ArduPilot

Hi everyone, I am an Australian Mechatronics Engineering Student and kicking off the project for my undergraduate thesis. I’ll be incorporating Autopilot rover to control an eight wheeled rough terrain robot and develop a Deep ... [More] Reinforcement Learning algorithm to train the robot to choose the optimal path when navigating in challenging environments like rubble. The plan is to use a standard GPS module to control the robot during normal operation and a RealSense depth + tracking camera combo to detect obstacles and switch to the machine learned close-in navigation policy. In this mode the depth camera can generate a terrain level map and roughness estimation factor which can be fed into the DRL policy along with the robots current and goal position to determine intermediate positions to reach along the path. These positions can then be sent to the mission planner as new waypoints for the flight controller to aim for (using the tracking camera). I am still fairly new to ArduPilot, so any comments or tips on my setup are extremely welcome. The main equipment being used in the project is: 8-wheel robot with motor driver board compatible with rover Autopilot signals Pixhawk flight controller GPS module Intel RealSense D435i depth camera Intel RealSense T265 tracking camera NUC companion computer Telemetry radio PC ground controller with MAVLink protocol This is a general software overview to show how the DRL path planner will fit in with the robot’s operation: Below is a full wiring diagram of the motor controller board, computer vision system and the autopilot equipment. Hopefully everything will work as intended, I aim to have the system functioning properly in normal mode (just GPS and Pixhawk) before I start designing the DRL algorithm. I’m sure there will be lots of changes as I begin to piece everything together and test but I’m looking forward to seeing the performance of the final system. Up next I will be documenting the assembly process of the robot and the first round of operation tests. Once again any feedback or questions are welcome so let me know what you think. Tom. 10 posts - 5 participants Read full topic [Less]

Hi everyone, I am an Australian Mechatronics Engineering Student and kicking off the project for my undergraduate thesis. I’ll be incorporating Autopilot rover to control an eight wheeled rough terrain robot and develop a Deep ... [More] Reinforcement Learning algorithm to train the robot to choose the optimal path when navigating in challenging environments like rubble. The plan is to use a standard GPS module to control the robot during normal operation and a RealSense depth + tracking camera combo to detect obstacles and switch to the machine learned close-in navigation policy. In this mode the depth camera can generate a terrain level map and roughness estimation factor which can be fed into the DRL policy along with the robots current and goal position to determine intermediate positions to reach along the path. These positions can then be sent to the mission planner as new waypoints for the flight controller to aim for (using the tracking camera). I am still fairly new to ArduPilot, so any comments or tips on my setup are extremely welcome. The main equipment being used in the project is: 8-wheel robot with motor driver board compatible with rover Autopilot signals Pixhawk flight controller GPS module Intel RealSense D435i depth camera Intel RealSense T265 tracking camera NUC companion computer Telemetry radio PC ground controller with MAVLink protocol This is a general software overview to show how the DRL path planner will fit in with the robot’s operation: Below is a full wiring diagram of the motor controller board, computer vision system and the autopilot equipment. Hopefully everything will work as intended, I aim to have the system functioning properly in normal mode (just GPS and Pixhawk) before I start designing the DRL algorithm. I’m sure there will be lots of changes as I begin to piece everything together and test but I’m looking forward to seeing the performance of the final system. Up next I will be documenting the assembly process of the robot and the first round of operation tests. Once again any feedback or questions are welcome so let me know what you think. Tom. 7 posts - 5 participants Read full topic [Less]

https://www.youtube.com/embed/KopP3jFKCsU This video shows a call between @Mallikarjun_SE and myself where we add support for the STM32L431 microcontroller to ArduPilot. It is a long video, but may be interesting if someone else wants ... [More] to add a new MCU Note that adding the L431 was much harder than adding most MCUs as it wasn’t actually a supported ChibiOS MCU, so we needed to add support to ChibiOS too. The happy ending is the board did boot up and work as an AP_Periph CAN peripheral. 5 posts - 4 participants Read full topic [Less]

https://www.youtube.com/embed/KopP3jFKCsU This video shows a call between @Mallikarjun_SE and myself where we add support for the STM32L431 microcontroller to ArduPilot. It is a long video, but may be interesting if someone else wants ... [More] to add a new MCU Note that adding the L431 was much harder than adding most MCUs as it wasn’t actually a supported ChibiOS MCU, so we needed to add support to ChibiOS too. The happy ending is the board did boot up and work as an AP_Periph CAN peripheral. 8 posts - 4 participants Read full topic [Less]

https://www.youtube.com/embed/KopP3jFKCsU This video shows a call between @Mallikarjun_SE and myself where we add support for the STM32L431 microcontroller to ArduPilot. It is a long video, but may be interesting if someone else wants ... [More] to add a new MCU Note that adding the L431 was much harder than adding most MCUs as it wasn’t actually a supported ChibiOS MCU, so we needed to add support to ChibiOS too. The happy ending is the board did boot up and work as an AP_Periph CAN peripheral. 6 posts - 4 participants Read full topic [Less]

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https://www.youtube.com/embed/S-eND8-AZFI 6 posts - 4 participants Read full topic