PRE2017 3 Groep15: Difference between revisions

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''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''
''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''


== Coaching Questions ==
== Coaching Questions ==


[[Coaching Questions Group 15]]
[[Coaching Questions Group 15]]

Revision as of 15:33, 16 February 2018

Problem statement and objectives

Who are the users?

What do they require?

Approach, milestones and deliverables

Who's doing what?

SotA: literature study

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

Coaching Questions

Coaching Questions Group 15