The Distributed and Collaborative Intelligent Systems and Technology (DCIST) Collaborative Research Alliance (CRA) will create Autonomous, Resilient, Cognitive, Heterogeneous Swarms that can enable humans to participate in wide range of missions in dynamically changing, harsh and contested environments. These include search and rescue of hostages, information gathering after terrorist attacks or natural disasters, and humanitarian missions. Teams of humans and robots will operate as a cohesive team with robots preventing humans from coming in harms way (Force Protection) and extending and amplifying their reach to allow 1 human to do the work of 10 humans (Force Multiplication). The team is led by the University of Pennsylvania and includes collaborators from the U.S. Army Research Laboratory, Massachusetts Institute of Technology, Georgia Institute of Technology, University of California and University of Southern California.
Applications for unmanned aerial and ground vehicles requiring autonomous navigation in unknown, cluttered, and dynamically changing environments are increasing in fields such as transportation, delivery, agriculture, environmental monitoring, and construction. To achieve safe, resilient, and self-improving autonomous navigation, this project focuses on the design of adaptive online environment understanding and Lyapunov-theoretic control techniques to guarantees stable and collision-free operation in challenging conditions. This research direction is important because current practices rely on prior or hand-crafted maps that attempt to capture the whole environment, even if parts are irrelevant for specific navigation tasks. This increases memory and computation requirements, spreads the effects of noise, and makes current approaches brittle, particularly in conditions involving dynamic obstacles, unreliable localization, or illumination variation.
This project tackles the need to develop scalable computational methods for joint perception and planning that enable a team of autonomous agents to extract task-relevant information from heterogeneous and distributed data sources to support collaborative and distributed optimal planning with formal performance guarantees. Future control and decision-making systems need to strike a balance between guaranteed performance in the presence of uncertainty, extraction of information relevant to the task at hand, and reduced algorithmic complexity to enable real-time inference, planning, and learning. This confluence of control, estimation, machine learning, and computational science and engineering is necessary for high-confidence, high-reliability, minimal-supervision autonomous systems that can understand and act in high optempo missions. This project develops unified perception/action representations that can be used across different modalities and spatiotemporal scales; task-aware perception methods that account for the underlying control objective and the available informational and computational resources across the team; quantification of uncertainty in the unified multi-modal representations to enable principled information exchange among agents; hierarchical team architectures and abstractions that can model planning and estimation problems at the right level of granularity; distributed decision-making and inference methods capable of dealing with streaming multi-resolution data.
This project aims to take advantage of the hyperconvergence of computation, storage, sensing, and communication in small unmanned aerial vehicles (UAVs) to realize large-scale mapping of environmental factors such as temperature, vegetation, pressure, and chemical concentration that contribute to fire initiation. Developing UAV teams that recharge autonomously and communicate intermittently among each other and with static sensors will aid firefighters with continuous real-time surveillance and early detection of ensuing fires. This project focuses on three fundamental innovations to address the scientific challenges associated with autonomous, collaborative environmental monitoring. First, a new Satisfiability Modulo Optimal Control framework is proposed to handle mixed continuous flight dynamics and discrete constraints and ensure collision avoidance, persistent communication, and autonomous recharging for UAV navigation. Second, a distributed systems architecture using new uncertainty-weighted models will be developed to enable cooperative mapping across a heterogeneous team of UAVs and static sensors and avoid bandwidth-intensive data streaming. Lastly, a new Bayesian learning and inference approach is proposed to generate multi-modal (e.g., thermal, semantic, geometric, chemical) maps of real-time environmental conditions with adaptive accuracy and uncertainty quantification.