Overview
A complete simulation pipeline for autonomous fruit harvesting using a UR10 manipulator in Gazebo.
The system integrates perception, motion planning, and task orchestration into a deterministic workflow.
System Architecture
- UR10 robot model with custom orchard environment
- YOLO-based apple detection
- 3D pose estimation from RGB camera stream
- MoveIt 2 motion planning with collision avoidance
- State machine driven task planning
- Pick → place execution pipeline
Engineering Focus
- Clean separation between perception and control layers
- Robust collision checking within cluttered tree environment
- Deterministic task sequencing
- Simulation-first validation for deployment readiness
Outcome
A full-stack manipulation system demonstrating perception-to-action autonomy in a constrained agricultural scenario.
Building a perception-driven manipulation pipeline that needs to work without manual tuning per object?
Getting detection, pose estimation, motion planning, and task sequencing to work together reliably in a cluttered environment is harder than any single piece in isolation. This project was about making that pipeline deterministic — from camera feed to confident pick execution — without hand-tuning for each new scenario.
If you are building a manipulation system that needs to go from perception to reliable action in an unstructured environment, we can help.
Tell us about your projectOr email kodorobotics@gmail.com