Advisor: Geoff Hollinger
Associated Lab: Robot Decision Making Laboratory
Email: manish_saroya@honda-ri.com
Thesis: Topological Learning for Robotic Exploration and Navigation in Uncertain Environment
I’m passionate about building reliable autonomous robots for a range of applications including exploration, inspection, and mobility. From the past 7 years, I have been programming robots that make people's lives easier.
Research Interests: prediction based planning, deep learning, robot exploration and probabilistic methods.
MS in Robotics
B.Tech. in Electrical and Electronic Engineering
Team Member - DARPA Subterranean challenge (2018-2020)
Graduate Research Assistant - Oregon State University (2019-2020)
Graduate Teaching Assistant - Oregon State University (2019-2020)
Research Engineer - The Hitech Robotic Systemz Ltd., Gurgaon, India (2016-2018)
Visiting Scholar - Biorobotics Lab, CMU Pittsburgh, (2015)
Research Internship - Indian Institute of Technology Bombay, (2014)
Developed topology-informed Growing Neural Gas to generate navigation roadmaps for uncertain environments. Compared to a denser PRM, our method provides a ×5 computation efficiency improvement for shortest path search.
Figure shows a roadmap (grey graph) generated by our topology-informed growing neural gas algorithm. The probabilistic occupancy grid (colored image) is generated using a Hilbert map from point-cloud observations of the Intel Research Lab. The white path is a solution path planned over the roadmap through uncertain obstacles and narrow doorways.
IROS 2020 Paper, Code, Presentation Video
Learning structural cues to gain insight about unexplored regions in subterranean tunnel networks using techniques from topological image segmentation and image inpainting. These topological predictions improve frontier based exploration policy by 11–30 %.
In the figure, robot (blue) navigates from the start (red) at bottom centre. At each timestep, the robot decides which frontier (orange) to navigate to next. For the top row, the areas predicted to be open are shown in green. These lie within the unmasked area of interest (shaded).
Paper
Developed a novel recurrent convolutional deep learning architecture forestimating the missing elevation angle from 2D sonar images. Recurrent approach improves performance by up to8 % over previous single-frame deep learning approaches.
Integrated 2D lidar-based Adaptive Monte Carlo Localization (AMCL) on Autonomous Ground Vehicles (AGVs) utilized for material handling applications in various factory floors. These AGVs are in continuous operation since deployment in 2018.
Implemented and deployed a pure pursuit path tracking algorithm on autonomous forklifts. Generated non-holonomic trajectories for forklifts to perform pallet picking tasks with the path-planning team.
Devised a method for learning odometry motion model parameters to account for manufacturing errors.
Programmatically generated realistic subterranean tunnel networks. These environments contain structure in the form of topological features (loops and connected passages).
The below figure shows programmatically generated 3D multi-level gazebo world that are intended to represent the SubT urban circuit environment. Each generated world consists of a set of randomized floors. Each floor contains a random number of walls, with random sizes, positions and visual textures. Each floor plane has rectangular holes cut out of it so that a robot can navigate between different levels.
Designed a multiagent sequential controller utilizing a smooth affine field for robustness against environmental perturbations and modeling errors.
Developed a snake robot by mechanically connecting multiple spherical modules. Derived kinematic model for state estimation and control.
3D printed 9 DOF Snake Robot, which could navigate in rocky terrains. Wrote ROS nodes to perform various gaits such as sidewinding, rolling, and tree climbing. Created a GUI with Python's TkInter to run different gaits, and control various parameters such as motor-ID, speed, phase, etc.
Generated joint angles for walking and grasping gaits.
