Robotic manipulators are critical in many applications but are known to degrade over time. This degradation is influenced by the nature of the tasks performed by the robot. Tasks with higher severity, such as handling heavy payloads, can accelerate the degradation process. One way this degradation is reflected is in the position accuracy of the robot’s end-effector. In this article, we present a prognostic modeling framework that predicts a robotic manipulator’s remaining useful life (RUL) while accounting for the effects of task severity. Our framework represents the robot’s position accuracy as a Brownian motion process with a random drift parameter that is influenced by task severity. The dynamic nature of task severity is modeled using a continuous-time Markov chain (CTMC). To evaluate RUL, we discuss two approaches: 1) a novel closed-form expression for the residual life distribution (RLD) and 2) Monte Carlo (MC) simulations, commonly used in prognostics literature. Theoretical results establish the equivalence between these RUL computation approaches. We validate our framework through experiments using two distinct physics-based simulators for planar and spatial robot fleets. Our findings show that robots in both fleets experience shorter RUL when handling a higher proportion of high-severity tasks.

Multi-limb coordination is necessary for many daily activities. It also has particular relevance for a growing area of human-system applications such as teleoperated robot operation and supernumerary robot operation where an additional robotic limb may complement natural limb functions. Introducing sup-plemental sensory information, specifically haptic feedback, has shown the potential to improve assistive robotics control, human-machine interactions, and even human motor learning. However, there has been little research on how sensory information, including haptic feedback and visual information, contributes to the execution of multi-limb coordinated tasks. In this paper, we designed a tri-limb coordinated task where participants move a cursor to a randomly positioned target on a screen guided by variations of both haptic feedback and visual information. Our results indicated that while haptic feedback could partially sub-stitute for visual information, it could not completely replace nor surpass the task performance achieved with visual information as feedback. Importantly, haptic feedback does not negatively impact performance metrics such as how often the participant coordinates multiple limbs simultaneously (coordination score), controls their limbs without redundant movement (normalized sub-optimal path), completes the task (success rate), and the mental workload perceived during the task (cognitive load). This study provides insight into the contribution of sensory feedback in facilitating multi-limb coordination, providing relevant guidance for numerous applications of human interactions with assistive technologies.

We present a joint demonstration between the Robotics, Autonomous Systems, and Controls Laboratory (RASCAL) at UC Davis and the Columbia University Robotics Group, wherein a human-in-the-loop robotic grasping platform in the Columbia lab (New York, NY) is controlled to select and grasp an object by a C3-C4 spinal cord injury (SCI) subject in the UC Davis lab (Davis, CA) using a new single-signal, multi-degree-of-freedom surface electromyography (sEMG) human-robot interface. The grasping system breaks the grasping task into a multi-stage pipeline that can be navigated with only a few inputs. It integrates pre-planned grasps with on-line grasp planning capability and an object recognition and target selection system capable of handling multi-object scenes with moderate occlusion. Previous work performed in the RASCAL lab demonstrated that by continuously modulating the power in two individual bands in the frequency spectrum of a single sEMG signal, users were able to control a cursor in 2D for cursor to target tasks. Using this paradigm, four targets were presented in order for the subject to command the multi-stage grasping pipeline. We demonstrate that using this system, operators are able to grasp objects in a remote location using a robotic grasping platform.

We report prototype development and testing of a new mobile-phone-based brain-muscle-computer interface for severely paralyzed persons, based on previous results from our group showing that humans may actively create specified power levels in two separate frequency bands of a single surface electromyography (sEMG) signal. EMG activity on the surface of a single face muscle site (auricularis superior) is recorded with a standard electrode. This analog electrical signal is imported into an Android-based mobile phone and digitized via an internal A/D converter. The digital signal is split, and then simultaneously filtered with two band-pass filters to extract total power within two separate frequency bands. The user-modulated power in each frequency band serves as two separate control channels for machine control. After signal processing, the Android phone sends commands to external devices via a Bluetooth interface. Users are trained to use the device via visually based operant conditioning, with simple cursor-to-target activities on the phone screen. The mobile-phone prototype interface is formally evaluated on a single advanced Spinal Muscle Atrophy subject, who has successfully used the interface in his home in evaluation trials and for remote control of a television. Development of this new device will not only guide future interface design for community use, but will also serve as an information technology bridge for in situ data collection to quantify human sEMG manipulation abilities for a relevant population.

Optimizing and controlling the energy use of a hybrid-electric propulsion system is difficult due to the interaction of nonlinear mechanical, thermodynamic, and electromechanical devices. An optimization routine for the energy use of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle (UAV), the application of a cerebellar model arithmetic computer (CMAC) neural network to approximate the optimization results and control the hybrid-electric system, and simulation results are presented. The small hybrid-electric UAV is intended for military and homeland security missions involving intelligence, surveillance, or reconnaissance (ISR). The flexible optimization routine allows relative importance to be assigned between the use of gasoline, electricity, and recharging. The CMAC controller saves on the required memory compared to a look-up table by two orders of magnitude. The hybrid-electric UAV with the CMAC controller uses 37.8% less energy than a two-stroke gasoline-powered UAV during a three-hour ISR mission.

We describe technology and tools being created for a series of biorobotics studies of Norway rat pups. These studies aim to inform both animal behavior and autonomous robotics. For biology, the crucial idea is that by simulating animal behavior by instantiating actual sensorimotor rules, the specifics for how this behavior was achieved represents a new neurobehavioral/neuroethological theory on how the biological system itself achieves its behavior. For robotics, studying infant mammals enables the study of development of sensorimotor rules from the simple to the complex. Our studies are unique in that animal, simulation, and robot studies occur in parallel and inform each other. Our initial goal is to develop scientific methods to approach the problem and robotics technology that can instantiate the methods. Towards that goal, we describe our dynamic simulation models, our robotic rat pups, our robot tracking algorithms, and our data analysis techniques.