Inspection Control: Perception-Driven Admittance-Based Orientation Control

Overview

This repository presents a perception-driven orientation control framework for robotic inspection tasks. The system integrates real-time point cloud perception, orientation error estimation, and an admittance-based control strategy to enable compliant and stable end-effector alignment with target surfaces.

The framework is designed for human-in-the-loop robotic inspection, where the controller assists operators by autonomously regulating orientation while respecting physical constraints such as torque and velocity limits.

Key Features

• Perception-driven control using surface normals from point clouds
• Admittance-based outer-loop controller for compliant behavior
• PD-based orientation regulation with physically interpretable dynamics
• Compatible with ROS 2 and MoveIt Servo pipelines
• Real-time integration of perception, control, and execution
• Validated on UR5e manipulator hardware

System Architecture

The control pipeline consists of the following stages:

Depth Image -> Point Cloud -> Surface Normal Estimation
            -> Orientation Error Computation
            -> PD Controller (Torque Output)
            -> Admittance Dynamics (Virtual Sphere Model)
            -> Velocity Commands -> Robot Execution