Technology Innovation Hub

The Technology Innovation Hub on Autonomous Navigation and Data Acquisition Systems (TiHAN) described in this proposal is a multi-departmental initiative, including researchers from Dept. of Electrical Engineering, Dept. of Computer Science and Engineering, Dept. of Mechanical and Aerospace Engineering, Dept. of Civil Engineering, and Dept. of Design at IIT Hyderabad with collaboration and support from reputed institutions (including IIT Dharwad, IIIT Sri City, IIIT Dharwad, ICRISAT Hyderabad, CDAC Hyderabad, CDAC Trivandrum, and industry (Suzuki Motor Corporation, Terra Drone, Honeywell, Skoruz, etc.). With important focus on the research and development of interdisciplinary technologies in the specific domain area of “Autonomous Navigation and Data Acquisition systems,” this hub importantly focuses on addressing the challenges hindering the real-time adoption of unmanned autonomous vehicles for both civilian and military use cases.

Amidst the growing population and climate change, India is facing several challenges in the areas of agricultural yield, transportation management, security, and infrastructure maintenance, etc. Hence, there is an increasing and immediate need for improving these sectoral areas, for example, by developing crops which cultivate faster and are more resilient to current climate conditions, reducing the cost of crop cultivation, reducing carbon footprints by intelligently monitoring the transport systems, etc. However, due to the traditional governance, practices, and maintenance policies which are manual, many of the sectors in India such as discussed above are still underperforming, and thus require immediate technology upgradation. Recently, the utilization of unmanned autonomous vehicles has shown promising improvements in many of the sectors globally. For example, utilization of autonomous drone-planting systems can improve the uptake rate to 75% while reducing the planting costs by 85% [1]. Vast fields and low efficiency in crop monitoring together create farming’s largest obstacle. Also, unpredictable weather conditions exacerbate the risk and field maintenance costs. Although, previously satellite imagery offered the most advanced form of monitoring, they suffer from drawbacks such as images had to be ordered in advance, and could be taken only once a day. Further, the cost of investment is huge, and the images suffer from clarity during extreme weather conditions. However, in such scenarios, time-series animations developed using images collected from Unmanned Aerial Vehicles (UAVs) can provide the precise development of a crop and reveal production inefficiencies enabling better crop management. Utilization of autonomous UAVs for traffic sensing, disaster response, and infrastructure inspection have also proved to be efficient in enabling faster decision making [1]. Likewise, many domains such as maritime, waste management, pollution monitoring, etc., benefit from utilizing unmanned autonomous vehicles for efficient surveillance, management and faster decision making [2]. In a recent report, it is estimated that the autonomous navigation market is estimated at USD 2.2 billion in 2018 and is projected to reach USD 13.5 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 16.19% from 2018 to 2030. One can clearly observe the important role of autonomous navigation systems in this decade. Nevertheless, unmanned vehicles also suffer from several technological and social drawbacks such as integration of multi-sensory perception system, communication technologies, computing capabilities, payload capacity, swarming, stability, security, government regulations.The important challenges that are to be addressed before the autonomous navigation and data acquisition systems can be adopted in real-time use cases.

➢ Development of an autonomous Unmanned Ground Vehicle (UGV) for number of applications like surveillance, reconnaissance, agriculture, construction, etc.
➢ Development of real-time CPS system for multiple sensor based quality and efficient data aggregation system.
➢ Establishment of a living lab and testbed at IIT Hyderabad for the development and real-time testing of autonomous vehicles, autonomous navigation frameworks and data acquisition systems. Specifically, the context of Indian roads and traffic conditions will be considered thus benefiting Indian markets and policy making.
➢ Development of standard architecture for the Autonomous Vehicles in Indian context.
➢ Development of standard operating procedures and frameworks for quality data acquisition.
➢ Multiple sensor integration and calibration for real-time noise resilient or low-noise data aggregation using mobile aerial and ground vehicles.
➢ Efficient processing framework and transmission of huge amount of data collected from the multiple sensors integrated.
➢ To analyze and model driver behavior in Indian scenario with optical flow and semantic segmentation.
➢ Analysis of Indian traffic flow and accident.
➢ Development of algorithms and software for enabling fully autonomous functioning of UGVs in various terrains using ML/AI techniques.
➢ Experimenting with dynamic decision algorithms and system components leading to the development of the tools and technologies for a fully autonomous UGV
➢ Enabling System Security in UGV systems to prevent hackers from gaining control of these vehicles
➢ A thorough characterization of the possible security attacks on the ML-based components of an ADAS system. A library of countermeasures against these attacks, that vary in robustness, performance overhead, power overhead, and cost will be developed.
➢ Development of a light-weight framework for adopting speech technologies for autonomous navigation.
➢ Study the legal and ethical issues of autonomous vehicles which aid in policy making and while enacting legal frameworks.
➢ Testing and demonstration of the developed UGV


➢ Development of autonomous UAV system for urban and rural applications such as extending the cellular coverage, disaster, and survey operations.
➢ Development of standard operating procedures for quality data acquisition from multiple on-board sensors.
➢ Development of Autonomous UAVs and test facility
➢ Sensor calibration for quality data aggregation from mobile aerial vehicles.
➢ Integration of a variety of subsystems that include sensors and IoT, communications, edge based computer vision and artificial intelligence frameworks for intelligent decision making thus realizing a real-time aerial CPS system.
➢ Frameworks for quality data aggregation and transmission of huge data generated using the real-time aerial CPS system. ➢ Drone-based product delivery
➢ Radio frequency wave propagation modeling and analysis for UAV communications under different environmental conditions.
➢ Additive or 3D manufacturing of the UAVs and their components for light-weight and custom design.
➢ Stochastic system modeling of the UAVs with accurate modeling of interaction between software and hardware components.
➢ Automated drone swarming with adaptive network recovery and reformation
➢ Intelligent inter-drone swarm coordination and communication
➢ Define performance bounds (both theoretically and experimentally) for intra-swarm and inter-swarm communications using state of the art wireless technologies (both ad-hoc and cellular)


➢ Mechanical development of Humanoid including necessary electronics like motors, sensors, and gait activities.
➢ Development of necessary networking schemes, privacy and security measures.
➢ Computer Vision system that helps self-navigation, object recognition, and person identification.
➢ Speech recognition, understanding, and speech synthesis system to interact with humans.
➢ Analytics framework for cognitive social interaction with elderly and needy people.


➢ Realization of real-time CPS system using UAVs with edge-computing capabilities for real-time processing of huge data aggregated from various on-board sensors.
➢ Sensor calibration for quality data aggregation from mobile aerial and ground vehicles.
➢ Real-time transmission techniques for huge data generated from various on board sensors.
➢ The over-reaching aim of the collaboration includes development of the tools and methodologies in order to enable the cost-effective high throughput and standardized phenotyping services to assist crop-improvement programs.
➢ Utilization of UAVs or drones equipped with various sensors (RGB, Multispectral, Hyperspectral, Thermal, LIDAR, etc.) [4], [5] for accelerating the data collection in a standardized way.
➢ Development of UAV based soil nutrient analysis for optimization of the agronomic inputs.
➢ Deployment of ground sensor networks for acquiring soil parameters and UAVs for autonomous data acquisition from the deployed ground sensor networks.
➢ Growth stage pest and disease detection using images captured from UAVs.
➢ Development of UAV based autonomous remote sensing framework to monitor the different growth stages of crop.
➢ Analysis of different physical traits of plant and estimation of growth stage using the collected aerial images.
➢ Development of artificial intelligence based frameworks for predicting phenotypic traits, stress, pest and disease identification for deciding the best genotype at a faster pace and less human intervention.
➢ Development of an intelligent farmer assistance system which will collect input from farmers, process using computer vision processing unit, and send expert advice to the farmer regarding crop disease and remedies.
➢ Adapting and optimization of the developed AI based frameworks for edge computing platforms with minimal latency execution.

    TIH-Targets List

S.No. Target Area Set Targets Achieved Targets
1st Yr 2nd Yr 3rd Yr 4th Yr 5th Yr Total 1st Yr 2nd Yr 3rd Yr 4th Yr 5th Yr Total
1

Technology Development

1.1 No of Technologies (IP, Licensing, Patents etc) 3 5 7 10 10 35 3 0 0 0 0 3
1.2 Technology Products 3 5 7 10 10 35 4 0 0 0 0 4
1.3 Publications, IPR and other Intellectual activities 15 50 50 70 80 265 22 0 0 0 0 22
1.4 Increase in CPS Research Base 5 10 25 25 25 90 8 0 0 0 0 8
2

Entrepreneurship Development

2.1 Technology Business Incubator (TBI) 1 0 0 0 0 1 1 0 0 0 0 1
2.2 Start-ups & Spin-off companies 3 7 10 10 13 43 3 0 0 0 0 3
2.3 GCC - Grand Challenges & Competitions 0 1 1 1 1 4 0 0 0 0 0 0
2.4 Promotion and Acceleration of Young and Aspiring technology entrepreneurs (PRAYAS) 1 0 1 0 1 3 1 0 0 0 0 1
2.5 CPS-Entrepreneur In Residence (EIR) 2 3 8 8 9 30 2 0 0 0 0 2
2.6 Dedicated Innovation Accelerator (DIAL) 0 1 0 1 1 3 0 0 0 0 0 0
2.7 CPS-Seed Support System (CPS- SSS) 0 1 0 1 0 2 0 0 0 0 0 0
2.8 Job Creation 100 700 2600 3300 4000 10700 117 0 0 0 0 117
3

Human Resource Development

3.1 Graduate Fellowships 60 60 60 60 60 300 61 0 0 0 0 61
3.2 Post Graduate Fellowships 20 25 25 25 30 125 47 0 0 0 0 47
3.3 Doctoral Fellowships 10 15 10 5 0 40 25 0 0 0 0 25
3.4 Faculty Fellowships 3 3 3 3 3 15 6 0 0 0 0 6
3.5 Chair Professors 0 3 3 0 0 6 0 0 0 0 0 0
3.6 Skill Development 50 110 110 120 120 510 50
(1599)
0 0 0 0 50
3.7 Postdoctoral Fellowships 0 5 10 5 4 24 6 0 0 0 0 6
4

International Collaboration

4.1 International Collaboration 1 1 1 1 1 5 4 0 0 0 0 4
Total 277 1005 2931 3655 4368 12236 360 0 0 0 0 360
Total Set Targets 12236 Total Achieved Targets 360