PRE2016 3 Groep7

Group 7 Wiki

Welcome to the wiki page of group 7, where we present the development of our project: the intelligent G.U.A.R.D. robot. Over the course of 8 weeks the progress of our group will be shown here, such as a planning, the research topics (e.g. literature studies, designs, simulations) and the results. Feel free to take a look around!

Subject: After the brainstorming session in week 1, we came up with a couple of application fields that we could direct our project towards. These included ‘Smart homes’, ‘Guidance robots’ and ‘Cleaning’. Out of these fields we chose the topic of ‘Guidance robots’. Now it was time to find a specification within this field, and then we thought of the lack of safety on the streets, that is often highlighted in TV-programs such as ‘Opsporing verzocht’. While streets and buildings are full of cameras in the current day and age, the obtained evidence of criminal actions is often lacking, due to e.g. a too low camera resolution, a wrong mounting angle of the camera, and/or the criminals themselves, that often demolish the cameras during the crime. Could this be improved by using robot technology? We think that is possible, and present the G.U.A.R.D.: the Guidance Unit And Route Director. The G.U.A.R.D. is a robot that is mainly intended for escorting people that feel unsafe during nighttime. It can guide people to e.g. their home, but its main function is to protect them, for example by calling for help (to 112/911) or by planning routes that avoid less safe streets in a city. This robot could be designed with varying specifications, tailored to the exact functionality that is desirable in a given application area (e.g. a metropole, a city or a small village).

Objectives: The objective of our project is to improve safety and accessibility of living environment. This will be done by creating a concept for a guidance robot in order to help people to quickly and safely reach their destinations. Individuals or small groups that feel unsafe or require additional support to reach their destination can request the help of such a guidance robot.

In order to complete the tasks the guidance robot needs to be able to:

- move in order to guide people

- interact with people

- pathfind through real life real complex and unpredictable paths.

- find optimal routes for clients to guide them to their destination quickly and unharmed.

- call for support when a dangerous situation occurs.

Optimal design choices will need to be made for each of these points.

Approach

Our solution to the problem of unsafe streets and possible dangers at night is the use of a Robotic system that will guide its users to a safe area, for example their homes. This system has been dubbed the GUARD-system. (Guidance Unit And Route Director). This system consists primarily of Robotic ‘bodyguards’. These robots can either wait at places where they are most likely needed, patrol around areas or be called upon by using a smartphone app. It is then possible to state your destination to the robot, which will then accompany you to that destination. To create the feeling of safety, it will have practical options such as a connection to the local police station. To implement the GUARD-System we need a robot with integrated area knowledge and pathfinding, as well as camera’s and motion sensors to avoid it from bumping into obstacles. It also needs to be stable and rigid, so users can support themselves if needed. Also important is an internet/phone connection for calling aid when needed. Some voice recognition is needed to optimize the system, this is useful for receiving directions and recognizing danger, for example. Since streets are less safe at night, the GUARD-system will primarily be used then. At daytime, the robots can either be stored away and charge, or they can be used as city guides in touristic areas.

State of the art

Danger recognition

One of the important technologies on our robot is being able to recognize threats to the escorted client. One main technology which has this are the so-called “smart” CCTVs.

This technology is not yet widely spread. Smart CCTV refers to visual surveillance systems that analyse and interpret video footage by using pattern recognition technologies. This is even so far developed some cameras can notice violence. The CCTV can recognize this through a person’s irrational or random movement usually seen through violence. This is important for GUARD, the escort robot, as it needs to independently notice threats and then notify local authorities.

A technology can also be used to incorperate existing CCTV-networks into our design. This is called a System of Systems. System of systems (SoS) is a network of complex autonomous systems which interchange information. Several aspects are important to make the SoS work.

-Interoperability

The agents in the system should work separately and autonomous. Each system in the SoS should be able to distribute information to all other systems and all systems must be able to interact autonomously inside and outside the system.

-Integration

Each agent should be able to control the SoS and communicate not depending on their characteristics, like software or position. This is the reason why in a SoS all systems need to talk in the same language. Else two agents might not be able to interact directly.

-Autonomy

As said before each agent should act independently and be able to fulfil its task needing no assistance, outside the extra information gained through the SoS.

To incorperate this, a central language between the robots and sensor networks needs to be found. A way with which a threat is separated from a non-threat needs to be obtained from another technology, such as the “smart CCTVs”.

One thing GUARD needs to detect is violence. There are some algorithms which do this exactly. These use a database of sounds and videos of fights, gunshots, blood and explosions. As these are all signs of danger. This is a form of action recognition. Sadly violence detection is one of the least studied versions of this. Enrique Bermejo Nievas et al. (2011) propose that using a database will bring great results detecting violence in certain situations. They wanted to detect violence and created a database of 1000 ice hockey videos which included violence and 1000 action movie fights. They found that a descriptor called MoSIFT, can detect violence in these situations with an accuracy of 90% using the databases. This can be used by our robot. First needed is a huge database of videos of street violence, like beatings, and including a database of sounds which could be sounds of threats or gunfire. This than can be used by a descriptor like MoSIFT or one similar to recognize threats and violence during the escort mission of our robot and thus bringing the client to safety and notifying human authoraties.

Security robots

The threat detection is also common in another technology than CCTVs. The security robots. While not extremely developed the security robots show promising ideas which GUARD can use to know when to notify the police and the person it escorts.

Autonomous security systems have great potential. Though there is one main difference between the security robots and GUARD is that regular security robots are mostly deployed when no person may enter and thus can report all noticed intruders. GUARD on the other hand is used on the public streets. It could give a sort of tag to its client but it will still run into many citizens who aren’t a threat and don’t have to be reported. Thus regular heat and movement detection won’t fully cut it for GUARD. A method to further establish how a threat will be defined is needed.

Thus further research would be to also incorporate the danger recognition of the smart CCTVs into the security robots and maybe even combine the different robots and a CCTV system into a SoS. This could greatly improve the efficiency of the GUARD.

A hugely invested in and developed security robot is the Knightscope K5. This is a security robot designed to patrol bigger open areas of companies’ buildings. The company claims this robot can autonomously patrol a certain defined area and can detect anomalies and report these to a central station which is operated by humans. These anomalies are detected using cameras in the infrared range and visual range. It can also pick up wireless signals and thus detect phones and other internet connected devices. Using this it can detect unfamiliar devices which can be a sign of espionage or intruders. The company is now working on gun detection but the company won’t reveal how this will be done. A smaller version, the K3, is a similar model with slightly less features to be used in more enclosed areas.

If the K5 is as good as the Knightscope company claims then this would be a good starting point from which we can base our own design off from. As both GUARD and the K5 are used in open areas, and both need to detect anomalies. Though GUARD needs to better distinguish between threat and friendly as it will be deployed in a larger more public environment.

Autonomously Moving AI

Another important piece of technology the GUARD-robots need is the ability to move autonomously. This technology is a very busy area of innovation, as it is currently a very important area in the use of self-driving cars. The technology described in this paragraph will primarily be based on the current workings of self-driving cars, since most information and innovation resides in this area.

Autonomous moving is needed for the GUARD robots, because the alternative would require remote controlling. At this point one should wonder if robots are needed at all, since then it would be easier to send actual people instead of letting them control the robots from a distance. To create an autonomously moving robot, a few technologies are required. The most important technology is GPS. Without it, the robot is unable to know its location, route and destination. With the GPS-system, the robot only needs to be able to receive inputs from its users to function on a basic level. With these basics, the robot would in theory be able to move the way it is designed to. However, the problem then lies in the fact that roads aren't flat or unoccupied.

Influence of cameras on crime To know if the GUARD will help, first there has to be looked at the influence on the next closest technology. The one that is used long enough and widely spread enough is CCTV, or else known as camera surveillance. A study of the effects was done in 1999 in the United Kingdom, the record holder of cameras per capita. This study was done by Coretta Phillips.

The author says the main claimed effects of CCTV were:

1. Caught in the act: catching a crime red-handed could increase chance offenders will be caught and persecuted and thus deterred.

2. Framing: CCTV could reduce crime by deterring away potential offenders who don’t want to be caught on camera

3. Nosyparker effect: reduction of crime could happen because people will use the observed area more, and thus human surveillance will increase as well.

4. Effective Deployment: Better knowledge of risk areas is gained and thus security workers can be better deployed, reducing crime.

5. Publicity (general/specific): As an area is known to be observed criminals will less likely go to this area to perpetrate a felony. As it looks like people take crime more seriously.

6. Time for crime: The longer a crime takes the more likely its caught on camera. Short crime might not be as effected as its longer counterparts.

7. Memory Jogging: publicity encourages potential victims to be more conscious and take precautions.

8. Appeal to Cautious: Those who are more security minded use observed areas more than careless people, thus a displacement of victims is caused.

For our moving robot most effects are of interest. Effects 3 and 7 are less important as our robot moves with our client and does not observe only a certain area. And the robot is the precaution that can be taken and thus already fulfils this role. The author than looked at the actual measured effects of CCTVs on crime.

First she looked at property crime in streets. This showed promising results. Areas which had at least two cameras had a noticeable decrease in crime in comparison to unobserved areas. Also no displacement effect was noticed. Overall crime was reduced, This confirms effect 2, cameras do deter potential criminals. At least for property theft, which also includes vandalism and burglaries. The effects faded slightly though for petty thefts and vehicle thefts.

In areas like markets the effect was also looked at. This gave also promising results as seen in table 1. This is the effect of CCTVs on property crime in the town centre of Newcastle. This shows that in busier areas the effects are very big. These big numbers are attributed to effects 1 and 2.

In another town centre in Airdrie, Scotland, similar effects were noticed. In total crime was reduced by 21% in two years. Crimes like housebreaking, theft from motor vehicles and shoplifting, reduced by 48%. Arson and mischief, like graffiti, reduced by 19%. And again no major displacement of crime was seen. An exception is shoplifting which did slightly increased in Glasgow until CCTV was installed there. Thus areas without CCTVs did not experience as great benefits from observed areas. A big plus is the major increase in reported crimes. There was a 116% increase in detections improving effects 1 and 4, caught in the act and effective deployment. And similar number were noticed across the country.

There were some areas like Doncaster where the influence of CCTVs was only 6%. There were major reductions in criminal damage, robbery and theft-person, but no decrease of shoplifting, burglary, violence or drug offences. In a very few areas no real effect was noticed. These are mostly indoor housing projects where most of the crimes were done by the residents. Though there were several problems with this study. It was done in less than a year after instalment and the effects might not have been established. And also residents who should have monitored the CCTVs were neglecting it. Only 14% of the residents said to daily watch the monitored areas and 33% said they don’t want to report a crime in fear of retaliation. This could have had huge impacts on the effects of CCTVs.