The ability of robotic systems to effectively address disaster scenarios that are potentially dangerous for human operators is continuing to grow as a research and development field. This leverages research from areas such as bimanual manipulation, dexterous grasping, bipedal locomotion, computer vision, sensing, object segmentation, varying degrees of autonomy, and operator control/feedback. This paper describes the development of a semi-autonomous bimanual dexterous robotic system that comes to the aid of a mannequin simulating an injured victim by operating a fire extinguisher, affixing a cervical collar, cooperatively placing the victim on a spineboard with another bimanual robot, and relocating the victim. This system accomplishes these tasks through a series of control modalities that range from supervised autonomy to full teleoperation and allows the control model to be chosen and optimized for a specific subtask. We present a description of the hardware platform, the software control architecture, a human-in-the-loop computer vision algorithm, and an infrastructure to use a variety of user input devices in combination with autonomous control to compete several dexterous tasks. The effectiveness of the system was demonstrated in both laboratory and live outdoor demonstrations.

Existing brain-computer interface (BCI) control of highly dexterous robotic manipulators and prosthetic devices typically rely solely on neural decode algorithms to determine the user's intended motion. Although these approaches have made significant progress in the ability to control high degree of freedom (DOF) manipulators, the ability to perform activities of daily living (ADL) is still an ongoing research endeavor. In this paper, we describe a hybrid system that combines elements of autonomous robotic manipulation with neural decode algorithms to maneuver a highly dexterous robotic manipulator for a reach and grasp task. This system was demonstrated using a human patient with cortical micro-electrode arrays allowing the user to manipulate an object on a table and place it at a desired location. The preliminary results for this system are promising in that it demonstrates the potential to blend robotic control to perform lower level manipulation tasks with neural control that allows the user to focus on higher level tasks thereby reducing the cognitive load and increasing the success rate of performing ADL type activities.

The Johns Hopkins University Applied Physics Laboratory (APL) led the development of the Modular Prosthetic Limb (MPL), an advanced upper-extremity prosthetic limb, under the DARPA Revolutionizing Prosthetics program. In addition to its use as an advanced prosthetic, APL is also exploring more advanced autonomy including closed-loop force feedback control with the MPL. This short paper provides an overview of the MPL system and summarizes two demonstrations performed at APL using fingertip force sensors to perform autonomous writing and fingertip grasping.

A vision-based obstacle detection system for small unmanned aerial vehicles (UAVs) is presented. Obstacles are detected by segmenting the image into sky and non-sky regions and treating the non-sky regions as obstacles. The feasibility of this approach is demonstrated by using the vision output to steer a small unmanned aircraft to fly towards an obstacle. The experiment was first verified in a hardware in the loop (HIL) simulation and then successfully implemented on a small modified remote control plane using a large inflatable balloon as the obstacle.

We have developed a novel device to supply hydraulic power for anaerobic mobile robotic systems. This power source is capable of operation in environments with no oxygen, such as underwater and space. The design, unlike the internal combustion engine, produces power on demand, eliminating idling when there is no load on the system. Steam and oxygen are the only byproducts of this power supply. This monopropellant-powered free piston hydraulic pump (FPHP) was designed as a human scale (1.0 to 3.0 kW) power supply. The FPHP utilizes high concentration hydrogen peroxide, which decomposes into hot gas when exposed to a catalyst, as the monopropellant energy source. Energy is extracted from the hydrogen peroxide and transferred directly to hydraulic fluid by expanding the hot decomposition gas in an integrated piston/cylinder arrangement. Based upon a specific power and specific energy analysis using a Ragone plot, the performance of the FPHP potentially exceeds that of a battery based hydraulic power supply for short operation times.