics is a very small company with only 2-10 employees and their site is hosted by the third party company Unbounce; the company does not look very professional. To summarize; there are several companies currently working on the idea of an self following suitcase and they are at varying stages of releasing the technology in form of a product but it seems most of the companies have run into some issues and none are delivering yet. However, since the companies are already promoting their idea a lot, there are also numerous responses from potential buyers, both negative and positive. This is a really important source of feedback, since this technology is largely based on the users and their preferences. The most important positive remarks are:

  • It is an innovative idea
  • It is efficient
  • It can help disabled people, elderly and pregnant women to transport their suitcases without health risks

The most important negative remarks or fears are:

  • It could be stolen easily
  • It could be vulnerable to hacking
  • The weight could become too high, since the allowed weight for hand luggage is limited
  • The size of the batteries might be too high, since the guidelines for batteries on aircraft are strict
  • Terrorism could become easier
  • It might not useful enough for the target group. This can of course be avoided by searching for other applications, such as aiding elderly or disabled people and improving efficiency in work environments.

It is important that these subjects are adequately thought of in the design of such a suitcase.

However, the case of self following trolleys does not seem to have been explored yet. One device which resembles this idea is the Stewart Golf X9 Follow (https://www.stewartgolf.co.uk/trolleys/x9-follow/), a self following golf cart and another device is a prototype of a self following shopping cart (http://www.technion.ac.il/en/2014/11/a-shopping-cart-that-follows-you/). The golf cart seems to be the only one of the products listed in this section which is actually for sale. Thus, no self following trolleys have been made for airports, retail stores, train stations or other places where a lot of products need to be moved. Since the golf cart is the only actual product resembling the technologies aimed for in this product, it is interesting to investigate how this company has approached the problem of self-following trolleys. The golf cart uses Bluetooth and a separate remote which the user should keep with him and the range is 50 meters. The cart rides a few meters behind the user when on ‘Follow-mode’, but apparently it sometimes ‘chases rabbits’. The battery life is 25 to 30 holes, which means a few hours. Furthermore, it has downhill braking and an integrated stabilizer, which are important to have on golf courses. Of course, this machine is only useful for transporting golf clubs and golf accessories.

For the intended devices, several important (software) components and concepts are needed, of which there generally is a lot of research about. The important software concepts are autonomous image recognition, obstacle avoidance, remote tracking, control systems, autonomous driving and Bluetooth/GPS connections. Hardware subjects that are relevant are battery efficiency, the most optimal motors and wheels, Arduino connections and battery life. Furthermore, there are general subjects like privacy, security, user preferences and (airport) regulations.

A list of relevant sources (scientific papers and patents) follows underneath:

  • Patent for self-following vehicle. This patents is actually pretty short and concise. It refers to other patents as well; it combines several systems into the concept of a target following devices. These systems are: a frame with a front and rear side, wheels, a driving system for wheels, a control system, a remote target unit and a stereoscopic detection system. [2]
  • Patent for a small DC motor. DC motors have of course existed for a long time, but this patent is specifically about a mini-sized DC motor. The important components of the DC motor are: a motor frame with a cylindrical portion with constant thickness, field magnets and a minimum sized air gap between the magnets and the frame to be able to rotate the armature assembly. [3]
  • Research paper on the design and implementation of a mapping robot with omni-wheels and a Raspberry-Pi. While the aim of this robot is different than in this case, the general architecture of this robot is still useful. Some requirements that are overlapping are: lightweight, movement at walking speed, enough space for sensors and electronics and relatively cheap materials. It was decided that four omni-wheels would be used for their mapping robot with one electric motor on top of each, as well as two double sided L298 motor drivers, an Arduino based controller, a Raspberry Pi as on-board computer and a Li-Pol 7.4 V battery as power source. The sensor which have been used is an ultrasonic HC-SR04 distance sensor. The reason why this article might be useful for this project is their conclusion that it is perfectly possible to create a robot with very general components which aim resembles the aim pursued in this project. The major difference is the weight it should handle and the autonomy of the robot, which can be tackled by using bigger motors, batteries and a stronger on-board computer. [4]
  • Research paper on the power and propulsion system for an autonomous robot. This paper is written about a robot participating in the RoboCup competition, a major robotics competition which has the goal of creating a robot soccer team which should win from the human team in 2050. The competition is attended by hundreds of teams each year and the robots get better each year. In this paper, the optimal energy storage system and propulsion system are researched. It is concluded that a Li-polymer battery of 25.9 V assisted by two Li-polymer batteries of 7.4 V each are the optimal power sources, since the power density is high. For the motor system, two cascaded cell modules for controlling motor speed and power flow control are used with DC-DC converters and a kicker circuit. The motor itself is a permanent magnet DC motor. An important remark made in the paper is that deep discharge needs to be avoided for safety and protection of the system. Thus, a basic voltage needs to be present so that the battery life is extended. Furthermore, for this project it is important that the batteries follow airport security guidelines. [5]
  • Research paper on a person-following autonomous trolley. This paper was written about the design of a robot that follows his user using both ultrasonic sensors and Bluetooth connection. The robot also reacts on human speech. The paper is mostly focused on the control style variants for robots. In many ways, this paper resembles this project, since it is about self-following robots used in mainly supermarkets, but use for disabled people, sports and military is also suggested in the paper. The approach of the researchers is different than the approach in this project however, since they consider 3D sensors and image processing not cost efficient. Ultrasonic sensors are used instead. While this can certainly work in supermarkets, it might be more difficult to make that work in busy areas as airports. Furthermore, an important remark is made, namely that GPS might be too unreliable at these distances. The robot made for this research paper mainly has components which are also present in similar robots found in other papers, namely two DC motor, a L289N motor controller, a Bluetooth module, an Arduino Mega on board computer, a 12V battery and regular mini wheels. [6]
  • Research paper on a novel object avoidance algorithm that is easy to tune [7]
  • Patent for an object detection system [8]
  • Research paper on the linear time-invariant control system [9]
  • Research paper on object detection using convolutional neural networks for classification [10]

Requirements

USE Aspects

References

  1. ↑ Alves-Oliveira, P., & Paiva, A. (2016, October). A study on trust in a robotic suitcase. In Social Robotics: 8th International Conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings (Vol. 9979, p. 179). Springer.
  2. ↑ Hendrik, W. (2014). U.S. Patent No. EP 2 590 041 A3. Washington, DC: U.S. Patent and Trademark Office.
  3. ↑ Kuroda, M. (2009). U.S. Patent No. US 7598706 B2. Washington, DC: U.S. Patent and Trademark Office.
  4. ↑ Krinkin, K., Stotskaya, E., & Stotskiy, Y. (2015). Design and implementation Raspberry Pi-based omni-wheel mobile robot. 2015 Artificial Intelligence and Natural Language and Information Extraction, Social Media and Web Search FRUCT Conference (AINL-ISMW FRUCT). doi:10.1109/ainl-ismw-fruct.2015.7382967
  5. ↑ Ghaderi, A., Sanada, A., Nassiraei, A. A., Ishii, K., & Godler, I. (2008). Power and propulsion systems design for an autonomous omni-directional mobile robot. 2008 Twenty-Third Annual IEEE Applied Power Electronics Conference and Exposition.
  6. ↑ Krishnamohan, T., Mahendran, A., Paramananthasivam, V., Selvakumar, A. (2016). Human following Trolley-Auto Walker, SLIIT conference 2016
  7. ↑ Sezer, V., & Gokasan, M. (2012). A novel obstacle avoidance algorithm:“Follow the Gap Method”. Robotics and Autonomous Systems, 60(9), 1123-1134.
  8. ↑ Liu, M. Y., Tuzel, O., massoud Farahmand, A., & Hara, K. (2018). U.S. Patent Application No. 15/218,182.
  9. ↑ Ju, P., & Zhang, H. (2018). Achievable delay margin using LTI control for plants with unstable complex poles. Science China Information Sciences, 61(9), 092203.
  10. ↑ Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2017). Object detection networks on convolutional feature maps. IEEE transactions on pattern analysis and machine intelligence, 39(7), 1476-1481.
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