Electrical vehicles (EV) have been considered to be an effective way to combat global climate change. To extend the driving range of EV, this paper studies the active battery cell balancing control based on linear parametric varying model predictive control (MPC). Specifically, an equivalent circuit model is used to predict cell terminal voltage, and three different MPC-based battery cell balancing control strategies are proposed to dynamically transport electricity from cell to cell to reduce the imbalance. In particular, for the first control strategy, MPC is set up to be a tracking controller with the primary control objective of forcing all cells’ terminal voltage to follow the same trajectory generated by a nominal cell model; for the second control strategy, MPC maximizes the lowest cell voltage, so that the battery operating range can be extended; for the third and last strategy, MPC minimizes the maximum variation among cell terminal voltages. To assess the effectiveness of the proposed battery cell balancing control strategies, simulations are performed on all three MPC formulations, using both steady-state and transient conditions. Numerical results show that the proposed battery cell balancing control can achieve a driving range extension of 9% for dynamic driving cycle and 7% for steady-state condition, based on our simulation setup. Compared to the existing work, our approaches do not require the over-restrictive assumption that the trip duration is known in advance, while at the same time achieve similar driving range extension. Furthermore, it is also shown that different driving condition favors different cell balancing control strategy, indicating a need for a hybrid approach. Finally, real time implementability is demonstrated via throughput analysis.Note to Practitioners—Improving the efficiency of electric vehicles is of paramount importance to combat the global climate challenge. This paper contributes by proposing effective cell level balancing control methodologies to extend the driving range of electric vehicles to improve their energy efficiency and public acceptance. The control methods, which are based on model predictive control, are analytically derived with details for embedded implementation. Simulation results demonstrate the effectiveness of the proposed methodologies, with future work to investigate the applicability of nonlinear model predictive control with large number of cells.

This paper examines the performance differences across gender and age when operating a robotic manipulator arm and also seeks to determine which human factors are considered important predictors of performance for each group. To examine these differences, 93 participants were recruited and divided up into both male (46) and female (47) as well as young (46) and old (47). While men and women had nearly identical human factors, except for women exhibiting better dexterity, different navigation strategies were utilized by the genders leading men to perform the tasks quicker and with less overall moves than women. While task completion speed was affected most by working memory (WM) and spatial abilities for men, it was seen to be mostly dependent on physical abilities for women. Substantial differences were seen between the age cohorts in WM and dexterity which resulted in the younger cohort completing tasks quicker and with a higher rate of commands than the older cohort; no difference was observed in the total number of moves which provided evidence of a similar navigation strategy across the age groups. To improve task speed performance, older adults used all facets of their information processing and spatial abilities as compared to the younger group who used a narrower subset. To compensate for the aforementioned variations in important human factors, human-computer interface design considerations are suggested.

Robots assisting the disabled or elderly must perform complex manipulation tasks and must adapt to the home environment and preferences of their user. Learning from demonstration is a promising choice, that would allow the non-technical user to teach the robot different tasks. However, collecting demonstrations in the home environment of a disabled user is time consuming, disruptive to the comfort of the user, and presents safety challenges. It would be desirable to perform the demonstrations in a virtual environment. In this paper we describe a solution to the challenging problem of behavior transfer from virtual demonstration to a physical robot. The virtual demonstrations are used to train a deep neural network based controller, which is using a Long Short Term Memory (LSTM) recurrent neural network to generate trajectories. The training process uses a Mixture Density Network (MDN) to calculate an error signal suitable for the multimodal nature of demonstrations. The controller learned in the virtual environment is transferred to a physical robot (a Rethink Robotics Baxter). An off-the-shelf vision component is used to substitute for geometric knowledge available in the simulation and an inverse kinematics module is used to allow the Baxter to enact the trajectory. Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot, (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allows the controller to learn how to correct its manipulation mistakes.

Picking up an object with a wheelchair mounted robotic arm can be decomposed into a wheelchair navigation task designed to position the robotic arm such that the object is “easy to reach”, and the actual grasp performed by the robotic arm. A convenient definition of the notion of ease of reach can be given by creating a score (ERS) that relies on the number of distinct ways the object can be picked up from a given location. Unfortunately, the accurate calculation of ERS must rely on repeating the path planning process for every candidate position and grasp type, in the presence of obstacles. In this paper we use the bootstrap aggregation over hand-crafted, domain specific features to learn a model for the estimation of ERS. In a simulation study, we show that the estimated ERS closely matches the actual value and the speed of estimation is fast enough for real-time operation, even in the presence of a large number of obstacles in the scene.

The goal of this study was to model the important individual differences to predict a user's performance when operating an assistive robotic manipulator for a general population. Prior research done led to the identification of ten potential human factors to be observed including dexterity (gross and fine), spatial abilities (orientation and visualization), visual acuity in each eye, visual perception, depth perception, reaction time, and working memory. Eighty-nine individuals completed a test battery of potential human factors and, then, completed several tasks using a robotic manipulator designed to simulate find-and-fetch/pick-and-place tasks. During interaction with the robot, time on task, number of moves, and number of moves per minute were recorded. We successfully developed statistical models predicting performance that revealed several important human factors. Speed of information processing, spatial ability, dexterity, and working memory were all seen to be significant predictors of task performance. For time on task, linear and polynomial models showed roughly similar predictive performance on unseen test data achieving root-mean-square percentage error of about 7.3%; for number of moves per minute, a polynomial model was best with 9.1% error; and for number of moves, a linear model was best with 12.8% error.

This brief is focused on the parameter estimation problem of a second-order adaptive quadratic neuronal model. First, it is shown that the model discontinuities at the spiking instants can be recast as an impulse train driving the system dynamics. Through manipulation of the system dynamics, the membrane voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parameterized realizable model is then utilized inside a prediction error-based framework to design a dynamic estimator that allows for rapid estimation of model parameters under a persistently exciting input current injection. Simulation results show the feasibility of this approach to predict multiple neuronal firing patterns. Results using both synthetic data (obtained from a detailed ion-channel-based model) and experimental data (obtained from in vitro embryonic rat motoneurons) suggest directions for further work.

We report a small dual cohort pilot study with traumatic spinal cord injured (SCI) subjects designed to investigate the utility of a wheelchair-mounted robotic arm for these subjects. The UCF-MANUS, a vision-based 6DOF assistive robotic arm, has been designed to aid individuals with upper limb extremities to complete tasks of daily living that they would otherwise be unable to complete themselves. Pick-and-place IADL tasks were designed and ten (10) users post-SCI were selected under IRB guidelines to be trained and tested with the system for 1 to 2 h weekly over a period of three weeks. During this time, they controlled the robot either through a manual or an autonomous (supervised) mode of operation. Baseline characteristics (pre-study), quantitative performance metrics (during study), and psychometrics (post-study) were obtained and statistically analyzed to test a set of hypotheses related to performance and satisfaction with the two control modes. At the end of the study, both the autonomous and the manual mode had comparable task completion times while user effort required for operating the robot in autonomous mode was significantly less than that for the manual mode. However, the autonomous mode failed to commensurately raise the user's level of satisfaction. Over the three-week study, the manual mode users showed a pronounced learning effect in terms of reducing mean task completion time and number of commands while the auto mode users showed improvement in terms of reduction of variability. Based on qualitative feedback and quantitative results, possible directions for system design are presented to concurrently achieve better performance and satisfaction outcomes.

The UCF-MANUS, a vision-based 6DOF assistive robotic arm, has been designed to aid individuals with arm function limitations to complete tasks of daily living that they would otherwise be unable to complete themselves. This paper reports a small dual cohort pilot study with traumatic spinal cord injured (SCI) subjects designed to investigate the utility of the UCF-MANUS for these subjects. Pick-and-place ADL tasks were de÷ned and users trained and tested with the system for three weeks during which they controlled the robot either through a manual or an autonomous (supervised) mode of operation. Baseline characteristics (pre-study), quantitative performance metrics (during study) and psychometrics (post-study) were obtained and statistically analyzed to test a set of hypotheses related to performance and satisfaction with the two control modes. It was seen that manual interaction showed more variability and inef÷ciency in performance metrics as compared to autonomous operation. Suprisingly the latter mode, however, did not lead to better measures for user satisfaction. A discussion is provided to explain the results. Based on qualitative feedback and quantitative results, possible directions for system design are presented in order to concurrently achieve better performance and satisfaction outcomes.

We describe the progress in implementing a vision based robotic assist device to facilitate Activities of Daily Living (ADL) tasks for a class of users with motor disabilities. The goal of the research is to reduce time to task completion and cognitive burden for users interacting with an unstructured environment via a Wheelchair Mounted Robotic Arm (WMRA). A developed robot system is tested with five healthy subjects to assess its usefulness.

We document the progress in the design and implementation of a motion control strategy that exploits visual feedback from a narrow baseline stereo head mounted in the hand of a wheelchair mounted robot arm (WMRA) to recognize and grasp textured ADL objects for which one or more templates exist in a large image database. The problem is made challenging by kinematic uncertainty in the robot, imperfect camera and stereo calibration, as well as the fact that we work in unstructured environments. The approach relies on separating the overall motion into gross and fine motion components. During the gross motion phase, local structure on an object around a user selected point of interest (POI) is extracted using sparse stereo information which is then utilized to converge on and roughly align the object with the image plane in order to be able to pursue object recognition and fine motion with strong likelihood of success. Fine motion is utilized to grasp the target object by relying on feature correspondences between the live object view and its template image. While features are detected using a robust real-time keypoint tracker, a hybrid visual servoing technique is exploited in which tracked pixel space features are utilized to generate translational motion commands while a Euclidean homography decomposition scheme is utilized for generation of orientation setpoints for the robot gripper. Experimental results are presented to demonstrate the efficacy of the proposed algorithm.

In this paper, a homography-based adaptive visual servo controller is described to enable a robot end-effector to track a desired Euclidean space trajectory as determined by a sequence of images for the fixed camera configuration. To achieve the objective, a Lyapunov-based adaptive control strategy is employed to actively compensate for unknown depth measurements and the lack of an object model. The error systems are constructed as a hybrid of pixel information and reconstructed Euclidean variables obtained by comparing the images and decomposing a homographic relationship.

This paper considers the problem of position/orientation tracking control of wheeled mobile robots via visual servoing in the presence of parametric uncertainty associated with the mechanical dynamics and the camera system. Specifically, we design an adaptive controller that compensates for uncertain camera and mechanical parameters and ensures global asymptotic position/orientation tracking. Simulation and experimental results are included to illustrate the performance of the control law.