There are an increasing number of applications that require the end-effector of a robotic manipulator to be compliant in response to the intended force exerted by a human operator. In physical human-robot interaction, the stability and compliance are often affected in the presence of both human and an environment, especially during the transition from noncontact to contact. Unlike previous works, the physical human-robot interaction in this letter takes into consideration of the interaction of the human, robot, and a moving environment. To achieve this, this letter proposes a variable admittance control strategy with an adaptive controller to produce a time-varying stiffness interacting with the human and a moving environment simultaneously. The passivity of the system is guaranteed for time-varying admittance with human interaction coupled with a moving environment. Experiments are conducted to demonstrate the capability of the controller.

In this paper, we developed a kinematic model for a legged piezoelectric miniature robot driven by standing wave. The robot consists of a piezoelectric bending actuator with rigidly attached legs. The kinematic model allows us to compute the velocity analytically so that the robot motion can be predicted. To construct the kinematic model, the behavior of the robot at the contact instances with the ground during the up and down stages is studied. A prototype is fabricated so as to verify our kinematic model experimentally, measured using a motion capture system. The result shows that the measured speed was in close agreement with what was predicted by our model.

This paper introduces an approach to fabricating lightweight, untethered soft robots capable of diverse biomimetic locomotion. Untethering soft robotics from electrical or pneumatic power remains one of the prominent challenges within the field. The development of functional untethered soft robotic systems hinges heavily on mitigating their weight; however, the conventional weight of pneumatic network actuators (pneu-nets) in soft robots has hindered untethered operations. To address this challenge, we developed film-balloon (FiBa) modules that drastically reduced the weight of soft actuators. FiBa modules combine transversely curved polymer thin films and three-dimensionally printed pneumatic balloons to achieve varied locomotion modes. These lightweight FiBa modules serve as building blocks to create untethered soft robots mimicking natural movement strategies. These modules substantially reduce overall robot weight, allowing the integration of components such as pumps, valves, batteries, and control boards, thereby enabling untethered operation. FiBa modules integrated with electronic components demonstrated four bioinspired modes of locomotion, including turtle-inspired crawling, inchworm-inspired climbing, bat-inspired perching, and ladybug-inspired flying. Overall, our study offers an alternative tool for designing and customizing lightweight, untethered soft robots with advanced functionalities. The reduction of the weight of soft robots enabled by our approach opens doors to a wide range of applications, including disaster relief, space exploration, remote sensing, and search and rescue operations, where lightweight, untethered soft robotic systems are essential.

We present a platform to allow up to 50000 students to simultaneously collect and learn from their personal activity, transportation, and environmental data. The main goals that we met during the design of our sensor platform were to: be low cost; remain powered for the duration of the data collection campaign; robustly sense a wide range of environmental parameters; and be packaged in a form factor conducive to wide-spread adoption and ease of use. We describe and generalize the design methods we applied on the hardware and firmware. Our sensors employ Wi-Fi communication to move data as well as to localize themselves using a radio-map of Singapore. Our system uses embedded as well as server-based machine learning algorithms to perform on-sensor transportation mode identification and state inference. The testing and validation methods that we applied ensured that over 98% of the deployed sensors successfully met all of their design goals. In addition, we summarize the results of a large-scale deployment of our system for a nation-wide experiment in Singapore in 2015, and describe three sample applications of the collected data. We publish sample data sets and algorithm code for researchers to analyze.

Robotic Wire Arc Additive Manufacturing (WAAM) utilizes a robot arm as a motion system to build 3D metallic objects by depositing weld beads one above the other in a layer by layer fashion. A key part of this approach is the process study and control of Multi-Layer Multi-Bead (MLMB) deposition, which is very sensitive to process parameters and prone to error stacking. Despite its importance, it has been receiving less attention than its single bead counterpart in literature, probably due to the higher experimental overhead and complexity of modeling. To address these challenges, this paper proposes an integrated learning-correction framework, adapted from Model-Based Reinforcement Learning, to iteratively learn the direct effect of process parameters on MLMB print while simultaneously correct for any inter-layer geometric digression such that the final output is still satisfactory. The advantage is that this learning architecture can be used in conjunction with actual parts printing (hence, in-situ study), thus minimizing the required training time and material wastage. The proposed learning framework is implemented on an actual robotic WAAM system and experimentally evaluated.