1. Design and Implementation of Path Trackers for Ackermann Drive based Vehicles (Preprint, 2019)

    Abstract: This article is an overview of the various literature on path tracking methods and their implementation in simulation and realistic operating environments. The scope of this study includes analysis, implementation, tuning, and comparison of some selected path tracking methods commonly used in practice for trajectory tracking in autonomous vehicles. Many of these methods are applicable at low speed due to the linear assumption for the system model, and hence, some methods are also included that consider non-linearities present in lateral vehicle dynamics during high-speed navigation. The performance evaluation and comparison of tracking methods are carried out on realistic simulations and a dedicated instrumented passenger car, Mahindra e2o, to get a performance idea of all the methods in realistic operating conditions and develop tuning methodologies for each of the methods. It has been observed that our model predictive control-based approach is able to perform better compared to the others in medium velocity ranges.
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  2. A Proposal of FPGA-based Low Cost and Power Efficient Autonomous Fruit Harvester
    IEEE 6th International Conference on Control, Automation and Robotics (ICCAR 2020), Singapore
    Abstract: In this paper, we present a power-efficient and low-cost prototype of a robotic harvester which employs multiple subsystems such as fruit detection, odometry, localization, proficient manipulation through computer vision, deep learning and a novel end-effector design. Fruit Plucking is performed using an end effector, and 3-degree of freedom(DOF) arm (made out of the integration of two linear actuators and a rotating platform) consolidated with a 4-wheeled differential drive mobile platform. Effective implementation of the visual processing is executed on the FPGA Fabric of the Xilinx PYNQ-Z2 Board, which accelerates Deep Neural Networks (DNNs) with improved Latency and Energy Efficiency as compared to a CPU or GPU based implementation.
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  3. A Prototype of an Intelligent Ground Vehicle for constrained environment: Design and Development (Accepted, to appear)
    IEEE International Conference on Control and Robots (ICCR 2019), Jeju Island, South Korea
    Autonomous vehicles are bound to take over the urban road scenario in the near future. While fully autonomous driving is yet to be deployed on urban roads unconditionally, constrained environments provide an opportunity for preliminary testing and validation as the technology emerges. Autonomous robots can be tuned to be robust in constrained environments. The technologies developed can then be extended and transferred to unconstrained environments with required safety precautions. This paper describes the design, development and testing of  KLAVYA 7.0, an autonomous differential drive robot that can follow lanes while avoiding stationary obstacles as well as navigate through a series of land markings specified by GPS coordinates. It was developed to participate in the 27th Intelligent Ground Vehicle Competition (IGVC 2019). The paper describes the overall mechanical, embedded and software architecture developed for this constrained environment along with system integration, testing and results.
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