Bio

Amanda Prorok is a Postdoc in the General Robotics, Automation, Sensing and Perception (GRASP) Lab at the University of Pennsylvania, where she works with Prof. Vijay Kumar on multi-robot systems. Prior to moving to UPenn, she spent one year working on cutting-edge sensor technologies at Sensirion, during which period her team launched the world’s first multi-pixel miniature gas sensor onto the market. She completed her PhD at EPFL, Switzerland, where she addressed the topic of indoor localization for large-scale, cooperative systems. Her dissertation was awarded the Asea Brown Boveri (ABB) award for the best thesis at EPFL in the fields of Computer Sciences, Automatics and Telecommunications. Before starting her doctorate, she spent two years in Japan working for Mitsubishi in the robotics industry, as well as for the Swiss government in a diplomatic role, on a full scholarship that was awarded to her by the Swiss-Japanese Chamber of Commerce.

Heterogeneous Robot Swarms

Heterogeneous Robot Swarms

As we harness swarms of autonomous robots to solve increasingly challenging tasks, we must find ways of distributing robot capabilities among distinct swarm members. My premise is that that one robot type is not able to cater to all aspects of a given task, due to constraints at the single-platform level. Yet, it is an open question how to engineer heterogeneous robot swarms, since we lack the foundational theories to help us make the right design choices and understand the implications of heterogeneity.

My approach to designing swarm robotic systems considers both top-down methodologies (macroscopic modeling) as well as bottom-up (single-robot level) algorithmic design. My first research thrust targeted the specific problem of indoor localization for large robot teams, and employed a fusion of ultra-wideband and infrared signals to produce high accuracy. I developed the first ultra-wideband time-difference-of-arrival sensor model for mobile robot localization, which, when used collaboratively, achieved centimeter-level accuracy. Experiments with ten robots illustrated the effect of distributing the sensing capabilities heterogeneously throughout the team. This bottom-up approach highlighted the compromise between homogenous teams that are very efficient, yet expensive, and heterogeneous teams that are low-cost.

My second research thrust, which aims at formally understanding this compromise, targets the general problem of distributing a heterogeneous swarm of robots among a set of tasks. My strategy is to model the swarm macroscopically, and subsequently extract decentralized control algorithms that are optimal given the heterogeneous swarm composition and underlying task requirements. I developed a dedicated diversity metric that identifies the relationship between performance and heterogeneity, and that provides a means with which to control the composition of the swarm so that performance is maximized. This top-down approach complements the bottom-up method by providing high-level abstraction and foundational analyses, thus shaping a new way of exploiting heterogeneity as a design paradigm.