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Inspecting Large Irrigation Networks in the Indus Basin: Challenges and Prospects Abubakr Muhammad, PhD Assistant Professor of Electrical Engineering Director, Laboratory for Cyber Physical Networks and Systems LUMS School of Science &

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Inspecting Large Irrigation Networks in the Indus Basin: Challenges and Prospects

IEEE RAS Technical Committee on Agricultural Robotics. Webinar. Feb 27, 2015

Abubakr Muhammad, PhD

Assistant Professor of Electrical Engineering Director, Laboratory for Cyber Physical Networks and Systems LUMS School of Science & Engineering Lahore, Pakistan

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Acknowledgements

  • Joint work with

– PhS & MS Students: Syed M. Abbas, Hamza Anwar,Talha Manzoor, Mudassir Khan – Collaborators: Prof Karsten Berns, RRLab, Univ of Kaiserslautern, Germany

  • Funding

– LUMS Faculty Initiative Fund (2014) – DAAD Grant: Robotic Profiling of Waterways. RoPWat (2014-15)

  • General Support

– Punjab Irrigation Department (PID) – International Water Management Institute (IWMI)

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Outline

  • Motivation and context
  • Smart water grids philosophy
  • Siltation of canals and rivers
  • Traditional canal cleaning process ( یئافص لھب )
  • Proposed solution
  • Towards performance limits
  • Conclusions and outlook
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SLIDE 4

LUMS Overview

  • Pakistan’s 2nd Private

University

– Founded in 1985 – Non-profit organization – 100 acre campus

  • 2300 Student Body

– Approx. 1800 undergraduates & 500 graduate students – 35% women – 60% resident on-campus

  • 120 faculty members

– PhD’s from Stanford, MIT, Yale, Oxford, Cambridge, Imperial

2/27/2015

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SLIDE 5

Cyber Physical Networks & Systems (CYPHYNETS) Lab

  • Est. 2008
  • Director

PhD Georgia Tech, Postdocs (Penn, McGill)

  • 2 Jr. Faculty

PhD Warick (control), PhD Siegen (robotics)

  • 3 PhD students
  • 6 MS students
  • 4 Full-time Research Assistants
  • 1 Lab Engineer
  • 2 Lab Technicians

Areas

  • Water Networks & Hydro-informatics
  • Agricultural Automation & Robotics
  • Cyber-Physical Systems

http://cyphynets.lums.edu.pk

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SLIDE 6

Graduate Training / Outreach

New Graduate Courses at LUMS

  • EE-561, Digital Control Systems
  • EE-662, Parameter & State Estimation
  • EE-562, Robot Motion Planning
  • CMPE-633c, Geometric Mechanics & Control
  • CMPE-633b, Robot Dynamics & Control
  • EE-565, Mobile Robots

Conferences / Workshops Organized

  • International Workshops on Intelligent Water Grids (IWG), 2013

– Symposium on Pakistan’s Water Futures – Workshop on Sensing and and Control for Water Networks – Workshop on Hydro-informatics – Mini-course on System Identification of Irrigation Channels

  • Workshop Series on Field and Assistive Robotics (2011-2015)

– 1st, 3rd, 5th, 7th, 9th in Lahore, Pakistan – 2nd, 4th, 6th , 8th in Dagstuhl / Kaiserslautern, Germany

NMO Secretariat, IIASA-Pakistan collaboration (2012-) IEEE CSS Society Pakistan Chapter (2011-)

Field & Assistive Robotics

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SLIDE 7

Agricultural Productivity Loss

Main reason is water!

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Motivation / Concerns

8

Annual canal diversions and sea escapage Flow reduction due to climate change

Vulnerability sources

  • Source. UNEP South Asia report, 2008
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Managing the World’s Largest Irrigation Network

90,000 Km of watercourses 3 reservoirs, 23 barrages 45 canal commands 36 million acre irrigated area System Efficiency: extremely poor!

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A Networked Smart Water Grid

Embedded controller Gate control Flow Measurements Wireless connectivity

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A Networked Smart Water Grid

Cyber Physical Systems / Internet of Things perspectives

  • Physical elements: rivers, watercourses, barrages, weirs, gates, pumps
  • Cyber elements : sensors, controllers, comm., services
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Smart Water Metering: E. Sadqiya Hakra Br. Canal Command LUMS-IWMI-Punjab Irrigation Dept. Collaboration (2012-14)

Laboratory for Cyber Physical Networks & Systems

  • Dept. of Electrical Engineering, LUMS

Project Site: 17 Distributaries in Bahawalnagar. System Architecture (above). Field installations (below).

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Identification & Control of Irrigation Channels (2011-14)

Laboratory for Cyber Physical Networks & Systems

  • Dept. of Electrical Engineering, LUMS

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Abstraction

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Autonomous Land Vehicles for Demining & Agriculture ALVeDA & MDRD (2010-2013)

Robot Vision: Terrain Classification, RGB-D & Monocular SLAM, Visual Servoing, Soil Estimation in a Bucket Excavator. Field Experiments: Channel mapping in Lahore (left). Scanning a minefield in Beirut (right).

Collaboration: RRLab, TU Kaiserslautern Funding: DAAD, LUMS, National Instruments

Objective: Push performance limits with low- cost vision sensors and simple mechatronics.

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What is Silt?

  • Particles of earth,

slightly larger than clay and slightly smaller than sand.

  • It is composed of

quartz and feldspar.

  • Occurs as soil, as

suspended sediment in a surface water body,

  • r as soil deposited at

the bottom of a waterway.

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Silt in Waterways

  • Slow moving water

deposits silts on the canal bed.

  • Reduces channel carrying

capacity.

  • Outlets draw more water

than their allotted share due to raised water levels.

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Silt Removal in Punjab یئافص لھب

  • Punjab Irrigation Department first started large-scale de-silting of

canals during 1990s.

  • Since then, PIDA (Punjab Irrigation and Drainage Authority)

conducts this campaign annually to clean its canals of silt and other garbage.

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Inspection of Canal Water Beds

  • Bed levels observed every 1000 ft.
  • Silt depth of more than 6 inches is marked for

removal.

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Silt Quantity Estimation

I.S F.S B.S B.S

benchmark silted water bed change point silt depth 1000 ft

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Silt Quantity Estimation

silt depth bed width top width

  • X-section area of silt

– bed width + top width

2

× silt depth

  • top width = 𝑔(silt depth)

– depending on canal geometry

  • X-section area calculated after

every 1000 ft

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Silt Quantity Estimation

  • Water bed divided into

patches of 1000 ft length

– Area calculated at both ends

  • f a single patch (A1 and A2).
  • Silt volume (cft)

– A1+A2

2

× 1000

I.S F.S B.S B.S

benchmark silted water bed change point 1000 ft

Estimate of silt clearance

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Various Methods of Silt Removal

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How Can We Automate This Process?

  • Extent of the canal system (40,000km+) and the tight

time-lines (< 3 weeks)

  • Makes it feasible to consider an automated solution

as a scalable and economic alternative to manual

  • peration.
  • Cleaning automation is too ambitious.
  • Perhaps, we can start by profiling / inspection only?
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Challenges

  • While cleaning, the original shape of the cross-section

and bed-slope must be restored.

  • What is the “true profile”?
  • Selection of map granularity to measure the deviation

from the true profile.

  • A way to deploy and recover the profiling system.
  • Profiler must not obstruct the canal operation.
  • Profiler must have the capability to negotiate narrow

passages and soft muddy beds.

  • Solution should be fast and easily scalable
  • Minimal specialist training.
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Proposed solution

  • An autonomous aerial inspection system
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Robotic Profiling for Clearing Watercourses (RoPWat)

  • Development of semi-autonomous robotic system

for profiling watercourses

  • 3D perception system to be deployed on

commercial vehicles

  • UAV for monitoring state of the canal
  • Simulation of the cleaning process
  • Simulation of vehicle for cleaning
  • Sponsored by LUMS Faculty Initiative Fund (FIF)

and later by DAAD.

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Proposed Solution

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CMU Riverine Mapping Project

Riverine reconnaissance with a low-Flying intelligent UAS.

Scherer et al. River mapping from a flying robot: state estimation, river detection, and obstacle mapping, Autonomous Robots, Vol. 32, No. 5, May, 2012.

An online state estimation system A self supervised vision based river detector. A scrolling incremental distance transform algorithm. A novel scanning LADAR configuration & analysis of measurement data.

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What is Achievable?

  • Analysis on 1D version of the aerial inspection problem
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Guassian Process (GP) Regression

Effect of variation in sampling interval

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Guassian Process (GP) Regression

Effect of sensor noise variation

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Incorporating Localization error

  • GP regression with noisy remains exactly the same
  • except that the covariance function is

instead of where

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Incorporating Localization error

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Verifying Results on a Test Rig

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What about Estimated Silt Volume?

  • Area under the (noisy) curve,

with mean and variance Error bounds:

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Analysis

  • Localization error matters “more” than sensor precision
  • Translates to strict requirements on positioning /

elevation sensors and good algorithms

  • The point of the analysis is not to construct new

algorithms but to find what is achievable.

  • This really challenges the state of the art in

– analytical methods – systems engineering

  • Framework is generic: analysis carries over to 3D case.
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Progress

2014: Team building, analysis, framework 2015: System building, testing of algorithms 2016: Field Trials

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Some Basic Questions …

  • Why Automation in developing countries like Pakistan?

– Devolution of governance – Ensuring rights – Conflict resolution

  • Major challenges

– Natural resources – Food and Agriculture – Critical infra-structures – Security – Healthcare

Participation Accountability Entitlements

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Scaling Problems in Development

Technical and institutional challenges in developing countries are really problems of scales:

  • Spatial scales: The inability to monitor and maintain

geographically extent infrastructures (e.g. the world’s largest contiguous irrigation network running over tens of thousands of km of open channels)

  • Time scales: The inability to collect information, reconfigure,

and react within short time spans (e.g. irrigation warabandi rosters, issued once in a cropping season despite the fact that water demand and supply varies over much shorter time spans)

  • Human scales: The inability to scale human expertise across

institutions (e.g. farmer organization roles in relationship to irrigation officials for maintaining channels, ensuring equity, collecting abiana [water fees], etc.) Robotics, AI, control and automation may be the answer to some

  • f these!
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Final Observation on Scales (Ag Robotics)

  • Booming population, urbanization.
  • Average farm size in Pakistan going down.
  • Can we empower the poor?

1960 2000

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Conclusions and Outlook

  • Water problems can inspire a range of ICT inspired

systems engineering, informatics and systems analysis solutions.

  • Canal inspection is an interesting structural

inspection problem with an important and unique socio-economic context for Pakistan.

  • The solution would require substantial improvements

to the state of the art, both in theory and practice.

  • Aerial robotics is an emerging area due to recent

availability of development platforms.

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References

– Gul-I-Ryna, Abubakr Muhammad, ”Silt Removal from Irrigation Canals in Punjab”, Technical Report, LUMS, 2013. – Talha Manzoor, “Sedimentation and Silt Cleaning in Waterways .“ Presentation at WFAR6, 2014. – Hamza Anwar, Abubakr Muhammad, Karsten Berns, “Towards Performance Limits of Aerial Canal Inspection” (under review) – Hamza Anwar, Syed Muhammad Abbas, Abubakr Muhammad, Karsten Berns, "Volumetric Estimation of Contained Soil using 3D Sensors", 3rd International Commercial Vehicle Technology Symposium (CVT), Kaiserslautern, Germany, 2014. – Lab website cyphynets.lums.edu.pk