Distributed Autonomous Systems Laboratory
Prof. Girish Chowdhary
Prof. Girish Chowdhary is the director of Distribulted Autonomous Systems Laboratory(DASLAB) and Associate Professor and Donald Biggar Willet Faculty Fellow at the University of Illinois at Urbana-Champaign. He holds appointments with Agricultural and Biological Engineering, Coordinated Science Lab, Aerospace Engineering, Electrical and Computer Engineering, Department of Computer Science and the Beckman Institute.
Prof. Chowdhary was an Assistant Professor at the Department of Mechanical and Aerospace Engineering, Oklahoma State University for three years prior to moving to UIUC. He was a Postdoctoral Researcher at the Laboratory for Information and Decision Systems (LIDS) of the Massachusetts Institute of Technology.
Prof. Chowdhary has a Ph.D. degree from Georgia Institute of Technology. Prior to coming to Georgia Tech, he spent three years working as a research engineer with the German Aerospace Center's (DLR) Institute for Flight Systems Technology in Braunschweig, Germany. He holds a BE with honors from RMIT university in Melbourne, Australia.
Prof. Chowdhary is the author of several peer reviewed publications spanning the area of adaptive control, fault tolerant control, autonomy and decision making, machine learning, vision and LIDAR based perception for Unmanned Aerial Systems (UAS), and GPS denied navigation. He has been involved in the development of over 15 research unmanned aerial platforms.
Meet Our Team
Nolan Replogle, M.S.
Oklahoma State University, USA
Hassan Kingravi, Ph.D.
Pindrop Security, USA
Hossein Mohomadipanah, Ph.D.
University of Wisconsin, USA
Maximilian Muehlegg, Ph.D.
Audi GMbH, Germany
Erkan Kayacan, Ph.D.
Assistant Professor, The University of Queensland, Australia
Zhongzhong Zhang, Ph.D.
Harshal Maske, Ph.D.
Ford Motor Company, USA
Denis Osipychev, Ph.D.
Wyatt McAllsiter, Ph.D.
Sri Theja Vuppala, M.S.
EarthSense Inc., USA
Karan Chawla, M.S.
Elroy Air, USA
Sathwik M. Tejaswi, M.S.
Schneider National, USA
Hunter Young, M.S.
Intelligent Automation Inc., USA
Beau Barber, M.S.
Jasvir Virdi, M.S.
Nexteer Automotive, USA
Aaron Havens, M.S.
Garrett Thomas Gowan, M.S.
Aurora Innovation Inc., USA
Bhavana Jain, M.S.
Benjamin Thompson, B.S.
HCM Systems, USA
Volga Can Karakus, B.S.
EarthSense Inc., USA
Genevieve(Genny) Korn, B.S.
Micron Technology, USA
Billy Doherty, B.S.
EarthSense Inc., USA
Zhenwei(Selina) Wu, B.S.
Sahil Modi, B.S.
Joshua Varghese, B.S.
Anwesa Choudhuri, Girish Chowdhary, Alexander G. Schwing International Conference on Computer Vision (ICCV), 2021
Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tracking of objects over time remains a challenge. MOTS methods formulate tracking locally, i.e., frame-by-frame, leading to sub-optimal results. Classical global methods on tracking operate directly on object detections, which can lead to a combinatorial growth in the detection space. To address these issues, we formulate a global method for MOTS over the space of assignments rather than detections. In step 1 of our two step method we find all top-k assignments of objects detected and segmented between any two consecutive frames. We then develop a structured prediction formulation to score assignment sequences across any number of consecutive frames and use dynamic programming to find the global optimizer of the structured prediction formulation in polynomial time. In step 2 we connect objects which reappear after having been out of view for some time. For this we formulate an assignment problem. On the challenging KITTI-MOTS and MOTSChallenge datasets, this method achieves state-of-the-art results among methods which don’t use depth information.
Arun Sivakumar, Sahil Modi, Mateus Gasparino, Che Ellis, Andres Velasquez, Girish Chowdhary*, Saurabh Gupta* Robotics: Science and Systems (RSS), 2021
We describe a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for over-the-canopy drones or larger agricultural equipment. However, autonomously navigating them under the canopy presents a number of challenges: unreliable GPS and LiDAR, high cost of sensing, challenging farm terrain, clutter due to leaves and weeds, and large variability in appearance over the season and across crop types. We address these challenges by building a modular system that leverages machine learning for robust and generalizable perception from monocular RGB images from low-cost cameras, and model predictive control for accurate control in challenging terrain. Our system, CropFollow, is able to autonomously drive 485 meters per intervention on average, outperforming a state-of-the-art LiDAR based system (286 meters per intervention) in extensive field testing spanning over 25 km.
Andres Eduardo Baquero Velasquez, Vitor Akihiro Hisano Higuti, Mateus Valverde Gasparino, Arun Narenthiran Sivakumar, Marcelo Becker, Girish Chowdhary Under Review
This paper presents a state-of-the-art LiDAR based autonomous navigation system for under-canopy agricultural robots. Under-canopy agricultural navigation has been a challenging problem because GNSS and other positioning sensors are prone to significant errors due to attentuation and multi-path caused by crop leaves and stems. Reactive navigation by detecting crop rows using LiDAR measurements is a better alternative to GPS but suffers from challenges due to occlusion from leaves under the canopy. Our system addresses this challenge by fusing IMU and LiDAR measurements using an Extended Kalman Filter framework on low-cost hardwware. In addition, a local goal generator is introduced to provide locally optimal reference trajectories to the onboard controller. Our system is validated extensively in real-world field environments over a distance of 50.88~km on multiple robots in different field conditions across different locations. We report state-of-the-art distance between intervention results, showing that our system is able to safely navigate without interventions for 386.9~m on average in fields without significant gaps in the crop rows, 56.1~m in production fields and 47.5~m in fields with gaps (space of 1~m without plants in both sides of the row).
Wyatt McAllister, Joshua Whitman, Joshua Varghese, Adam Davis, Girish Chowdhary IEEE Transactions on Robotics, 2021
This work presents advances in predictive modeling of weed growth, as well as an improved planning index to be used in conjunction with these techniques, for the purpose of improving the performance of coordinated weeding algorithms being developed for industrial agriculture. We demonstrate that the evolving Gaussian process (E-GP) method applied to measurements from the agents can predict the evolution of the field within the realistic simulation environment, Weed World. This method also provides physical insight into the seed bank distribution of the field. In this work, we extend the E-GP model in two important ways. First, we have developed a model that has a bias term, and we show how it is connected to the seed bank distribution. Second, we show that one may decouple the component of the model representing weed growth from the component, which varies with the seed bank distribution, and adapt the latter online. We compare this predictive approach with one that relies on known properties of the weed growth model and show that the E-GP method can drive down the total weed biomass for fields with high seed bank densities using less agents, without assuming this model information. We use an improved planning index, the Whittle index, which allows a balanced tradeoff between exploiting a row or allowing it to accrue reward and conforms to what we show is the theoretical limit for the fewest number of agents, which can be used in this domain.
Prabhat K. Mishra, Mateus V. Gasparino, Andres E. B. Velsasquez, Girish Chowdhary Under Review
A control affine nonlinear discrete time system is considerd with mached and bounded state dependent uncertainties. Since the structure of uncertainties is not known, a deep learning based adaptive mechanism is utilized to encounter disturbances. In order to avoid any unwanted behavior during the learning phase, a tube based model predictive controller is employed, which ensures satisfaction of constraints and input-to-state stability of the closed-loop states. In addition, the proposed approach is applicable to multi-agent systems with non-interacting agents and a centralized controller.
Joshua E. Whitman, Harshal Maske, Hassan A. Kingravi, Girish Chowdhary IEEE Control Systems Magazine, 2021
This article should be useful for anyone interested in using robots in large-scale environments that are changing in time and space. The methods presented here have been used to help teams of robots monitor and destroy weeds within a field of crops. In general, this article discusses modeling and monitoring complex systems that vary in both space and time, given a limited number of agents or sensors providing measurements spread out across a large area. A novel method for solving this problem is presented, with several tremendously useful properties. First, it can be easily trained and updated even with large, “dirty” data collected at many places at many times. Second, this model lends itself well to the kinds of analyses familiar to the controls community, which means that several formerly very challenging problems become much easier (for example, predicting future evolution, deciding how many sensors are needed and where to place them, and determining the basic structures beneath the system dynamics). As far as it is possible to tell, the methods presented here are unmatched in their scope and power. A graduate-level mathematical background is recommended for this article, and an open code repository is provided for the ease of implementation.
Tianchen Ji, Sri Theja Vuppala, Girish Chowdhary, Katherine Driggs-Campbell Conference on Robot Learning, 2020
To achieve high-levels of autonomy, modern robots require the ability to detect and recover from anomalies and failures with minimal human supervision. Multi-modal sensor signals could provide more information for such anomaly detection tasks; however, the fusion of high-dimensional and heterogeneous sensor modalities remains a challenging problem. We propose a deep learning neural network: supervised variational autoencoder (SVAE), for failure identification in unstructured and uncertain environments. Our model leverages the representational power of VAE to extract robust features from high-dimensional inputs for supervised learning tasks. The training objective unifies the generative model and the discriminative model, thus making the learning a one-stage procedure. Our experiments on real field robot data demonstrate superior failure identification performance than baseline methods, and that our model learns interpretable representations.
Girish Joshi, Jasvir Virdi, Girish Chowdhary Conference on Robot Learning, 2020
In this paper, we present Asynchronous implementation of Deep Neural Network-based Model Reference Adaptive Control (DMRAC). We evaluate this new neuro-adaptive control architecture through flight tests on a small quadcopter. We demonstrate that a single DMRAC controller can handle significant nonlinearities due to severe system faults and deliberate wind disturbances while executing high-bandwidth attitude control. We also show that the architecture has long-term learning abilities across different flight regimes, and can generalize to fly different flight trajectories than those on which it was trained. These results demonstrating the efficacy of this architecture for high bandwidth closed-loop attitude control of unstable and nonlinear robots operating in adverse situations. To achieve these results, we designed a software+communication architecture to ensure online real-time inference of the deep network on a high-bandwidth computation-limited platform. We expect that this architecture will benefit other deep learning in the closed-loop experiments on robots.
Naveen Kumar Uppalapati, Benjamin Walt, Aaron Havens, Armeen Mahdian, Girish Chowdhary, Girish Krishnan Robotics: Science and Systems (RSS), 2020
We present a hybrid rigid-soft arm and manipulator for performing tasks requiring dexterity and reach in cluttered environments. Our system combines the benefit of the dexterity of a variable length soft manipulator and the rigid support capability of a hard arm. The hard arm positions the extendable soft manipulator close to the target, and the soft arm manipulator navigates the last few centimeters to reach and grab the target. A novel magnetic sensor and reinforcement learning based control is developed for end effector position control of the robot. A compliant gripper with an IR reflectance sensing system is designed, and a k-nearest neighbor classifier is used to detect target engagement. The system is evaluated in several challenging berry picking scenarios.
Girish Chowdhary, Mattia Gazzola, Girish Krishnan, Chinmay Soman, Sarah Lovell MDPI Sustainability, 2019
The shortage of qualified human labor is a key challenge facing farmers, limiting profit margins and preventing the adoption of sustainable and diversified agroecosystems, such as agroforestry. New technologies in robotics could offer a solution to such limitations. Advances in soft arms and manipulators can enable agricultural robots that can have better reach and dexterity around plants than traditional robots equipped with hard industrial robotic arms. Soft robotic arms and manipulators can be far less expensive to manufacture and significantly lighter than their hard counterparts. Furthermore, they can be simpler to design and manufacture since they rely on fluidic pressurization as the primary mechanisms of operation. However, current soft robotic arms are difficult to design and control, slow to actuate, and have limited payloads. In this paper, we discuss the benefits and challenges of soft robotics technology and what it could mean for sustainable agriculture and agroforestry.
Vitor A. H. Higuti, Andres E. B. Velasquez, Daniel Varela Magalhaes, Marcelo Becker, Girish Chowdhary Journal of Field Robotics, 2018
This paper describes a light detection and ranging (LiDAR)-based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM-30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row-based and high-leaf-cover semistructured crop plantations, such as corn (Zea mays) and sorghum ( Sorghum bicolor). The key contribution of our work is a LiDAR-based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low-hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within-row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row-lines based on current and previous information. The proposed LiDAR-based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR-based navigation in realistic field environments for small under-canopy robots.
Erkan Kayacan, Zhongzhong Zhang, Girish Chowdhary Robotics: Science and Systems (RSS), 2018 (Best Paper Award)
This paper presents embedded high precision control and corn stands counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor and stand counting are measured manually. This is highly labor intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a nonlinear moving horizon estimator that identifies key terrain parameters using onboard robot sensors and a learning-based nonlinear model predictive control (NMPC) that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm to enable TerraSentia to count corn stands by driving through the fields autonomously. We present results of an extensive field-test study that shows that i) the robot can track paths precisely with less than 5cm error so that the robot is less likely to damage plants, and ii) the machine vision algorithm is robust against interferences from leaves and weeds, and the system has been verified in corn fields at the growth stage of V4, V6, VT, R2, and R6 from five different locations. The robot predictions agree well with the ground truth with the correlation coefficient R=0.96.