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An approach to mitigating the creation of additional orbital debris is to remove the sources of future medium debris by actively removing large spent objects from congested orbits. NASA has introduced the ADRV, an efficient and effective solution to remove large debris from LEO such as spent rocket bodies and non-functional satellites. The concept yields a single use, low-cost, lightweight, high mass fraction vehicle that enables the specific removal of large orbital debris (1000 - 4000 kg mass, 200 - 2000 km altitude, and 20 98-degree inclination). The ADRV performs rendezvous, approach, and capture of non-cooperative tumbling debris objects, maneuvering of the mated vehicle, and controlled, targeted reposition or deorbit of the mated vehicle. Due to its small form factor, up to eight ADRVs can be launched in a single payload, enabling high impact orbital debris removal missions within the same inclination group. Three key technologies were developed to enable the ADRV: - 1) The spacecraft control system (SCS) is a guidance, navigation, and control system that provides vehicle control during all phases of a mission; - (2) The debris object characterization system (DOCS) characterizes movement and capture of non-cooperative targets; and - (3) The capture and release system (CARS) allows the vehicle to capture and mate with orbital debris targets. These technologies can improve the current state-of-the-art capabilities of automated rendezvous and docking technology significantly for debris objects with tumbling rates up to 25 degrees per second. This approach leverages decades of spaceflight experience while automating key mission areas to reduce cost and improve the likelihood of success.

The goal of this computer vision software is to take the guesswork out of grapple operations aboard the ISS by providing a robotic arm operator with real-time pose estimation of the grapple fixtures relative to the robotic arms end effectors. To solve this Perspective-n-Point challenge, the software uses computer vision algorithms to determine alignment solutions between the position of the camera eyepoint with the position of the end effector as the borescope camera sensors are typically located several centimeters from their respective end effector grasping mechanisms. The software includes a machine learning component that uses a trained regional Convolutional Neural Network (r-CNN) to provide the capability to analyze a live camera feed to determine ISS fixture targets a robotic arm operator can interact with on orbit. This feature is intended to increase the grappling operational range of ISSs main robotic arm from a previous maximum of 0.5 meters for certain target types, to greater than 1.5 meters, while significantly reducing computation times for grasping operations. Industrial automation and robotics applications that rely on computer vision solutions may find value in this softwares capabilities. A wide range of emerging terrestrial robotic applications, outside of controlled environments, may also find value in the dynamic object recognition and state determination capabilities of this technology as successfully demonstrated by NASA on-orbit. This computer vision software is at a technology readiness level (TRL) 6, (system/sub-system model or prototype demonstration in an operational environment.), and the software is now available to license. Please note that NASA does not manufacture products itself for commercial sale.

The technology is comprised of a simple and reliable circuit that detects a single bad cell within a battery pack of hundreds of cells and it can monitor and balance the charge of individual cells in series. NASA's BMS is cost effective and can enhance safety and extend the life of critical battery systems, including high-voltage Li-ion batteries that are used in electric vehicles and other next-generation renewable energy applications. The BMS uses saturating transformers in a matrix arrangement to monitor cell voltage and balance the charge of individual battery cells that are in series within a battery string. The system includes a monitoring array and a voltage sensing and balancing system that integrates simply and efficiently with the battery cell array, limiting the number of pins and the complexity of circuitry in the battery. The arrangement has inherent galvanic isolation, low cell leakage currents, and allows a single bad or imbalanced cell in a series of several hundred to be identified. Cell balancing in multi-cell battery strings compensates for weaker cells by equalizing the charge on all the cells in the chain, thus extending battery life. Voltage sensing helps avoid damage from over-voltage that can occur during charging and from under-voltage that can occur through excessive discharging.

Originally developed by NASA and GM, the Robo-Glove technology was a spinoff of the Robonaut 2 (R2), the first humanoid robot in space. This wearable device allows the user to tightly grip tools and other items for longer periods of time without experiencing muscle discomfort or strain. An astronaut working in a pressurized suit outside the space station or an assembly operator in a factory might need to use 15 to 20 lbs of force to hold a tool during an operation. Use of the Robo-Glove, however, would potentially reduce the applied force to only 5 to 10 lbs. The Robo-Glove is a self-contained unit, essentially a robot on your hand, with actuators embedded into the glove that provide grasping support to human fingers. The pressure sensors, similar to the sensors that give R2 its sense of touch, are incorporated into the fingertips of the glove to detect when the user is grasping an object. When the user grasps the object, the synthetic tendons automatically retract, pulling the fingers into a gripping position and holding them there until the sensor is released by releasing the object. The current prototype weighs around two pounds, including control electronics and a small display for programming and diagnostics. A lithium-ion battery, such as one for power tools, is used to power the system and is worn separately on the belt.

NASA is currently developing the next generation space suit for future missions, including the optimization of space suit gloves. When non-assisted space suit gloves are coupled to a pressurized suit and operated in a vacuum, they tend to limit the range of motion of an astronaut's hand to as little as 20% of normal range. Many of NASA's future missions will be in challenging environments where an astronauts hand dexterity will be critical for the success of NASA missions. Innovators at JSC have improved the performance on the second-generation, robotically assisted SSRG, to reduce exertion and improve the hand strength and dexterity of an astronaut in situ. The SSRGs system detects user finger movements using string potentiometers and contact with objects using force-sensitive resistors (FSRs). FSRs are imbedded in the distal and medial phalanges, palmar side of the glove. To move a finger, an actuator pulls a tendon through a Bowden Cable system which transfers mechanical pulling force of an inner cable relative to a hollow outer cable, like the brakes on a bicycle, as seen in the Figure below. An improved controller commands the new, more powerful linear actuator to drive tendon operation while minding custom controller parameters inputted through a digital editor tool. The Space Suit RoboGlove is at TRL 6 (system/subsystem model or prototype demonstrated in a relevant environment) and it is now available for licensing. Please note that NASA does not manufacture products itself for commercial sale.

This concept presents a new statistical likelihood function and Bayesian analysis update for non-standard measurement types that rely on associations between observed and cataloged features. These measurement types inherently contain non-standard errors that standard techniques, such as the Kalman filter, make no effort to model, and this mismodeling can lead to filter instability and degraded performance. Vision-based navigation methods utilizing the Kalman filter involve a preprocessing step to identify features within an image by referencing a known catalog. However, errors in this pre-processing can cause navigation failures. AFP offers a new approach, processing points generated by features themselves without requiring identification. Points such as range or bearing are directly processed by AFP. Operating on finite set statistics principles, AFP treats data as sets rather than individual features. This enables simultaneous tracking of multiple targets without feature labeling. Unlike the sequential processing of the Kalman filter, AFP processes updates in parallel, independently scoring each output based on rigorous mathematical functions. This parallel processing ensures robust navigation updates in dynamic environments, and without requiring an identification algorithm upstream of the filter. Computational simulations conducted at Johnson Space Center demonstrate that AFP's performance matches or exceeds that of the ideal Kalman filter, even under non-ideal conditions. Anonymous Feature Processing for Enhanced Navigation is at a technology readiness level (TRL) 4 (component and/or breadboard validation in laboratory environment) and is now available for patent licensing. Please note that NASA does not manufacture products itself for commercial sale.

Robonaut 2 (R2) has the capability of functioning autonomously or it can be controlled by direct teleoperations, which is advantageous for hazardous environments. When functioning autonomously, R2 understands what to do and how to do it based on sensory input. R2's torso holds the control system while the visor holds several cameras that are incorporated into the visual perception system. With these capabilities, R2 can reduce or eliminate the need for humans to be exposed to dangerous environments. R2 also has a very rugged four-wheel base called the Centaur 2. The Centaur 2 base can lower or raise itself to and from the ground and turn its wheels in any direction, allowing it to turn in place and drive forward or sideways. This enables the R2 to enter hazardous areas or tackle difficult terrain without endangering its human operator. Robonaut 2 as a whole, or some of its components, can be an invaluable tool for land mine detection, bomb disposal, search and rescue, waste recycling, medical quarantined area, and so much more. The suite of technologies provides an ability to manipulate tools to help with a task, or it can tackle many tasks in a row, where a standard robot may not have the dexterity or sensing capability to get the job done. R2 could pick through nuclear waste, measure toxicity levels, and survey areas too remote or dangerous for human inspection. R2 could deal with improvised explosive devices, detect and dispose of bombs or landmines, and operate equipment that can break through walls or doors.

The innovation builds upon conventional UWB hardware by incorporating tracking methodology and algorithms in addition to external amplifiers for signal boost. The tracking methodology is a triangulation calculation consisting of Angle of Arrival (AOA) and Time Difference of Arrival (TDOA) using a cross-correlation peak detection method. By directly estimating TDOA information from UWB pulses, the method achieves the high temporal resolution (on the order of picoseconds) needed to measure AOA with extreme precision. The system uses a PC to synchronize and process data in real time from two receivers, or clusters, to display the position of the transmitter-equipped person or object. The interface software enables the PC to access the two data sets simultaneously through separate sockets. In the data collection process, data segments from each receiver are interleaved with those from the other receiver in chronological order of collection. Within the PC, the data segments are stored in a separate buffer; therefore, the contents of the buffers are representations of the same UWB pulse waveform arriving at the two receivers at approximately the same time. This data synchronization provides the separate and simultaneous collection of waveform data that the tracking algorithm requires for accurate real-time tracking.

The RFID Tag with Long Range and Wide Coverage Capabilities technology allows a RFID tag to direct a RFID reader beam signal back in the direction of arrival. This technology requires no added power to provide telemetry for long range readers by using multiple beams instead of one narrow beam signal. Each of the predetermined number of beams is typically associated with a unique identification number to derive bearing information. This innovation is suited for IC-based RFID tags as well as Surface Acoustics Wave (SAW) tags, which are useful for extreme environments. The technology improves the ability to obtain telemetry (quantity, location, or sensor information) without GPS over a distant range. When the tag reports its identification, it also provides angular information to the source, which makes this technology useful for navigation and mapping applications. Because the technology provides an estimated angle between the signal antenna and the surface of each tag, the technology is able to triangulate the position of a mobile item identified with a RFID tag. The same innovation can be integrated to a RFID reader in order to enhance its range and distribute power to passive tags. The innovation has commercial applications in construction, oil and gas, seaport/harbor management, Internet of Things (IoT) and many more industries.