APL at 100: A Research and Exploratory Development Perspective
Although predicting the future is a difficult task, in many ways the Research and Exploratory Development Department (REDD) at the Johns Hopkins University Applied Physics Laboratory (APL) exists to do just that. The department’s researchers seek to envision future challenges for APL and the nation and develop innovative solutions to those challenges. This brief article begins with a framework for thinking about what APL research and development will look like when the Lab reaches its centennial. Then it discusses some key areas of research that we predict will be active in 2042.
DetectsVX: Organophosphate-Sensing Hydrogel Platform
Materials that selectively actuate in response to chemicals in the environment can serve as the foundation for new sensing platforms that take advantage of innate chemical reactivities to provide low-power, selective sensing of chemical agents. A Johns Hopkins University Applied Physics Laboratory (APL) team designed an organophosphate-sensing platform, called DetectsVX, that uses a hydrogel. Initial demonstrations of sensors based on this selectively actuating material have been successful, and the team is currently pursuing innovations in both hydrogel chemistry and sensing mode.
System Integration with Multiscale Networks (SIMoN): A Geospatial Model Transformation Framework for a Sustainable Future
A team at the Johns Hopkins University Applied Physics Laboratory (APL) developed SIMoN (System Integration with Multiscale Networks), a framework that connects predictive resource models from domains such as water, electricity, climate, and population, and passes data about resource usage and availability between models. SIMoN is useful for interfacing models with different native environments and geospatial definitions and can potentially be adapted to many other applications.
Untethered Autonomous Soft Robotics
Liquid crystal elastomer (LCE)–based soft robots with reversible actuation could be beneficial for both Department of Defense and civilian applications, including in exploration of confined spaces, payload delivery, remote sensing and data collection, and small biomedical devices. In the work described in this article, we developed a first-principle model for designing high-work-capacity LCEs. Further, we built bilayer structures for actuation applications. We then built a Bluetooth-controlled soft robotic system and quantified its performance. The article also discusses the outlook for LCE-based soft robotics for Department of Defense applications.
Aerobatic Flight for Robotic Fixed-Wing Unmanned Aerial Vehicles
Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles have traditionally been severely limited in terms of maneuverability. Through technical advancements in controls and platform design, the Johns Hopkins University Applied Physics Laboratory (APL) is widening the flight envelope for autonomous fixed-wing UAVs.
Thin-Film Thermoelectric Conversion Devices for Direct Thermal-to-Electric Conversion for DC and Pulse Power
New propulsion technologies are critical to developing new capabilities in Department of Defense platforms. An innovative approach taking nuclear heat and directly generating DC electric power with solid-state thermoelectric devices, without the need for a steam power plant, can lead to reliable and compact systems while offering several system-level advantages. These advanced thermal-to-electric device developments are also applicable to efficient radioisotope thermoelectric generators (RTGs) for space outer-planetary missions. Similarly, many platforms, and special operations in particular, need very compact (lightweight, small volume) pulse electric power sources with high specific power density in the range of ~1,000 kW/kg and a long shelf life. This article describes progress with fundamental scientific advances relevant to these thermal-to-electric conversion applications leveraging recent advances in nano-engineered thin-film thermoelectric materials.
Neuro-Inspired Dynamic Replanning in Swarms—Theoretical Neuroscience Extends Swarming in Complex Environments
In the NeuroSwarms framework, a team including researchers from the Johns Hopkins University Applied Physics Laboratory (APL) and the Johns Hopkins University School of Medicine (JHM) applied key theoretical concepts from neuroscience to models of distributed multi-agent autonomous systems and found that complex swarming behaviors arise from simple learning rules used by the mammalian brain.
Motifs to Models: Leveraging Biological Circuits toward Novel Computational Substrates
The Motifs to Models team at the Johns Hopkins University Applied Physics Laboratory (APL) leverages the existence proof provided by biological circuitry—of robustness, adaptability, and low-sample learning at very low size, weight, and power—to explore novel computational substrates toward critical sponsor needs in computation and artificial intelligence.
Verification of Safety in Artificial Intelligence and Reinforcement Learning Systems
For complex artificially intelligent systems to be incorporated into applications where safety is critical, the systems must be safe and reliable. This article describes work a Johns Hopkins University Applied Physics Laboratory (APL) team is doing toward verifying safety in artificial intelligence and reinforcement learning systems.
Intent-Aware Pedestrian Prediction for Adaptive Crowd Navigation
In this article, we describe the work of a team of researchers from the Johns Hopkins University Applied Physics Laboratory (APL) and Johns Hopkins University (JHU) to develop adaptive crowd navigation policies for robots by reasoning and predicting future pedestrian motion.