There has been an increasing demand to automate the non-patient care matters so that the clinical staff can focus on delivering patient care. For example, out-patients undergoing chemotherapy increases their toilet usage frequency due to the treatment. As they are undergoing chemotherapy, their output waste contains a level of chemical. This task is compulsory yet troublesome and time-consuming so it is often desired to be removed from the nursing staff for them to focus on patient care. Hence, in this paper, we propose a manipulator system to automatically dispose the bedpan used by patients undergoing chemotherapy. The main technical challenge lies in the removal of the bedpan from the commode as the interaction of the grasping is highly dynamic, along with the different conditions of the bedpans. To address this manipulation issue, a Residual Reinforcement Learning (RRL) method that leverages vision-based commode pose estimation and the reinforcement learning (RL)-based uncertainty compensation for improvement of the grasping accuracy is proposed to increase the robustness of the disposal. The experiments conducted show that the manipulator can dispose the bedpan without human intervention and the proposed method achieves a 100 % success rate while the traditional method without RL is only 50 %.

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.

Plane-to-plane transitioning has been a significant challenge for climbing robots. To accomplish this, additional actuator or robot module is usually required which significantly increases both size and weight of the robot. This paper presents a two-wheg miniature climbing robot with a novel passive vertical tail component which results in robust transitioning capabilities. The design decision was derived from an indepth force analysis of the climbing robot while performing the transition. The theoretical analysis is verified through a working prototype with robust transitioning capabilities whose performance follows closely the analytical prediction. The climbing robot is able to climb any slope angles, 4-way internal transitions, and 4-way external transitions. This work contributes to the understanding and advancement of the transitioning capabilities and the design of a simple climbing robot, which expands the possibilities of scaling down miniature climbing robot further.

Development of mesoscale robots is gaining interest in security and surveillance domains due to their stealth and portable nature in achieving tasks. Their design and development require a host of hardware, controls, and behavioral innovations to yield fast, energy-efficient, distributed, adaptive, robust, and scalable systems. We extensively describe one such design and development process by: (1) the genealogy of our embedded platforms; (2) the key system architecture and functional layout; (3) the developed and implemented design principles for mesoscale robotic systems; (4) the various key algorithms developed for effective collective operations of mesoscale robotic swarms, with applications to urban sensing and mapping. This study includes our perception of the embedded hardware requirements for reliable operations of mesoscale robotic swarms and our description of the key innovations made in magnetic sensing, indoor localization, central pattern generator control, and distributed autonomy. Although some elements of the design process of such a complex robotic system are inevitably ad-hoc, we focus on the system-of-systems design process and the component design integration. This system-of-systems process provides a basis for developing future systems in the field, and the designs represent the state-of-the-art development that may be benchmarked against and adapted to other applications.

This paper presents the design and fabrication of ORION-II for autonomous Intelligence, Surveillance and Reconnaissance (ISR). ORION-II is a miniature climbing robot equipped with all the necessary electronic components to achieve ISR tasks. It consists of a robot chassis (tail) carrying the electronics and two DC motors each driving a wheel-leg (wheg) with four “flaps” equipped with bilayer compliant tapes. Two types of tapes are used for attachment of ORION-II: bilayer PDMS/foam and bilayer micro-suction/foam. The two types of tapes are tested on different climbing surfaces, and the climbing performance is reported. ORION-II could climb rougher surfaces when using the PDMS/foam tape, and perform internal climbing transitions when using the micro-suction/foam tape. The total weight of ORION-II is 153.18 g as compared with 71.5 g of our previous version ORION-I.

In small mobile robots, decreasing the number of actuators is usually desirable to reduce the size and weight of the robot, but it is usually at the expense of the robot's degree of freedom (DOF). This work presents the development and preliminary experimental testing of a novel Legged Piezoelectric Miniature Robot (LPMR) driven only by a single piezoelectric unimorph actuator and yet fully capable of being maneuvered to move forward, turn right, or turn left. The underactuated motion is achieved by exploiting the bending vibration modes disparity of the piezoelectric actuator at different driving frequencies and designing specific positions of the robot's legs to generate a differential-drive-like mechanism. The speed of the robot can be controlled through regulating the magnitude of the applied voltage. The proposed underactuated system is experimentally verified and a preliminary characterization of the LPMR in terms of its forward and turning speed versus applied voltage and payload is investigated and reported.

Automated welding has been very effective in enhancing the quality and quantity of weld jobs along with improving the safety of the workers. Nevertheless, most existing welding robots are massive and immobile. Typically, the work-pieces are transferred to the robots for welding. This makes it difficult for many welding applications that have large scale structures e.g. shipbuilding, construction, on-site repair work, etc. In these cases, the welding robot should be able to be transported to the work-pieces. The purpose of this paper is to explore and study various recent developments in portable welding robot designs. Based on this, several strategies of designing portable welding robot are classified and discussed.