Mon, Apr 24, 2023 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
PHD Thesis Defense - Christopher Denniston
Title: Active Sensing In Robotic Deployment
Committee Members: Prof. Gaurav S. Sukhatme (Chair), Prof. Jesse Thomason, Prof. David A. Caron
Date/Time: April 24th, 2-4pm
Location: RTH 306 or on Zoom https://usc.zoom.us/j/2869134593
Robots have the potential to greatly increase our ability to measure complex environmental phenomena, such as harmful algae blooms, which can harm humans and animals alike in drinking water. Such phenomena require study and measurement at a scale that is beyond what can be accomplished by robots that plan to completely cover the area. Despite this, many sensing robots still are deployed with non-active behaviors, such as fixed back-and-forth patterns. The lack of deployment of active sensing systems in practice is due to difficulties with problems encountered in the real world. We identify and address solutions for three main issues which plague complex real robotic active sensing deployments.
First, active sensing systems are difficult to use, with complex deployment-time decisions that affect the efficiency of sensing. We describe systems that eschew these decisions, allowing for efficient and automatic deployment. We find that these systems provide a non-technical deployment procedure and outperform hand-tuned behaviors.
Second, active sensing robots tend to perform a survey that maximizes some general goal and requires the user to interpret the collected data. We propose a system that, instead, plans for the specific user task of collecting physical samples at limited, unknown locations. We demonstrate that planning for this specific task while sensing allows for more efficiency in the active sensing behavior.
Finally, existing models for active sensing do not accurately model the interaction of the sensed signal and obstacles in the environment. We propose two novel modeling techniques which allow active sensing of signals which have complex interactions with obstacles, such as electromagnetic waves. Both outperform traditional modeling techniques and enable scalable active sensing to a large number of measurements on a real robot. Additionally, we find that they allow the robot to actively place signal-emitting devices while sensing the signals from these placed devices.
Audiences: Everyone Is Invited
Contact: Asiroh Cham