Autonomous Infrastructure Inspection and Maintenance

Autonomous Infrastructure Inspection and Maintenance

Kostas Alexis Autonomous Systems Lab

ETH Zurich

Concept Overview

• Autonomous Path-Planning for Optimal Remote Structural Inspection • 3D reconstruction of the infrastructure facilities • Damage detection • Non-destructive Testing - Contact-based Inspection • Infrastructure Maintenance via Aerial Manipulation

Ultimate goal: Complete Autonomous Infrastructure Inspection and Maintenance Solution

Illustration of Infrastructure Inspection Scenario

Concept Overview

A challenging mission! • Self-localization within complex environments • Robust and safe flight control • Efficient path-planning for remote inspection • Visual/Inertial-based environmental reconstruction • Obstacle Avoidance • Physical Interaction Control for contact-based inspection • Aerial robotic manipulation

A great scientific/technological challenge with significant financial impact!

Illustration of Infrastructure Inspection Scenario

Presentation Outline

Autonomous Path-Planning for Remote Infrastructure Inspection

3D reconstruction of the environment

State estimation and mapping

Stereo-IMU Dense Reconstruction

Physical Interaction Control for Contact-based Inspection

Aerial robotic manipulation towards Infrastructure Maintenance

Path-planning for Infrastructure Inspection

Develop algorithms that can: • Compute the path that an aerial vehicle has

to follow to fully inspect a 3D structure assuming a specific visibility sensor model.

• Compute such a path with minimum distance and account for vehicle limitations and constraints.

• Compute inspection path in reasonable time despite its strong ND-hardness

• Account for local changes of the structure and have robustness against uncertainty


Create a meta-tree structure to enable an incremental sampling-based approach to inspection path-planning

Problem: given a representation of the structure, compute the optimal inspection path.

Challenge: ensure full coverage and cost optimality while handling constraints and keeping the computational times low.

Challenge: Have a good solution at “anytime”.

Challenge: handle uncertainty.

Goal: an efficient “anytime” inspection planning algorithm with performance guarantees.


Evaluation in a 2D scenario for Holonomic and Nonholonomic vehicles. Convergence plots are very promising!


Experimentally Evaluated considering both holonomic and nonholonomic vehicle trajectories.

First paths are computed in a few seconds.


State Estimation & Dense Reconstruction

Fundamental requirement: Deployment of robust field robots capable of self-localization and reconstructing the model of the environment.

Photo: Mine Inspection scenario

M. Achtelik, M. Burri, M. Chli, P. Furgale, S. Leutenegger, S. Lynen, J. Nikolic, S. Omari, J. Rehder


Visual-Inertial SLAM Sensor


Tightly-coupled keyframe-based visual-inertial SLAM [1]

Tight integration of IMU allows highly dynamic motions and efficient outlier

rejection.

• Realtime dense stereo-based 3D reconstruction

– Poses: ASLam [1]

– Stereo depthmap: ELAS [2]

– Mapping: Octomap [3]

• Efficient outlier rejection based on photoconsistency

[1] S. Leutenegger et.al., “Keyframe-Based Visual-Inertial SLAM Using Nonlinear Optimization”, RSS 2013

[2] A.Geiger et.al., “Efficient Large Scale Stereo Matching”, ACCV 2010

[3[ A. Hornung et.al, “Octomap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees” ,Aut. Rob. 2013


Aerial Contact-based Inspection

Fully automate the process of contact-based inspection at all technologies involved: • Control • Perception • Planning

This task is combined with the task of remote inspection as the selection of the “inspection

through contact points” are based on the previously derived model of the environment.


G. Darivianakis, K. Alexis, M. Burri, R. Siegwart, "Hybrid Predictive Control for Aerial Robotic Physical Interaction towards Inspection Operations", IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China (accepted)


Subject to: • Hybrid vehicle dynamics capturing all operation modes • Encoded Input/State constraints • Obstacle polyhedric constraints

Hybrid Model Predictive Control problem over a prediction horizon N



Problem: Compute the optimal route that inspects (in the sense of visiting) all the points in the structure. Challenge: close to real-time solvers. Challenge: handle non-smooth surfaces Challenge: allow for “on-the-fly” variation of the contact points


Heavy-Aerial Robotic Work-Task Execution

Employ a tilt-rotor configuration to enable the execution of work-tasks that require forces exceeding the capabilities of typical MAVs.

C. Papachristos et. al.

Heavy-Aerial Robotic Work-Task Execution

Summary

Aerial Robots can greatly benefit Infrastructure Inspection and Maintenance

It is a task that poses Great Scientific Challenges

Main Areas of Scientific Research

It corresponds to a market with great potential

Single and Multi-robot Path-Planning for Inspection

Single and Multi-robot Perception, Localization and Mapping

Flight Control for Physical Interaction and Manipulation

Aerial Manipulation

Good time to make these systems autonomous and reliable – good time for business

Aerial robot navigation and obstacle avoidance

Short list of References

[1] G. Darivianakis, K. Alexis, M. Burri, R. Siegwart, "Hybrid Predictive Control for Aerial Robotic Physical Interaction towards Inspection Operations“, IEEE International Conference on Robotics and Automation (ICRA), 2014, Hong Kong, China [2] C. Papachristos, K. Alexis, A. Tzes, " Efficient Force Exertion for Aerial Robotic Manipulation: Exploiting the Thrust-Vectoring Authority of a Tri-TiltRotor UAV "IEEE International Conference on Robotics & Automation (ICRA), 2014, Hong Kong, China [3] K. Alexis, C. Huerzeler, R. Siegwart, “Hybrid Modeling and Control of a Coaxial Unmanned Rotorcraft Interacting with its Environment through Contact”, IEEE International Conference on Robotics and Automation, ICRA 2013, Karlsruhe, Germany [4] S. Leutenegger et.al., “Keyframe-Based Visual-Inertial SLAM Using Nonlinear Optimization”, RSS 2013 [5] Markus Achtelik, Sammy Omari, Roland Siegwart, Closing the MAV navigation loop: from control to localization and path-planning, IROS 2013 …and on going work…

Thank you! Questions?

Kostas Alexis Autonomous Systems Lab ETH Zurich [email protected]

Technology

Autonomous Infrastructure Inspection and Maintenance