PRE2017 3 Group15

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(Problem statement and objectives)
(Who's doing what?)
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== Who's doing what? ==
== Who's doing what? ==
1. Litter generation
2. Map generation
3. Collaboration of robots
4. Pathfinding
== SotA: literature study ==
== SotA: literature study ==

Revision as of 16:00, 19 February 2018


Problem statement and objectives

Litter is a very big problem, Especially in city centers. Litter has a lot of negative effects on society. It can attract pests like rats and flies, which can result in human diseases. It can bring harm to animal life. It can increase the use of fossil fuel instead of recycling and it can reduce the sense of safety. To prevent this, public places should remain clean. Currently this is a hard human job, with a low profile. This job could be replaced by cleaning robots, that would clean more efficiently and more thoroughly. That is why the objective of this project is to make cleaning robots work together to clean the streets.

Who are the users?

What do they require?

Approach, milestones and deliverables

Who's doing what?

1. Litter generation 2. Map generation 3. Collaboration of robots 4. Pathfinding

SotA: literature study

Path finding

Litter detection

Robot movement

Litter collection

Robot design

Development of Outdoor Service Robot to Collect Trash on Streets OSR-01

This paper describes the design of an autonomous robot which is to be used to collect trash on the streets. The robot has two wheels to move but drives an already provided route. To avoid objects it uses four 2-D laser range finders. It is currently only able to pickup PET bottles using a hand with five degrees of freedom. It can detect objects using a omni-camera. To measure the distance to the object, it uses two additional cameras. The image recognition is done using a technique known as 'template matching'. This means that the robot has a large library of objects labelled as trash which it compares to the images received from the omni-camera. If the images are sufficiently similar, the robot will pick it up.

Obata, M., Nishida, T., Miyagawa, H., Kondo, T., & Ohkawa, F. (2006). Development of Outdoor Service Robot to Collect Trash on Streets. IEEJ Transactions on Electronics, Information and Systems, 126(7), 840-848. doi:10.1541/ieejeiss.126.840

Development of Outdoor Service Robot to Collect Trash On Streets OSR-02'

This is a follow up on the previous paper. For the new prototype, dubbed OSR-02, an extra hand is added. This allows one hand to hold a trash bin while the other can put the trash in it. Furthermore, the wheels are replaced with crawlers. The sensors and detection system was were kept the same. More detailed tests were also documented, showing that the OSR-02 is able to get over a ditch of 180 mm in width. The robot was also tested in public space, where it was able to successfully pickup plastic and glass bottles in the route and able to avoid pedestrians.

Nishida, T., Takemura, Y., Fuchikawa, Y., Kurogi, S., Ito, S., Obata, M., . . . Ohkawa, F. (2006). Development of outdoor service robots. Paper presented at the 2006 SICE-ICASE International Joint Conference, 2052-2057. 10.1109/SICE.2006.315491

A Study on Development of Home Mess-Cleanup Robot McBot

This paper describes the design of an autonomous robot which is to be used to cleanup indoors. The robot has two arms to grasp the object and a lifting support. Objects are recognized by a RFID tag. After an object is picked up, it is able to place on for example a shelf. Self localization is done by placing RFID tags on the ground.

Ma, Y., Kim, S., Oh, D., & Cho, Y. (2008). A study on development of home Mess-Cleanup Robot McBot. 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. doi:10.1109/aim.2008.4601644

Educational Outdoor Mobile Robot for Trash Pickup

Inspired by the 'Push the Talking Trash Can' of Disney, an interactive low-cost outdoor mobile trash can is designed. With this robot, they aimed to raise environmental awareness, help clean up the environment and promote robotics education among children. The robot is also equipped with a low-cost air quality monitoring system. They purposely avoided autonomous robot because it minimizes control by children and they will find it more fun and have a sense of accomplishment by interacting with, and remotely controlling the robot. Also, autonomous is difficult because the roads in underdeveloped countries often have potholes, uneven construction etc making it difficult to navigate effectively. On the robot a LCD display is mounted to display the air quality and broadcast messages and animations. It can be controlled remotely using smart phone/tablet. The materials used in the construction costed less than 250 dollar.

Pattanashetty, K., Balaji, K. P., & Pandian, S. R. (2016). Educational outdoor mobile robot for trash pickup. 2016 IEEE Global Humanitarian Technology Conference (GHTC). doi:10.1109/ghtc.2016.7857304

Vision-Based Coverage Navigation for Robot Trash Collection Task

This paper describes an algorithm to optimally find and pickup trash and benchmarks this against existing algorithms. The proposed algorithm consists of four distinct steps

1. Follow the wall to obtain the contour and size of the working space. By doing this the working space can be split up into rectangular cells.

2. Scan for garbage in the current cell

3. Find and move to an unvisited area. Repeat step 2 and 3 until all areas have been visited.

4. Deposit trash and move back to initial location

Step 3 is implemented using the 'Boustrophedon Path-Planner' algorithm and a random path planner. It turned out that the 'Boustrophedon Path-Planner' performed better.

Chiang, C. (2015). Vision-based coverage navigation for robot trash collection task. 2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS). doi:10.1109/aris.2015.7158229

Path Planning for Complete and Efficient Coverage Operation of Mobile Robots

The paper presents a method for mobile robots to perform area coverage tasks where completeness and efficiency of coverage are important. The method can be used for robotic de-mining, cleaning, painting, etc.

It is assumed that the robot is operated in an enclosed indoor environment and it knows its map in terms of occupancy grids.

A divide and conquer strategy is employed for efficiency. A cell decomposition algorithm divides the given area into cells (sets of grids):

1. Occupancy grid maps are rotated along their orientation invariant angle so that two identical maps with different rotation result in the same maps.

2. The given area is decomposed into cells based on the change in free space segments for each 'slice' of the map.

3. Noisy cells (created due to complex structures and sensor noise) are merged into larger neighbor cells.

Next, the path is generation for efficient area coverage.

1. Predefined template paths are generated for each cell (back and forth or spiral motion) to find an optimal path to cover them. Predefined templates are used to reduce computational complexity.

2. A path for the overall area is formed from the path that requires minimum time for each cell. A graph search algorithm is used for this purpose.

J. W. Kang, S. J. Kim, M. J. Chung, H. Myung, J. H. Park and S. W. Bang, "Path Planning for Complete and Efficient Coverage Operation of Mobile Robots," 2007 International Conference on Mechatronics and Automation, Harbin, 2007, pp. 2126-2131. doi: 10.1109/ICMA.2007.4303880

A Multi-Robot System for Continuous Area Sweeping Tasks

A trash collecting robot performs a so-called 'continuous area sweeping' task. With this task, a robot must repeatedly visit all points in a fixed area. This paper extends this task to multi-robot scenarios.

The approach described in this paper is not to simply send the robots along the same routes again and again, but to sweep based on a task-dependent cost function. For example, when removing trash robots should prioritise heavily-trafficked areas.

The paper mostly focuses on dividing the overall area between multiple robots.

M. Ahmadi and P. Stone, "A multi-robot system for continuous area sweeping tasks," Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., Orlando, FL, 2006, pp. 1724-1729. doi: 10.1109/ROBOT.2006.1641955

DustCart, an autonomous robot for door-to-door garbage collection: from DustBot project to the experimentation in the small town of Peccioli

DustCart is an autonomous garbage collecting robot. It can navigate urban environments with static and dynamic obstacles. Users can request a garbage removal after which the robot is dispatched, interacts with the user through a touchscreen interface, and receives a garbage bag. Next, it moves to a site where the garbage is deposited. DustCart monitors air quality, temperature, and humidity along the way.

Two DustCart robots were tested for three months in the small town of Peccioli, Italy.

G. Ferri, A. Manzi, P. Salvini, B. Mazzolai, C. Laschi and P. Dario, "DustCart, an autonomous robot for door-to-door garbage collection: From DustBot project to the experimentation in the small town of Peccioli," 2011 IEEE International Conference on Robotics and Automation, Shanghai, 2011, pp. 655-660. doi: 10.1109/ICRA.2011.5980254

System design of a litter collecting robot

Litter is a very big problem, because a broad study of annoyances of the Dutch public found that people rated litter to be more annoying than noise from neighbors and cigarette smoke. There is however a big difference between the places where most litter occurs, which are near sporting facilities and parking lots, and where people perceive it as annoying, which are mostly in shopping malls and on the beach. Furthermore, litter has more consequences than just annoyance. It can attract pests like rats and flies, which can result in human diseases. It can bring harm to animal life. It can increase the use of fossil fuel instead of recycling. It can reduce the sense of safety, and above all, can increase the amount of litter on the street, making this a negative spiral. To prevent this spiral, public places should remain clean.

Currently this is a hard human job with low profile. It is, however, important to keep humans involved in the job, because human cleaners in the streets have a social aspect. Using a robot that cleans most of the litter, and a human to show the robot where it missed certain litter or to help with cleaning up hard to clean litter, the human will have a physically easier job, and has more time for the social aspect of being in the area. It is also important to make the robot quiet and power efficient.

To find the litter efficiently, the robot has a Portable Operator Device, allowing the human cleaner to make a picture of a new type of litter and send it to the robot, so that the robot will recognize it next time. To make the robot power efficient and quiet, a circle of plastic fingers were used and a flap that could close when litter was found. The fingers would then push the can into the hopper. To know where the litter is, scanning laser range finder and a camera were used. The camera takes a picture when the SLRF finds an object, and this image is compared to the litter pictures in the memory of the robot.

G. Bonnema, "System design of a litter collecting robot"

Autonomous Robotic Street Sweeping: Initial Attempt for Curbside Sweeping

Street cleaning can be a coverage or a tracking problem, which both require localization, coverage path planning and tracking control. Using two fisheye cameras and projective transformation, a top view was gained and edge filtering, a Hough transform and RANSAC line fitting was used to find the sidewalk along which the robot has to drive.

J.Jeon, B.Jung, J.C.Koo, H.R.Choi, H.Moon, A.Pintado, P.Oh, "Autonomous Robotic Street Sweeping: Initial Attempt for Curbside Sweeping"

Coverage Path Planning for Mobile Cleaning Robots

There are different ways in which a robot can do path planning in any given environment. The first way is Random Path Planning, in which the robot will move in a random direction until it is obstructed and will then chose a new random direction. A spiraling bias can be added to make this approach more convenient. A more sophisticated way to cover the whole area, is by using Exact Cellular Decomposition. This method splits the room into parts which are easier to cover. Which also makes it more efficient in places with obstacles. A variance on the Exact Cellular Decomposition is the Boustrophedon Cellular Decomposition, which does the same, but makes the parts so that it can be cleaned with a simple back and forth motion. A fourth method is a Backtracking Spiral Algorithm. Which does the same as a random spiral, but takes into account possible blocking objects by moving around them and adding the information gained of the object to make the spiral change shape so that the same places are not cleaned twice. The solution proposed is an extended version of the BCD in 5 steps:

1. The robot moves to the outer boundary of the environment

2. The robot follows this boundary until it has completely circled it

3. A BCD of the environment is created

4. Create a list containing every cell. The first one is where the robot is

5. The cells are covered in sequential order

To make this also work in dynamic environments, the robot will continue scanning the room and adjusting the individual cells when detecting sensor or localization errors.

Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is for a robot to be placed at an unknown location in an unknown environment and for the robot to build a consistent map of its environment while simultaneously determining its location within the map. The problem with SLAM is that the true locations are never known or measured directly.

The probabilistic version of SLAM checks the highest probability for a landmark to be and the robot to be given the history of vehicle locations, the history of control inputs and the set of all landmark observations. The problem with this is that much of the error comes from when the robot wrongly estimates its position with reference to a landmark only once.

To find a solution to SLAM, the programmer needs to find an appropriate representation for both the observation model and motion model that allows efficient and consistent computation of the prior and posterior distributions in the time update step and the measurement update step.

Coaching Questions

Coaching Questions Group 15

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