Tree-Perching-EpiSci

Kenneth Hoffmann, Julia Di

Project code repository for affiliateship between Stanford's Center for Design Research and EpiSci Systems Inc.

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Overview

The perching system flight experiments involve:

1. Perception
2. Planner
3. Perching Sequence

Perception

• The drone is in a hover state first and live camera images are collected
• The user may click on the tree
• The perception system segments the tree and detects the centroid
• Tree pose is continuously tracked

Planner

• Outputs trajectory and setpoints
• Takes in tree pose, tree velocity, drone pose, drone velocity

Perching Sequence

When ready to initiate the perching sequence:

• Time-of-Flight sensor on the gripper to check distance between gripper and tree
• Trigger "elbow" hinge to release drone vertical
• Throttle down

User input is required to (1) click on a perching location, (2) start perching sequence, (3) verify perch state.

In addition, the system can detect perch failures from IMU data.

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System Prerequisites

• This work uses ROS 1.
• The drone used for experiments is a Uvify IFO-S modified with microspine gripper hardware.
• In order to use the deep learning tree detectors (e.g. Segment Anything), the processing must be done on a CUDA-enabled device.
• Drone pose is tracked with an Optitrack motion capture system

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Usage

The autonomy state machine is detailed in ``percher/src/state_machine.cpp"

To run this:

1. Set up the ROS_MASTER_URI to the XML-RPC URI of the master node.
2. Run the vision and trajectory files with roslaunch object_tracking target_vision_tree.launch and roslaunch mav_trajectory_generation_example example.launch
3. Run the state machine with rosrun percher percher_node and rosrun mav_trajectory_generation_example dummy_topic_code.py

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License

This work was conducted by Kenneth Hoffmann and Julia Di as Stanford graduate students.