Sandeep Manjanna

I am a PhD candidate at Mobile Robotics Lab (MRL) in School of Computer Science at McGill University, Montreal, Canada. My area of research is field robotics with a focus on designing planning algorithms for autonomous vehicles to sample and map challenging environments. My current research includes adaptive sampling and surveying of marine and freshwater environments to reconstruct static field maps of physical phenomena such as, dissolved oxygen, plankton density, and turbidity. The focus of my research is on building representative field maps by collecting samples in an efficient manner so that large-scale maps can be generated with a limited battery life of the robotic vehicles. I have developed algorithms to persistently survey the coral reefs by autonomously capturing the images of the corals and by sampling the water quality measurements over the reefs to asses their health. I am interested in studying and understanding large-scale marine ecosystems and the effects of environmental changes on these ecosystems.

Policy Search on Aggregated State Space for Active Sampling

In this project, we present an adaptive sampling technique that generates paths to efficiently measure and then mathematically model a scalar field by performing non-uniform measurements in a given region of interest. We compute a sampling path that minimizes the expected time to accurately model the phenomenon of interest by visiting high information regions using non-myopic path generation based on reinforcement learning.

Heterogeneous Multirobot System for Exploration and Strategic Water Sampling

Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. We present a multi-robot, data-driven, watersampling strategy, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples

Data Driven Sampling for Marine Vehicles using Multi-scale Trajectories

An ideal map of the scalar field requires complete coverage of the region, but can be approximated by a good sparse coverage strategy along with an efficient interpolation technique. We propose to optimize the trade off between the environmental field mapping and the costs (energy consumed, time spent, and distance traveled) associated with sensing. We present an anytime algorithm for sampling the environment adaptively by following a multi-scale path to produce a variable resolution map of the spatial field.