His current research interests include characterization and electrochemical modeling of Li-ion batteries, traditional and electrochemical model-based Li-ion battery management system design, and real-world applications of control and estimation theory especially in alternative and renewable energy systems, mechatronics, robotics, and electrified and autonomous transportation. Dr. Lotfi is a member of the IEEE Control Systems Society and ASME Dynamic Systems and Control Division. Mr. Kenechukwu Churchill Mbanisi, Worcester Polytechnic InstituteKenechukwu C. Mbanisi received the B.Eng. degree in electrical and electronic engineering from Covenant University, Nigeria, in 2013, and the M.S. degree in robotics engineering from Worcester Polytechnic Institute (WPI), MA, USA in 2018. He is currently working towards the Ph.D. degree in robotics engineering from WPI, USA. His research interests include human motion modeling, planning and analysis, human-robot and human-machine interaction.

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Objective To investigate the effects of human force anticipation, we conducted an experimental load-pushing task with diverse combinations of informed and actual loading weights. Background Human motor control tends to rely upon the anticipated workload to plan the force to exert, particularly in fast tasks such as pushing objects in less than 1 s. The motion and force responses in such tasks may depend on the anticipated resistive forces, based on a learning process. Method Pushing performances of 135 trials were obtained from 9 participants. We varied the workload by changing the masses from 0.2 to 5 kg. To influence anticipation, participants were shown a display of the workload that was either correct or incorrect. We collected the motion and force data, as well as electromyography (EMG) signals from the actively used muscle groups. Results Overanticipation produced overshoot performances in more than 80% of trials. Lighter actual workloads were also associated with overshoot. Pushing behaviors with heavier workloads could be classified into feedforward-dominant and feedback-dominant responses based on the timing of force, motion, and EMG responses. In addition, we found that the preceding trial condition affected the performance of the subsequent trial. Conclusion Our results show that the first peak of the pushing force increases consistently with anticipatory workload. Application This study improves our understanding of human motion control and can be applied to situations such as simulating interactions between drivers and assistive systems in intelligent vehicles.

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Mobile telepresence robots (MTRs) have become increasingly popular in the expanding world of remote work, providing new avenues for people to actively participate in activities at a distance. However, humans operating MTRs often have difficulty navigating in densely populated environments due to limited situation awareness and narrow field-of-view, which reduces user acceptance and satisfaction. Shared autonomy in navigation has been studied primarily in static environments or in situations where only one pedestrian interacts with the robot. We present a multimodal shared autonomy approach, leveraging visual and haptic guidance, to provide navigation assistance for remote operators in densely-populated environments. It uses a modified form of reciprocal velocity obstacles for generating safe control inputs while taking social proxemics constraints into account. Two different visual guidance designs, as well as haptic force rendering, were proposed to convey safe control input. We conducted a user study to compare the merits and limitations of multimodal navigation assistance to haptic or visual assistance alone on a shared navigation task. The study involved 15 participants operating a virtual telepresence robot in a virtual hall with moving pedestrians, using the different assistance modalities. We evaluated navigation performance, transparency and cooperation, as well as user preferences. Our results showed that participants preferred multimodal assistance with a visual guidance trajectory over haptic or visual modalities alone, although it had no impact on navigation performance. Additionally, we found that visual guidance trajectories conveyed a higher degree of understanding and cooperation than equivalent haptic cues in a navigation task.