Robotics Thesis Proposal
- Newell-Simon Hall
- SANKALP ARORA
- Ph.D. Student
- Robotics Institute
- Carnegie Mellon University
Safe, Efficient Data Gathering in Physical Spaces
Reliable and efficient acquisition of data from physical spaces will have countless applications in industry, policy and defense. The capability of gaining information at different scales makes Micro-Aerial Vehicles (MAVs) excellent for aforementioned applications. However, reasoning about information gathering at multiple resolution is NP-Hard and the state of the art methods are too slow to present an approximate solution online. Moreover, a robust data gathering system needs to reason about multi-resolution nature of information gathering while being safe, and cognizant of its sensory and battery limitations.
This thesis addresses three key aspects of enabling safe, efficient, multi-resolution data gathering: online budgeted multi-resolution informative path planning (IPP), guaranteeing safety and, optimization of sensing bandwidth for implicit and explicit data gathering requirements.
Firstly, we present an online navigation algorithm to guarantee the safety of the robot through an Emergency Maneuver Library (EML). We discuss an efficient method to construct EML while exploiting vehicle's dynamics capabilities. We then present an information gathering approach that optimizes the sensory actions to ensure vehicle safety and gain information relevant for mission progress. We validate these methods by deploying them on-board a full scale helicopter, demonstrating significant performance increase. We address the IPP problem through Randomized Anytime Orienteering (RAOr), an anytime, asymptotically near-optimal algorithm, that enables the planning for information gathering online.
We will focus our future work on three sub-problems that will lead to a safe, efficient data gathering framework. The first is developing a receding horizon planner that enables the vehicle to stay safe while maximizing the information gathered, through embedding safety constraint and information theoretic reward functions in sampling based planning framework. The second is learning a set of heuristics to enable faster multi-resolution informative path planning through RAOr. The third is to use the safe data gathering framework to improve vehicle's long-term performance through improving its assumptions about the environment.
We will evaluate the performance of our information gathering framework on an autonomous MAV. We expect that our framework will enable long term deployment of autonomous multi-resolution data gathering systems, while guaranteeing their safety, enabling MAVs to realize their potential as efficient data gatherers.
Sebastian Scherer (Chair)
William (Red) L. Whittaker
Kostas Alexis (University of Nevada, Reno)