Outdoor usecase

Hi,

I am currently working on self driving RC cars as part of my graduation project each RC car is of scale 1/10 and it uses Jetson TX2 on-board computer, and we have 5x5 meter track in outdoor in open space region for testing, we are planning to use estimote for localization and path planning in our project.
And i am trying to figure out which is the package that best fits my project based on:
1-I’ve seen replies confirming that it can be used for outdoor i need a confirmation for that.
2-As i mentioned we are using Jetson TX2 on board computer i noticed that Robotics SDK support raspberry pi so i guess that also applies to jetson since it has GPIO i need confirmation for this point.
3-The cars are RC of small scale 1/10 so i need to know what margin of error in terms of accuracy should we expect?
4-We are using python as the main language for the project does it support python or we need another language?
5- What type of beacons and how many will be ideal for our track?
6- Is there a need for the RC cars to be connected to the internet to be able to utilize the beacons or the SDK? if yes is there any offline option?
6- Overall can we consider Robotics SDK ideal for localization and path planning for our project upon those variables?

Hopefully i get answers as soon as possible to get started,
Thanks,

I hope that i can get answers as soon as possible to get started or to know if i should consider Estimote as a solution that will best fit our needs.

Looking forward to hear from the staff.
Thanks in advance.

Hi,

  1. Both Estimote Beacons and Stickers are ready to work outdoors. However, keep in mind that extreme weather conditions may affect battery performance. You can read about it in more detail on our Community Portal.
    Additionally, please note that while our beacons are splashproof with their silicone enclosures, they’re not 100% waterproof. If water gets under the adhesive backing, it can kill the PCB.
  2. Yes, RoboticsSDK should work on Jetson as well. The only thing is that you have to enable GPIO UART which can be a bit tricky on Jetson TX2.
  3. Currently the expected error is around 0.3m
  4. Python is the best way to go. (see the listener.py example or check out visualization_demo.py on the examples branch)
  5. You will need at least 5 beacons - 4 placed in the corners of the track, the fifth one gets connected to your car.
  6. I think that Estimote Robotics SDK can be a perfect choice for you. There is only one more thing you should consider and that’s the position update rate which currently is limited to 2Hz.

I hope those answers will help.

Best regards,
Greg

Thank you so much for your clarification.
Also what about the SDK does it require internet connection or it doesnt?
And position update rate limitation what is 2Hz effect to the update rate like how many seconds it might delay and putting more Beacons would decrease this delay somehow?
And what kind of beacons do you recommend UWB or Location?

I’m sorry, I should have added that all 5 have to be UWB beacons.
Yes, in the current version internet connection is necessary.
Putting more beacons improves precision in larger spaces but I wouldn’t recommend that in your case (a 5x5m area). Currently there is no way to improve that parameter. When it comes to the delay it depends on how fast your car will be moving.

I see, The internet connection part is very bad limitation since the region where the track will be setup has bad network coverage… Why internet connection is required? and how the beacons consumes it does it do high traffic exchange ? if its only required for securing and hashing UUID it might be disabled ? since security is not a concern in my project right now.

First of all, you will need internet connection on your iPhone to enable Robotics mode on the beacons that create the location. Even a slow connection will do for that part as there is not that much data transfer going on. Than the device (Jetson in your case) on your robot has to connect to our Cloud in order to authorize and fetch the location from the cloud.

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