Multi-Robot Systems (MRS) are increasingly de-ployed for hazardous tasks in urban environments. Among many tasks, search and rescue remains challenging as it deals with exploration in an unknown indoor constrained environ-ment. For example, without global knowledge of the map of a building floor, it is not advantageous to choose one path over another at a corridor junction. Also, if the assigned frontiers are far from the robot, backtracking along a corridor will cost more than moving forward. Since exploration along corridors is similar to solving a maze, this paper examines classical maze-solving algorithms that are known to be computationally fast and lightweight, such as the Right Hand Rule (RHR), Random Mouse (RM), and more. The authors have identified two gaps that need to be addressed before these algorithms can be applied to physical MRS. Firstly, these algorithms are not designed for the cooperation of multiple agents in exploration. Secondly, they are often applied to only a low-fidelity simulation environment, which requires some work to make these algorithms transferable to work in the commonly used occupancy grid map environment. In this paper, the authors introduced RACE, a fast and lightweight collective urban exploration and search algorithm based on a modified and condensed version of the Ant Colony Optimization (ACO) algorithm. The proposed solution is successfully verified in a low-fidelity simulation, evaluated against other exploration and search algorithms like RHR and RM. An innovative approach of RACE Simulation to Physical implementation is presented and a physical system evaluation is performed to evaluate RACE against a Rapidly-Exploring Random Tree algorithm. Finally, the proposed solution is further verified with a physical experiment, in which a quadrupedal robot is assigned to explore part of a floor of SUTD, spanning approximately$(55m \times 40m)$. RACE also showed potential in handling challenging closed-loop and dead-end environments.

How does a wheeled robot move and turn? The answer is straightforward for a conventional wheeled robot, but it is not so easy for a robot with a discrete wheel design. Regular wheeled robots always have four contact points, resulting in static stability during locomotion. However, QuadRunner's novel leg mechanism provides only a semi-circular wheel shape, and proper gait planning is needed to go straight or turn. Therefore, this paper presents a dual frequency gait planning method which controls the robot's gait cycle's duty factor and generates unique turning gait patterns for wheel locomotion. Describing requirements and limitations, we found sets of solutions that can achieve turning. Results show that the smallest turning radius QuadRunner achieved is 1.05m, and the biggest is 1.86m. In addition, detailed experiments were made to observe the performance and stability of straight and turning wheel behaviors. Finally, a gait verification is made using high-speed cameras.

This paper presents QuadRunner, a transformable quasi-wheel legged robot that achieves both quadruped locomotion and wheel locomotion by exploiting a novel semicircular leg-wheel design with a Trotting Wheel gait. We built upon the Stanford Doggo open architecture platform and integrated it with a transformable leg-wheel design to enhance its locomotion capabilities. On its gait control, improvements were made to its trot gait kinematics with end-effector considerations as well as the design of a new trotting wheel gait. Our proposed locomotion strategy found that the robot's legged locomotion improves by an average speed of 10%. In addition, wheel to leg transition takes around 300 ms, and the speed of its wheel locomotion can reach more than five times its body-length/s (2.2 m/s) on flat terrain. Lastly, detailed experiments are conducted to observe the wheel-leg transition performance and gait verification under the absence of foot contact sensors.

Large scale aerial deployment of miniature sensors in tough environmental conditions requires a deployment device that is lightweight, robust, and steerable. We present a novel samara-inspired autorotating craft that is capable of two flight modes (autorotating mode and diving mode) with an average glide angle of 28.9$^{\circ }$(1.81 m lateral distance per 1 m loss of altitude) in the former mode. The bidirectional transition between the two modes and directional control is achieved by using only a single actuator. Also, in order to minimize its glide angle, a design optimization methodology is presented for our prototype, diving samara autorotating wing, along with a new cyclic control strategy for directional control of autorotating descent. The dynamic model, simulated in a six degrees-of-freedom environment using the blade element theory, is integrated with genetic algorithm to derive parameters for the wing geometry, flap angle for autorotation, and the proposed cyclic control. The physical prototype autorotates at a descent velocity of 1.43 m/s and rotation speed 4.17 Hz, and is able to transit to diving mode in an average duration of 272 ms to increase its descent velocity by at least 17.6 times. At any point during the dive, it is able to transit back into autorotation in an average duration of 327 ms. Semioutdoor experiments were used to investigate the bidirectional transitions and verify the glide angle (28.9$^{\circ }$), which is much improved from the previous prototype (SAW+, 58.4$^{\circ }$). Lastly, as a demonstration of a real-life deployment scenario and environmental conditions, the prototypes were dropped from a fixed-wing unmanned aerial vehicle at a suburban test site.

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.

Pose estimation is of paramount importance for flight control as well as localization and navigation of Unmanned Aerial Vehicles (UAVs) to enable autonomous operations. In environments without GPS, such estimation can only be determined using onboard sensors; optical flow using a monocular camera is a popular approach. Monocopters are a class of nature inspired UAVs known as free rotors where their design and flight dynamics are inspired by the falling samara seed. With a constantly rotating body frame, free rotors introduces some unique challenges for visual perception required during optical flow sensing. This paper addresses these problems with the introduction of SHIFT (Selective Heading Image for Translation) that selects optimal images for determining translation with optical flow. It achieves this by decoupling rotation vectors about the optical axis from translation vectors in a flow field through the separate tracking of orientation and position using an Unscented Kalman Filter with phase correlation in the log-polar and spatial domain. The experiments show that SHIFT's estimation in orientation is stable even under sinusoidal excitation with a median absolute percentage errors of less than 1%. It is able to track position and orientation of a UAV accurately.

In this paper, the high angular rate estimation for simultaneous localisation and mapping (SLAM) of a Flying Li-DAR (Flydar) is presented. The proposed EKF-based algorithm exploits the sinusoidal magnetometer measurement generated by the continuously rotating airframe for estimation of the robot hovering angular velocity. Significantly, the proposed method does not rely on additional sensors other than existing IMU sensors already being used for flight stabilization. The gyro measurement and the gyro bias are incorporated as a control input and a filter state respectively to enable estimation even under gyro saturation condition. Additionally, this work proposes leveraging on the inherently rotating locomotion to generate a planar lidar scan using only a single-point laser for possible lightweight autonomy. The proposed estimation method was experimentally evaluated on a ground rotating rig up to twice the gyro saturation limit with an effective rms error of 0.0045Hz; and on the proposed aerial platform - Flydar - hovering beyond the saturation limit with a rms error of 0.0056Hz. Lastly, the proposed method for SLAM using the rotating dynamics of Flydar was demonstrated with a localisation accuracy of 0.11m.

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.

In this work, reinforcement learning is used to develop a position controller for an underactuated nature-inspired Unmanned Aerial Vehicle (UAV). This particular configuration of UAVs achieves lift by spinning its entire body contrary to standard multi-rotors or fixed-wing aircraft. Deep Deterministic Policy Gradients (DDPG) with Ape-X Distributed Prioritized Experience Replay was used to train neural network function approximators that were implemented as the final control policy. The reinforcement learning agent was trained in simulations and directly ported over to real-life hardware. Position control tests were performed on the learned control policy and compared to a baseline PID controller. The learned controller was found to exhibit better control over the inherent oscillations that arise from the non-linear dynamics of the platform.

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.

This paper presents trajectory following control experiments of a miniature spherical rolling and spinning robot mechanism on three different types of outdoor surfaces. The research is inspired from the efficient locomotory rolling patterns of various insects in unstructured environment. A nonlinear adaptive sliding mode (ASMC) feedback method maintains the robot stability and robustness in the presence of parameter uncertainties and external disturbances. The proposed trajectory following control policy is developed, implemented and tested for the miniature spherical robot on three different types of irregular surfaces in outdoors. Trajectory following accuracy, roll angle stability and wheel velocity response are three parameters measured to evaluate robot performance. ASMC controller is compared with an integral sliding (ISMC) controller. Experimental results show that proposed control policy is able to manage an accurate trajectory following amidst robust control of a rolling and spinning robot on three types of irregular surface in practical outdoor conditions.

The seeds of Maple trees (Samara) use autorotation as a unique mechanism to disperse their seeds. By exploiting gyroscopic stability of a spinning wing, the Samara is able to cover large horizontal distance despite having no form of propulsion. We applied and adapted this natural ability in our novel concept, the Samara Autorotating Wings (SAW), and extended its stability and direction controllability by generalizing the mechanism to incorporate designs with more than 1 wing. By conceiving cyclic control, the translational motion of autorotation is regulated. A nonlinear model of SAW with $n$ wings is derived and control schemes developed to control the translational position during autorotation. Numerical simulations were performed to investigate the performance of the multi-wing SAW prototypes to track a conical spiral autorotation trajectory. Direct experiments were conducted in a vertical wind-tunnel through a special ball joint that allows z-axis translation and all three rotational degrees of freedom. Finally, free-fall drop tests are used to verify the directional controllability and performance of SAW.

This paper presents a novel caterpillar inspired rolling gait generation and control mechanism of a spherical robot. The research investigates efficient locomotory rolling gaits of Pleurotya caterpillar in unstructured environment. A similar rhythmic rolling pattern is produced for the spherical robot locomotion. A synergetically combined feedforward - feedback control strategy is further proposed. The feedforward component is generated from centrally connected pattern generators (CPGs)in conjunction with nonlinear robot dynamics. A nonlinear integral sliding mode (ISMC) feedback method regulates these rhythmic patterns to adjust robot stability and robustness in the presence of parameter uncertainties and external disturbances. The proposed control strategy is developed, implemented and tested for the spherical robot on both smooth and irregular surfaces. The robot performance is quantified by measuring the stability in roll angle and wheel velocities. Experimental results show that proposed novel strategy is efficient in producing a stable rolling gait and robust control of a spherical robot on different types of surface conditions.

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.

This paper describes the Transformable HOvering Rotorcraft (THOR), a prototype Unmanned Aerial Vehicle (UAV) that explores a novel approach in combining the range and speed of a horizontal flying platform with the hovering and maneuverability of a rotor-wing. This is achieved by integrating a tailless flying wing configuration with a single-axis rotor, or monocopter. By maintaining full utilization of all aerodynamic surfaces and propulsion sources in both flight modes, this method represents the most structurally efficient approach to achieving a cruising mode and a hovering mode on the same frame. Using a dual servo and motor configuration, we propose an under-actuated system that is able to achieve controllability in 4 degrees of freedom while in its horizontal cruising mode and in 5 degrees of freedom while in its hovering mode. In both indoor and outdoor experiments, the UAV is able to transition between either flight modes seamlessly and repeatedly without the need for any additional mechanisms and actuators.

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 describes the design and evaluation of a Legged Piezoelectric Miniature Robot (LPMR) propelled by standing wave vibrations at the legs, which are biologically inspired by the bounding gait locomotion of animals. The LPMR comprises of a single piezoelectric patch, a metal beam, two contact joints, two rigid legs and is able to achieve tether-less remote controlled operation. Through analysis of the bending modes of vibrations and driving frequency, a forward and backward motion of the underactuated system is achieved by choosing specific positions for the legs. At 100 V amplitude, the LPMR with the weight of 6.27 g, length of 50 mm, width of 10 mm and height of 1.5 mm achieves a maximum linear speed of 246.5 mm/s for forward motion and 302 mm/s for backward motion. The LPMR is also able to carry a payload of 100 g at a speed of 49.6 mm/s for forward motion and 87.9 mm/s for backward motion when applying 100 V amplitude. The corresponding maximum force generated by the LPMR is 9.8 mN during forward motion and 12 mN during backward motion at the same applied voltage. An experimental characterization for the LPMR in terms of speed versus applied voltage, speed versus embedded mass and blocking force for different applied voltages is explored and evaluated in this study.

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.

In this paper, a miniature nonholonomic spherical rolling robot capable of navigating over two dimensional surfaces is described. This 55 gram spherical robot consists of a 6 cm diameter external spherical shell driven by an internal two-wheeled differential drive cart. A gravity powered pendulum effect is produced as the internal device climbs up the internal surface of the shell, propelling the robot forward up to a speed of 0.16m/s. We derived its dynamic model using Lagrangian, and studied its dynamics and performance under various applied torques. The spherical robot is built and its overall mechanical, hardware and control architecture are elaborated. Experiments are conducted to evaluate the robot open and closed loop performance on a linear trajectory, captured using an optical motion capture system. We showed that with our implemented PD controller, the robot can follow the desired orientation.