Following the previous posts, I will analyze several models used in WRO 2019 Junior Group competition.
The game description, rules and scoring could be found at WRO website: https://wro-association.org/competition/previous-challenges
This model has a clip-like attachment, which could open and close. This type of attachment was also popular in previous WRO competition, such as WRO 2017 Junior Group models. There are two motors controlling the attachment. One motor is responsible for tightening and loosing of the clip, while the other motor is responsible for lifting up and putting down of the clip.
The field map of 2019 has some apparent change from that of previous years. It becomes more colorful, using colours to indicate different functional zones. Those colour zones might confuse the color sensor of the robot model, but if using them skillfully, the robot could better position itself on the field.
In this model, its front attachment could pick up one or two bulb blocks. If you observe carefully, you could notice a short beam in the middle of the clip which could separate two bulb blocks. When the robot is going to dismantle one bulb block, the clip is opened with a small angle, so the bulb block in the front is put down. When the vehicle is going to dismantle two black bulb blocks together, the clip spins and opens with a big angle, thus the two blocks are put down together.
For WRO 2019 Junior Group competition, what is the most challenging is not to design attachment, but how to plan the route and put bulb blocks to their destination quickly. Since the game introduces more randomness in the initial positions of the blocks, the program needs to consider different scenarios and plans the route smartly.
Compared to the other two models I will introduce later, this model’s route planning is easier to understand. Firstly, the model starts off from the base, turns left to pick up yellow bulb blocks. Please note that there might be one or two yellow bulb blocks, which is based on the draw result prior to the robot running.
A color sensor is mounted on the top of the vehicle. When the clip is lifted to the upper position, the color sensor is facing straight toward the first block on the clip. So what is the purpose of this colour sensor?
If there are two yellow bulb blocks loaded, the color sensor could identify yellow color. The program then knows that an black bulb block is put in the Area 1 of Green Room, shown in the above video. The robot will pass the Area 1 of Green Room (because the program knows that it is occupied by the black bulb block) and put the two yellow blocks in Area 1 and Area 2 of Yellow Room, respectively. After that, It will carry the black bulb block from Area 1 of Green Room to Trash Area.
However, if there is only one yellow bulb block picked up, the colour sensor mounted on the top could not identify yellow colour, so the program knows that an black bulb block is put in the Area 1 of Yellow Room. Guess what will the robot do? It might go straight to Area 2 of Yellow Room and put down one yellow block. After that, it will pick up the black bulb block from Area 1 of Yellow Room and put it into Trash Area.
The logic to handle yellow and green blocks is a bit brain twisting, while the logic in the latter part of the mission is easy to be understood. The robot transfers the green, red and blue bulb blocks one by one. A colour sensor mounted in the middle of the vehicle could identify the color of those bulb blocks when approaching them and then carry them to the proper destinations.
This model has a unique design. It allows for storage of maximum three blocks simultaneously. Its front attachment consists of two trap-like cases. They are controlled by two medium motors separately. When the cases capture the bulb blocks, they are lifted up to trap the bulb blocks inside. The third bulb block could be slid into a slot of the vehicle. Through carrying three bulb blocks for one ride, this model reduces the transportation time effectively.
So how does this model optimize its route planning and increase efficiency? When it approaches those smart bulb blocks, it uses a color sensor to identify the color first. Depending on the color checking result, the vehicle decides whether to twist its body left or right a little bit, so that the front trap could fetch the block on the left or right side. It is arranged in a way that the left case should carry yellow bulb block and the right case should get green bulb block. In this way, the vehicle could put down yellow and green blocks in Area 2 of Yellow Room and Green Room simultaneously, further leveraging its effort. The similar tactics applies to red and blue blocks.
Another point to improve efficiency is to leverage the transfer of black bulb block with other colorful blocks. From the video, we could observe that after putting a yellow and green blocks to their destinations, the robot fetches a black bulb block and puts it into Trash Area without extra trouble. In this way, the robot fully utilizes its storage capacity and reduces the time spent to accomplish the whole mission.
After that, the robot carries the next batch of smart bulb blocks. It scans the three bulbs and knows where the black bulb block is located. Like the previous batch, it first puts one red and blue bulb blocks in Area 2 of Red Room and Blue Room. After that, it reaches the place of black bulb block, fetches it to Trash Area and puts the third smart bulb block to its destination. The mission is done!
Personally, I really like the route planning idea of this robot model. Through storing three bulb blocks and transferring them in two batches, this model saves precious time in the competition.
This model has two parallel clips as its front attachment. Please note that each clip could carry two blocks at the same time, so totally, it could carry maximum of 4 blocks simultaneously.
Two color sensors are mounted over the clips. When the vehicle approaches a block, it could identify its color first. If the color sensor identifies two green blocks and one yellow block in total, the program could know that a black bulb block is put in the Area 1 of Yellow Room. On the contrary, if the color sensor identifies two yellow blocks and one green block, the black bulb block is put in the Area 1 of Green Room. This judgement is critical because it will result into different route planning of the robot.
Overall, the route planning of this model is complicated. For example, as the video shows, when the robot carries two yellow and one green bulb blocks, it puts down the green block temporarily and loads the black block instead. Next, it unloads the black block temporarily and puts the two yellow blocks to the destination. After that, it reloads the previous black bulb block and moves it to Trash Area. It then re-carries the previous green block and moves it to Area 1 of Green Room. If it were not for its widely opened clips which could accommodate the positional error, the robot might not be able to reload those blocks successfully.
For this model, the whole process requires accurate calculation. If any of the position is not accurately planned, those repeated putting down and lifting up will result into the failure. Even with careful calculation, it is still error prone. In the video, you could see that the yellow and green blocks in Area 1 are placed very close to the edge of grey area.
That is all for the introduction of WRO 2017 Junior Group models. Which model’s mechanical structure and route planning is your favorite? Hope you learn how to think out of box through the analysis of competition models. Enjoy the design and have fun!
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