Dr. Prithvi Krishna Chittoor is currently an Assistant Professor at the Department of Computational Intelligence, within the School of Computing at SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India. He was a Postdoctoral Research Fellow at the Singapore University of Technology and Design (SUTD), Singapore. He earned his Ph.D. from SRM Institute of Science and Technology, Chennai, India, with a specialization in Robotics. He holds a Master’s degree in Robotics Engineering and a Bachelor’s degree in Electrical and Electronics Engineering. He further enriched his academic exposure through a prestigious professional internship under the TEEP@AsiaPlus Program at Tamkang University in Taiwan, funded by the Ministry of Education in Taiwan. His research pursuits encompass a diverse range of interests, including drones, robots, autonomous navigation, and wireless charging.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Multidisciplinary, Electrical and Electronic Engineering, Mechanical Engineering
21
Scopus Publications
520
Scholar Citations
8
Scholar h-index
7
Scholar i10-index
Scopus Publications
Digital Eye Strain Monitoring for One-Hour Smartphone Engagement Through Eye Activity Measurement System Bhanu Dandumahanti, Prithvi Chittoor, Murali Subramaniyam Journal of Eye Movement Research, 2025 Smartphones have revolutionized our daily lives, becoming portable pocket computers with easy internet access. India, the second-highest smartphone and internet user, experienced a significant rise in smartphone usage between 2013 and 2024. Prolonged smartphone use, exceeding 20 min at a time, can lead to physical and mental health issues, including psychophysiological disorders. Digital devices and their extended exposure to blue light cause digital eyestrain, sleep disorders and visual-related problems. This research examines the impact of 1 h smartphone usage on visual fatigue among young Indian adults. A portable, low-cost system has been developed to measure visual activity to address this. The developed visual activity measurement system measures blink rate, inter-blink interval, and pupil diameter. Measured eye activity was recorded during 1 h smartphone usage of e-book reading, video watching, and social-media reels (short videos). Social media reels show increased screen variations, affecting pupil dilation and reducing blink rate due to continuous screen brightness and intensity changes. This reduction in blink rate and increase in inter-blink interval or pupil dilation could lead to visual fatigue.
Data-Driven Selection of Decontamination Robot Locomotion Based on Terrain Compatibility Scoring Models Prithvi Krishna Chittoor, A. Jayasurya, Sriniketh Konduri, Eduardo Sanchez Cruz, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara Applied Sciences Switzerland, 2025 Decontamination robots are becoming more common in environments where reducing human exposure to hazardous substances is essential, including healthcare settings, laboratories, and industrial cleanrooms. Designing terrain-capable decontamination robots quickly is challenging due to varying operational surfaces and mobility limitations. To tackle this issue, a structured recommendation framework is proposed to automate selecting optimal locomotion types and track configurations, significantly cutting down design time. The proposed system features a two-stage evaluation process: first, it creates an annotated compatibility score matrix by validating locomotion types against a robust dataset based on factors like friction coefficient, roughness, payload capacity, and slope gradient; second, it employs a weighted scoring model to rank wheel/track types based on their appropriateness for the identified environmental conditions. User needs are processed dynamically using a large language model, enabling flexible and scalable management of various deployment scenarios. A prototype decontamination robot was developed following the proposed algorithm’s guidance. This framework speeds up the configuration process and establishes a foundation for more intelligent, terrain-aware robot design workflows that can be applied to industrial, healthcare, and service robotics sectors.
Payload- and Energy-Aware Tactical Allocation Loop-Based Path-Planning Algorithm for Urban Fumigation Robots Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Abishegan M., Sriniketh Konduri, S. M. Bhagya P. Samarakoon, Mohan Rajesh Elara Mathematics, 2025 Fumigation effectively manages pests, yet manual spraying poses long-term health risks to operators, making autonomous fumigation robots safer and more efficient. Path planning is a crucial aspect of deploying autonomous robots; it primarily focuses on minimizing energy consumption and maximizing operational time. The Payload and Energy-aware Tactical Allocation Loop (PETAL) algorithm integrates a genetic algorithm to search for waypoint permutations, applies a 2-OPT (two-edge exchange) local search to refine those routes, and leverages an energy cost function that reflects payload weight changes during spraying. This combined strategy minimizes travel distance and reduces energy consumption across extended fumigation missions. To evaluate its effectiveness, a comparative study was performed between PETAL and prominent algorithms such as A*, a hybrid Dijkstra with A*, random search, and a greedy distance-first approach, using both randomly generated environments and a real-time map from an actual deployment site. The PETAL algorithm consistently performed better than baseline algorithms in simulations, demonstrating significant savings in energy usage and distance traveled. On a randomly generated map, the PETAL algorithm achieved 6.05% higher energy efficiency and 23.58% shorter travel distance than the baseline path-planning algorithm. It achieved 15.69% and 31.66% in energy efficiency and distance traveled saved on a real-time map, respectively. Such improvements can diminish operator exposure, extend mission durations, and foster safer, more efficient urban pest control.
Developing an Urban Landscape Fumigation Service Robot: A Machine-Learned, Gen-AI-Based Design Trade Study Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Prabakaran Veerajagadheswar, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara Applied Sciences Switzerland, 2025 Generative AI (Gen-AI) revolutionizes design by leveraging machine learning to generate innovative solutions. It analyzes data to identify patterns, creates tailored designs, enhances creativity, and allows designers to explore complex possibilities for diverse industries. This study uses a Gen-AI design generation process to develop an urban landscape fumigation service robot. This study proposes a machine-learned multimodal and feedback-based variational autoencoder (MMF-VAE) model that incorporates a readily available spraying robot dataset and includes design considerations from various research efforts to ensure real-time deployability. The objective is to demonstrate the effectiveness of data-driven and feedback-based approaches in generating design specifications for a fumigation robot with the targeted requirements of autonomous navigation, precision spraying, and an extended runtime. The design generation process comprises three stages: (1) parameter fixation, emphasizing functionality-based and aesthetic-based specifications; (2) design specification generation using the proposed MMF-VAE model with and without a spraying robot dataset; and (3) robot development based on the generated specifications. A comparative analysis evaluated the impact of the dataset-driven design generation. The design generated with the dataset proved more feasible and optimized for real-world deployment with the integration of multimodal inputs and iterative feedback refinement. A real-time prototype was then constructed using the model’s parametric constraints and tested in actual fumigation scenarios to validate operational viability. This study highlights the transformative potential of Gen-AI in robotic design workflows.
Occupancy-belief Planning of Plant Manipulation for Staking Pusong Li, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Prithvi Krishna Chittoor, Mohan Rajesh Elara, Rakesh Nagi IEEE International Conference on Intelligent Robots and Systems, 2025 While agricultural robotics has made great strides in recent years, manipulation of plants for tasks such as staking and harvesting remains highly challenging due to the high variability in dynamics and deformable nature of plants. To address the challenges created by dynamics uncertainty, we develop a system applying an occupancy-belief planning concept to plant manipulation for staking. We first train a dynamics model that predicts a per-pixel probability that the plant occupies the corresponding slice in space after a drag action using a large set of simulators. This model is then used to plan a manipulation action that maximizes the probability areas swept by the stake tying tool’s operating region are occupied by the plant, and minimize the probability areas swept by the non-operating side regions of the tool are occupied. We demonstrate our method both in simulation and with zero-shot sim-to-real transfer to a physical implementation. We show that adding consideration of belief through use of occupancy-belief allows our method to outperform both the visual foresight type approaches it is based on and other baselines and ablations, especially in the real-world case.
Boa Fumigator: An Intelligent Robotic Approach for Mosquito Control Sriniketh Konduri, Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Zhenyuan Yang, Mohan Rajesh Elara, Grace Hephzibah Jaichandar Technologies, 2024 The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications.
KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion Charan Vikram, Sidharth Jeyabal, Prithvi Krishna Chittoor, Sathian Pookkuttath, Mohan Rajesh Elara, Wang You Agriculture Switzerland, 2024 Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.
Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics Sidharth Jeyabal, Charan Vikram, Prithvi Krishna Chittoor, Mohan Rajesh Elara Applied Sciences Switzerland, 2024 Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.
Wireless Electrification System for Photovoltaic Powered Autonomous Drone Charging Prithvi Krishna Chittoor, Bharatiraja Chokkalingam IEEE Transactions on Transportation Electrification, 2024 The future is moving toward fully autonomous drone transportation-delivery systems. However, handling the charging of a large number of drones is still a pivotal problem in the drone charging infrastructure. The wired charging or battery-swapping method requires a large number of people or machines moving around the pad, creating obstructions for drones during landing and takeoff. In this article, a novel Building Integrated Photovoltaic (BIPV) structure is developed. The proposed system concentrates on wirelessly charging drones on the rooftop of the building and utilizing the wall space for electrification. However, the BIPV panels are subjected to shading effects from upper panels, reduced solar irradiations due to panel inclination, and the effect of temperature from the wall surface reflected solar irradiations. In the proposed experiment, four BIPV panel placements are compared for generating maximum output for the charging station. The proposed system transferred 120 W wirelessly with 88.6% power transfer efficiency at 10 mm vertical displacement. The BIPV concept has the potential to create an autonomous wireless charging environment for multiple drones for high-rise buildings with limited roof area. Furthermore, the article presents an economic analysis for cost-saving and reducing CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions.
Occupancy-belief Planning of Plant Manipulation for Staking P Li, SMBP Samarakoon, MAVJ Muthugala, PK Chittoor, MR Elara, ... 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems … , 2025 2025
Digital Eye Strain Monitoring for One-Hour Smartphone Engagement Through Eye Activity Measurement System BP Dandumahanti, PK Chittoor, M Subramaniyam Journal of Eye Movement Research 18 (4), 34 , 2025 2025 Citations: 5
Data-Driven Selection of Decontamination Robot Locomotion Based on Terrain Compatibility Scoring Models PK Chittoor, A Jayasurya, S Konduri, ES Cruz, SMBP Samarakoon, ... Applied Sciences 15 (14), 7781 , 2025 2025 Citations: 4
Payload-and Energy-Aware Tactical Allocation Loop-Based Path-Planning Algorithm for Urban Fumigation Robots PK Chittoor, BP Dandumahanti, A M, S Konduri, SMBP Samarakoon, ... Mathematics 13 (6), 950 , 2025 2025 Citations: 2
Developing an urban landscape fumigation service robot: A Machine-Learned, Gen-AI-Based design trade study PK Chittoor, BP Dandumahanti, P Veerajagadheswar, ... Applied Sciences 15 (4), 2061 , 2025 2025 Citations: 5
Boa fumigator: an intelligent robotic approach for mosquito control S Konduri, PK Chittoor, BP Dandumahanti, Z Yang, MR Elara, ... Technologies 12 (12), 255 , 2024 2024 Citations: 4
Koala: A modular dual-arm robot for automated precision pruning equipped with cross-functionality sensor fusion C Vikram, S Jeyabal, PK Chittoor, S Pookkuttath, MR Elara, W You Agriculture 14 (10), 1852 , 2024 2024 Citations: 8
Revolutionizing urban pest management with sensor fusion and precision fumigation robotics S Jeyabal, C Vikram, PK Chittoor, MR Elara Applied Sciences 14 (16), 7382 , 2024 2024 Citations: 5
An assessment of shortest prioritized path-based bidirectional wireless charging approach toward smart agriculture PK Chittoor, B Chokkalingam, R Verma, L Mihet-Popa IEEE Access 11, 123742-123755 , 2023 2023 Citations: 21
Wireless electrification system for photovoltaic powered autonomous drone charging PK Chittoor, B Chokkalingam IEEE Transactions on Transportation Electrification 10 (2), 3002-3011 , 2023 2023 Citations: 23
PV-Powered Wireless Drone Charging Station Assisted with Tracked-Vision A Nair, C Bharatiraja, C Prithvi Krishna, S Devakirubakaran 2023 Second International Conference on Electrical, Electronics, Information … , 2023 2023 Citations: 2
Building integrated photovoltaic powered wireless drone charging system PK Chittoor, C Bharatiraja Solar Energy 252, 163-175 , 2023 2023 Citations: 30
An IoT based centralized smart locker using RFID technology YVB C. Bharatiraja, Prithvi Krishna Chittoor AIP Conference Proceedings 2427 (1), 020098-1 to 020098-7 , 2023 2023 Citations: 7
Sensor fusion based intelligent hydroponic farming and nursing system YV Bhargava, PK Chittoor, C Bharatiraja, R Verma, K Sathiyasekar IEEE Sensors Journal 22 (14), 14584-14591 , 2022 2022 Citations: 32
Drone Operated Bidirectional Wireless Charging System for Energy Constrained Devices in Smart Farming Applications PK Chittoor, B C Electrochemical Society Transactions 107 (1), 11867-11874 , 2022 2022
IoT Powered Smart Hydroponic System for Autonomous Irrigation and Crop Monitoring YV Bhargava, PK Chittoor, B C Electrochemical Society Transactions 107 (1), 11937-11944 , 2022 2022 Citations: 6
Wireless-sensor communication based wireless-charging coil positioning system for UAVs with maximum power point tracking PK Chittoor, C Bharatiraja IEEE Sensors Journal 22 (8), 8175-8182 , 2022 2022 Citations: 30
Solar integrated wireless drone charging system for smart city applications PK Chittoor, C Bharatiraja 2021 IEEE 6th International Conference on Computing, Communication and … , 2021 2021 Citations: 10
A review on UAV wireless charging: Fundamentals, applications, charging techniques and standards PK Chittoor, B Chokkalingam, L Mihet-Popa IEEE access 9, 69235-69266 , 2021 2021 Citations: 326
Deep Learning-Based Autonomous Drone (s) Assistance C Prithvi Krishna, C Bharatiraja International Conference on Automation, Signal Processing, Instrumentation … , 2020 2020
MOST CITED SCHOLAR PUBLICATIONS
A review on UAV wireless charging: Fundamentals, applications, charging techniques and standards PK Chittoor, B Chokkalingam, L Mihet-Popa IEEE access 9, 69235-69266 , 2021 2021 Citations: 326
Sensor fusion based intelligent hydroponic farming and nursing system YV Bhargava, PK Chittoor, C Bharatiraja, R Verma, K Sathiyasekar IEEE Sensors Journal 22 (14), 14584-14591 , 2022 2022 Citations: 32
Building integrated photovoltaic powered wireless drone charging system PK Chittoor, C Bharatiraja Solar Energy 252, 163-175 , 2023 2023 Citations: 30
Wireless-sensor communication based wireless-charging coil positioning system for UAVs with maximum power point tracking PK Chittoor, C Bharatiraja IEEE Sensors Journal 22 (8), 8175-8182 , 2022 2022 Citations: 30
Wireless electrification system for photovoltaic powered autonomous drone charging PK Chittoor, B Chokkalingam IEEE Transactions on Transportation Electrification 10 (2), 3002-3011 , 2023 2023 Citations: 23
An assessment of shortest prioritized path-based bidirectional wireless charging approach toward smart agriculture PK Chittoor, B Chokkalingam, R Verma, L Mihet-Popa IEEE Access 11, 123742-123755 , 2023 2023 Citations: 21
Solar integrated wireless drone charging system for smart city applications PK Chittoor, C Bharatiraja 2021 IEEE 6th International Conference on Computing, Communication and … , 2021 2021 Citations: 10
Koala: A modular dual-arm robot for automated precision pruning equipped with cross-functionality sensor fusion C Vikram, S Jeyabal, PK Chittoor, S Pookkuttath, MR Elara, W You Agriculture 14 (10), 1852 , 2024 2024 Citations: 8
An IoT based centralized smart locker using RFID technology YVB C. Bharatiraja, Prithvi Krishna Chittoor AIP Conference Proceedings 2427 (1), 020098-1 to 020098-7 , 2023 2023 Citations: 7
IoT Powered Smart Hydroponic System for Autonomous Irrigation and Crop Monitoring YV Bhargava, PK Chittoor, B C Electrochemical Society Transactions 107 (1), 11937-11944 , 2022 2022 Citations: 6
Digital Eye Strain Monitoring for One-Hour Smartphone Engagement Through Eye Activity Measurement System BP Dandumahanti, PK Chittoor, M Subramaniyam Journal of Eye Movement Research 18 (4), 34 , 2025 2025 Citations: 5
Developing an urban landscape fumigation service robot: A Machine-Learned, Gen-AI-Based design trade study PK Chittoor, BP Dandumahanti, P Veerajagadheswar, ... Applied Sciences 15 (4), 2061 , 2025 2025 Citations: 5
Revolutionizing urban pest management with sensor fusion and precision fumigation robotics S Jeyabal, C Vikram, PK Chittoor, MR Elara Applied Sciences 14 (16), 7382 , 2024 2024 Citations: 5
Data-Driven Selection of Decontamination Robot Locomotion Based on Terrain Compatibility Scoring Models PK Chittoor, A Jayasurya, S Konduri, ES Cruz, SMBP Samarakoon, ... Applied Sciences 15 (14), 7781 , 2025 2025 Citations: 4
Boa fumigator: an intelligent robotic approach for mosquito control S Konduri, PK Chittoor, BP Dandumahanti, Z Yang, MR Elara, ... Technologies 12 (12), 255 , 2024 2024 Citations: 4
Payload-and Energy-Aware Tactical Allocation Loop-Based Path-Planning Algorithm for Urban Fumigation Robots PK Chittoor, BP Dandumahanti, A M, S Konduri, SMBP Samarakoon, ... Mathematics 13 (6), 950 , 2025 2025 Citations: 2
PV-Powered Wireless Drone Charging Station Assisted with Tracked-Vision A Nair, C Bharatiraja, C Prithvi Krishna, S Devakirubakaran 2023 Second International Conference on Electrical, Electronics, Information … , 2023 2023 Citations: 2
Occupancy-belief Planning of Plant Manipulation for Staking P Li, SMBP Samarakoon, MAVJ Muthugala, PK Chittoor, MR Elara, ... 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems … , 2025 2025
Drone Operated Bidirectional Wireless Charging System for Energy Constrained Devices in Smart Farming Applications PK Chittoor, B C Electrochemical Society Transactions 107 (1), 11867-11874 , 2022 2022
Deep Learning-Based Autonomous Drone (s) Assistance C Prithvi Krishna, C Bharatiraja International Conference on Automation, Signal Processing, Instrumentation … , 2020 2020
