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The '''Mobile Robotics Laboratory''' ('''MRL''') is an extension of the '''Guidance, Control and Decision Systems Laboratory''' ('''GCDSL''') in the Department of Aerospace Engineering, [[Indian Institute of Science]], [[Bangalore]], [[India]]. It is headed by Dr. [[Debasish Ghose]], Professor.
The '''Mobile Robotics Laboratory''' ('''MRL''') is an extension of the '''Guidance, Control and Decision Systems Laboratory''' ('''GCDSL''') in the Department of Aerospace Engineering, [[Indian Institute of Science]], [[Bangalore]], [[India]]. It is headed by Dr. [[Debasish Ghose]], Professor.<ref name=":0">{{Cite web|url=http://www.aero.iisc.ernet.in/people/debasish-ghose/|title=Aerospace Engineering, Indian Institute of Science, Bangalore|language=en|access-date=2019-01-25}}</ref>


MRL was established in 2002, and via GCDSL is considered as one of the leading robotic research centers in India.
MRL was established in 2002, and via GCDSL is considered as one of the leading robotic research centers in India.
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Over the years research has extended in the fields of Simultaneous Localization and Mapping ([[Simultaneous localization and mapping|SLAM]]), Aerial Robotics and [[machine vision]]. Recently there's been an emphasis on [[computer vision]] and [[Machine learning]] for improving versatility and cognitive abilities of robotic platforms.
Over the years research has extended in the fields of Simultaneous Localization and Mapping ([[Simultaneous localization and mapping|SLAM]]), Aerial Robotics and [[machine vision]]. Recently there's been an emphasis on [[computer vision]] and [[Machine learning]] for improving versatility and cognitive abilities of robotic platforms.


== Projects undertaken ==
== Current Projects ==

=== Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020)<ref>{{Cite web|url=http://www.rbccps.org/research/mbzirc/|title=Mohamed Bin Zayed International Robotics Challenge (MBZIRC)|date=2018-08-08|website=Robert Bosch Centre for Cyber-Physical Systems|language=en|access-date=2019-01-25}}</ref> ===
Goal: MBZIRC 2020 (Link: <nowiki>http://www.mbzirc.com/challenge/2020</nowiki> ) will be based on autonomous aerial and ground robots, carrying out navigation and manipulation tasks, in unstructured, outdoor and indoor environments. All the sub-challenges involve cooperation between multiple UAVs and swarm-abilities. This mission is at the frontier of Intelligent Aerial Robotics technology.

=== UAVs for Flood emergency response, aid planning and management (EPSRC), 2020<ref name=":1">{{Cite web|url=https://www.researchgate.net/profile/Debasish_Ghose|title=Debasish Ghose {{!}} Indian Institute of Science, Bengaluru {{!}} IISC {{!}} Department of Aerospace Engineering|website=ResearchGate|language=en|access-date=2019-01-25}}</ref> ===
The project focuses on using UAVs to gather information about an unfolding flooding disaster, allowing emergency response units to prioritise resources and deploy them effectively. It will also address the challenges associated with flying UAVs in difficult situations, as well as how the data can be combined with accelerated flood inundation models to generate detailed evacuation plans, build community flood resilience, save lives and reduce economic damage.

=== Intelligent Swarm and Cooperative Robots<ref name=":1" /> ===
There are tasks that cannot be done by a single robot alone. A group of robots collaborating on a task has the potential of being highly efficient, flexible and robust. If one robot fails, another robot could take its position. We use vision to achieve robot localization and navigation without using external infrastructure. We aim to develop a platform-independent approach that utilizes deep neural networks (DNNs) to enhance classical controllers to achieve high-level task.

=== Learning Systems (Machine Perception)<ref name=":0" /> ===
Learning can be used to improve the performance of a robotic system in a complex environment. However, providing safety guarantees during the learning process is one of the key challenges that prevents these algorithms from being applied to real world applications. This project explores advanced control and planning algorithms, and their applicability to robotics problems. To achieve reliable robot operations that satisfy given performance specifications, we apply nonlinear, robust, predictive and hybrid controls approaches and adaptive motion planning.

=== Interceptor Aerial Systems (Agile Pursuit of Target)<ref name=":1" /> ===

== Archived Projects ==


=== Glowworm swarm optimization (GSO) ===
=== Glowworm swarm optimization (GSO) ===
Line 31: Line 47:
Histogramic intensity switching (HIS) is a vision-based obstacle avoidance algorithm developed in the lab. It makes use of histograms of images captured by a camera in real-time and does not make use of any distance measurements to achieve obstacle avoidance. An improved algorithm called the HIS-Dynamic mask allocation (HISDMA) has also been designed. The algorithms were tested on an in-house custom built robot called the VITAR.
Histogramic intensity switching (HIS) is a vision-based obstacle avoidance algorithm developed in the lab. It makes use of histograms of images captured by a camera in real-time and does not make use of any distance measurements to achieve obstacle avoidance. An improved algorithm called the HIS-Dynamic mask allocation (HISDMA) has also been designed. The algorithms were tested on an in-house custom built robot called the VITAR.


=== Multi-robot simultaneous localization and mapping (SLAM) ===
=== Multi-Robot simultaneous localization and mapping (SLAM) ===
Implementation of occupancy grid mapping using a miniature mobile robot equipped with a set of five infrared based ranging sensors is explored in this research. Bayesian methods are used to update the map. Another variant of this technique will utilize a single IR-range sensor to obtain range to different distinctive features in the surrounding environment and utilize the readings obtained to make the SLAM converge. These techniques will be extended to a swarm of robots. These robots would communicate using the ZigBee protocol among themselves and with a global coordinator (PC) which would be responsible for map merging. Simulation experiments are being carried out using the Player/Stage software. The robotic platform is built using a custom designed set of swarm robots called Glowworms.
Implementation of occupancy grid mapping using a miniature mobile robot equipped with a set of five infrared based ranging sensors is explored in this research. Bayesian methods are used to update the map. Another variant of this technique will utilize a single IR-range sensor to obtain range to different distinctive features in the surrounding environment and utilize the readings obtained to make the SLAM converge. These techniques will be extended to a swarm of robots. These robots would communicate using the ZigBee protocol among themselves and with a global coordinator (PC) which would be responsible for map merging. Simulation experiments are being carried out using the Player/Stage software. The robotic platform is built using a custom designed set of swarm robots called Glowworms.


=== Quad-rotor UAVs ===
=== Quad-rotor and Aerial Manipulator Test-bed ===
A [[quadcopter|quadrotor]] micro-air-vehicle (MAV) is a rotor-based craft with four rotors, usually placed at the corners of a square frame. The four motor speeds (and hence thrusts) are the control inputs which result in motion of the quadrotor. The dynamics of this vehicle are fast and highly coupled, and hence presents a challenging control problem.<br />A quadrotor and control test-bed has been fabricated in-house at the Mobile Robotics Lab. Experiments on control are being conducted on the quadrotor, beginning with yaw, pitch and roll stabilization.
A [[quadcopter|quadrotor]] micro-air-vehicle (MAV) is a rotor-based craft with four rotors, usually placed at the corners of a square frame. The four motor speeds (and hence thrusts) are the control inputs which result in motion of the quadrotor. The dynamics of this vehicle are fast and highly coupled, and hence presents a challenging control problem.<br />A quadrotor and control test-bed has been fabricated in-house at the Mobile Robotics Lab. Experiments on control are being conducted on the quadrotor, beginning with yaw, pitch and roll stabilization.


Line 48: Line 64:
VITAR (Vision based Tracked Autonomous Robot) consists of a tracked mobile robot equipped with a pan-tilt mounted vision sensor, an onboard PC, driver electronics, and a wireless link to a remote PC. It has been utilized to test vision based algorithms such as the HIS and the HIS-DMA.
VITAR (Vision based Tracked Autonomous Robot) consists of a tracked mobile robot equipped with a pan-tilt mounted vision sensor, an onboard PC, driver electronics, and a wireless link to a remote PC. It has been utilized to test vision based algorithms such as the HIS and the HIS-DMA.


== Members ==
== Members<ref name=":0" />==


=== Current ===
=== Current ===
* [[Debasish Ghose]]
* [[Debasish Ghose|Dr. Debasish Ghose]], Faculty Lead
*Vidya Sumathy, PhD Candidate, Aerial Manipulation
*Lima Agnel Tony, PhD Candidate, Obstacle Avoidance.
*Shriram Swaminathan, PhD (In Progress), Spacecraft Trajectory Optimization. Presently Scientist, ISRO, Trivandrum] (Co-Supervisor: U.P. Rajeev).
*Chandrakanth Annam, PhD (In Progress), Real-Time Endo-Atmospheric Trajectory Optimization. Presently Scientist, DRDL, Hyderabad (Co-Supervisors: Ashwini Ratnoo, J. Umakant, and A.K. Sarkar).
*Narayani Vedam, PhD Candidate, Network-based Multi-Agent Strategies.
*Meenakshi Sarkar, PhD Candidate, Obstacle Avoidance Systems using Learning Techniques.
*Rudrashis Majumder, PhD Student, UAV Team Operations for Flood Monitoring and Evacuation.
*Aditya Hegde, PhD Student, Deep Learning for UAV Swarm Applications.
*Avnish Kumar, PhD (In Progress), Deep Learning for UAV Swarm Applications. Presently Scientist, DRDL, Hyderabad.
*Ankit Gupta, PhD (In Progress), Deep Learning for UAV Swarm Applications. Presently Scientist, Intel Research Center, Bangalore. Legends: PhD Candidate=Senior Research Fellow, PhD Student=Junior Research Fellow, PhD (In Progress)=Industry Sponsored program<ref>{{Cite web|url=https://www.isro.gov.in/research-and-academia-interface/research-fellowships|title=Research Fellowships - ISRO|website=www.isro.gov.in|access-date=2019-01-25}}</ref>
*
*


=== Former ===
=== Former ===
[http://www.aero.iisc.ernet.in/people/debasish-ghose/#students Full List of members]

* [http://guidance.aero.iisc.ernet.in/krishna/ Dr. K.N. Krishnanand]
* [http://guidance.aero.iisc.ernet.in/krishna/ Dr. K.N. Krishnanand]
* [http://guidance.aero.iisc.ernet.in/joseph/joseph%20thomas%20home.htm Joseph Thomas]
* [http://guidance.aero.iisc.ernet.in/joseph/joseph%20thomas%20home.htm Joseph Thomas]

Revision as of 03:44, 25 January 2019

Mobile Robotics Laboratory
TypePublic
Established2002
Location
Bangalore, India
CampusIndian Institute of Science
Websiteguidance.aero.iisc.ernet.in/robotics/index.html

The Mobile Robotics Laboratory (MRL) is an extension of the Guidance, Control and Decision Systems Laboratory (GCDSL) in the Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India. It is headed by Dr. Debasish Ghose, Professor.[1]

MRL was established in 2002, and via GCDSL is considered as one of the leading robotic research centers in India.

Research overview

MRL was started with the primary aim of performing research in the fields of Swarm robotics, Multi-Robot Systems and Cooperative Robotics with applications to tasks such as cooperative transportation, robotic formations, cooperative search/rescue, and odor source localization. Several robotic platforms have been built in-house and used for real-world-experiments in order to validate algorithms related to some of the above research problems.

The group is dedicated towards creating intelligent systems that are able to autonomously operate in complex and diverse scenarios. They are interested in the mechatronic design and control of vehicles that efficiently adapt to different situations and perform in dynamic environments. This includes development of novel methods and tools for perception, mapping and path planning.

Over the years research has extended in the fields of Simultaneous Localization and Mapping (SLAM), Aerial Robotics and machine vision. Recently there's been an emphasis on computer vision and Machine learning for improving versatility and cognitive abilities of robotic platforms.

Current Projects

Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020)[2]

Goal: MBZIRC 2020 (Link: http://www.mbzirc.com/challenge/2020 ) will be based on autonomous aerial and ground robots, carrying out navigation and manipulation tasks, in unstructured, outdoor and indoor environments. All the sub-challenges involve cooperation between multiple UAVs and swarm-abilities. This mission is at the frontier of Intelligent Aerial Robotics technology.

UAVs for Flood emergency response, aid planning and management (EPSRC), 2020[3]

The project focuses on using UAVs to gather information about an unfolding flooding disaster, allowing emergency response units to prioritise resources and deploy them effectively. It will also address the challenges associated with flying UAVs in difficult situations, as well as how the data can be combined with accelerated flood inundation models to generate detailed evacuation plans, build community flood resilience, save lives and reduce economic damage.

Intelligent Swarm and Cooperative Robots[3]

There are tasks that cannot be done by a single robot alone. A group of robots collaborating on a task has the potential of being highly efficient, flexible and robust. If one robot fails, another robot could take its position. We use vision to achieve robot localization and navigation without using external infrastructure. We aim to develop a platform-independent approach that utilizes deep neural networks (DNNs) to enhance classical controllers to achieve high-level task.

Learning Systems (Machine Perception)[1]

Learning can be used to improve the performance of a robotic system in a complex environment. However, providing safety guarantees during the learning process is one of the key challenges that prevents these algorithms from being applied to real world applications. This project explores advanced control and planning algorithms, and their applicability to robotics problems. To achieve reliable robot operations that satisfy given performance specifications, we apply nonlinear, robust, predictive and hybrid controls approaches and adaptive motion planning.

Interceptor Aerial Systems (Agile Pursuit of Target)[3]

Archived Projects

Glowworm swarm optimization (GSO)

The glowworm swarm optimization (GSO) algorithm is an optimization technique developed for simultaneous capture of multiple optimums of multi-modal functions.[4] The algorithm utilizes agents called glowworms which use a luminescent quantity called Luciferin to (indirectly) communicate the function-profile information at their current location to their neighbors. The glowworm depends on a variable local-decision domain, which is bounded above by a circular sensor range, to identify its neighbors and compute its movements. Each glowworm selects a neighbor that has a Luciferin value more than its own, using a probabilistic mechanism, and moves towards it. These movements that are based only on local information enable the swarm of glowworms to split into disjoint subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple optimums (not necessarily equal) of a given multi-modal function. The algorithm was tested on a custom designed system of robots called Kinbots.

Histogramic intensity switching

Histogramic intensity switching (HIS) is a vision-based obstacle avoidance algorithm developed in the lab. It makes use of histograms of images captured by a camera in real-time and does not make use of any distance measurements to achieve obstacle avoidance. An improved algorithm called the HIS-Dynamic mask allocation (HISDMA) has also been designed. The algorithms were tested on an in-house custom built robot called the VITAR.

Multi-Robot simultaneous localization and mapping (SLAM)

Implementation of occupancy grid mapping using a miniature mobile robot equipped with a set of five infrared based ranging sensors is explored in this research. Bayesian methods are used to update the map. Another variant of this technique will utilize a single IR-range sensor to obtain range to different distinctive features in the surrounding environment and utilize the readings obtained to make the SLAM converge. These techniques will be extended to a swarm of robots. These robots would communicate using the ZigBee protocol among themselves and with a global coordinator (PC) which would be responsible for map merging. Simulation experiments are being carried out using the Player/Stage software. The robotic platform is built using a custom designed set of swarm robots called Glowworms.

Quad-rotor and Aerial Manipulator Test-bed

A quadrotor micro-air-vehicle (MAV) is a rotor-based craft with four rotors, usually placed at the corners of a square frame. The four motor speeds (and hence thrusts) are the control inputs which result in motion of the quadrotor. The dynamics of this vehicle are fast and highly coupled, and hence presents a challenging control problem.
A quadrotor and control test-bed has been fabricated in-house at the Mobile Robotics Lab. Experiments on control are being conducted on the quadrotor, beginning with yaw, pitch and roll stabilization.

Robots developed inhouse

Kinbots

A robotic platform consisting of four wheeled-mobile robots have been developed in the lab for multi-robot testing. They are similar in principle to Braitenberg Vehicles and use simple perception/interaction/actuation techniques to achieve individual vehicle complexity and produce effective group behavior through cooperation. These robots have been used to test out the GSO algorithm

Glowworms

These miniature robots are developed based on Kinbots.

VITAR

VITAR (Vision based Tracked Autonomous Robot) consists of a tracked mobile robot equipped with a pan-tilt mounted vision sensor, an onboard PC, driver electronics, and a wireless link to a remote PC. It has been utilized to test vision based algorithms such as the HIS and the HIS-DMA.

Members[1]

Current

  • Dr. Debasish Ghose, Faculty Lead
  • Vidya Sumathy, PhD Candidate, Aerial Manipulation
  • Lima Agnel Tony, PhD Candidate, Obstacle Avoidance.
  • Shriram Swaminathan, PhD (In Progress), Spacecraft Trajectory Optimization. Presently Scientist, ISRO, Trivandrum] (Co-Supervisor: U.P. Rajeev).
  • Chandrakanth Annam, PhD (In Progress), Real-Time Endo-Atmospheric Trajectory Optimization. Presently Scientist, DRDL, Hyderabad (Co-Supervisors: Ashwini Ratnoo, J. Umakant, and A.K. Sarkar).
  • Narayani Vedam, PhD Candidate, Network-based Multi-Agent Strategies.
  • Meenakshi Sarkar, PhD Candidate, Obstacle Avoidance Systems using Learning Techniques.
  • Rudrashis Majumder, PhD Student, UAV Team Operations for Flood Monitoring and Evacuation.
  • Aditya Hegde, PhD Student, Deep Learning for UAV Swarm Applications.
  • Avnish Kumar, PhD (In Progress), Deep Learning for UAV Swarm Applications. Presently Scientist, DRDL, Hyderabad.
  • Ankit Gupta, PhD (In Progress), Deep Learning for UAV Swarm Applications. Presently Scientist, Intel Research Center, Bangalore. Legends: PhD Candidate=Senior Research Fellow, PhD Student=Junior Research Fellow, PhD (In Progress)=Industry Sponsored program[5]

Former

Full List of members

References

  1. ^ a b c "Aerospace Engineering, Indian Institute of Science, Bangalore". Retrieved 25 January 2019.
  2. ^ "Mohamed Bin Zayed International Robotics Challenge (MBZIRC)". Robert Bosch Centre for Cyber-Physical Systems. 8 August 2018. Retrieved 25 January 2019.
  3. ^ a b c "Debasish Ghose | Indian Institute of Science, Bengaluru | IISC | Department of Aerospace Engineering". ResearchGate. Retrieved 25 January 2019.
  4. ^ K.N. Krishnanand and D. Ghose, "Glowworm swarm based optimization algorithm for multi-modal functions with collective robotics applications," Multi-agent and Grid Systems, Issue 3, Volume 2, 2006, pp. 209 - 222.
  5. ^ "Research Fellowships - ISRO". www.isro.gov.in. Retrieved 25 January 2019.

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