Abstract-- In a companion paper, we present a modular framework for unicycle stabilization in polar coordinates that provides smooth steering laws through backstepping. Surprisingly, the same problem also allows application of integrator forwarding. In this work, we leverage this feature and construct new smooth steering laws together with control Lyapunov functions (CLFs), expanding the set of CLFs available for inverse optimal control design. In the case of constant forward velocity (Dubins car), backstepping produces finite-time (deadbeat) parking, and we show that integrator forwarding yields the very same class of solutions. This reveals a fundamental connection between backstepping and forwarding in addressing both the unicycle and, the Dubins car parking problems.

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models.

The centimeter-scale cyborg insects have a potential advantage for application in narrow environments where humans cannot operate. To realize such tasks, researchers have developed a small printed-circuit-board (PCB) which an insect can carry and control it. The electronic components usually remain bare on the board and the whole board is mounted on platform animals, resulting in uneven morphology of whole cyborg with sharp edges. It is well known that streamlined body shape in artificial vehicles or robots contributes to effective locomotion by reducing drag force in media. However, little is known how the entire body shape impacts on locomotor performance of cyborg insect. Here, we developed a 10 mm by 10 mm board which provided electrical stimulation via Sub-GHz communication and investigated the impact of physical arrangement of the board using Madagascar hissing cockroach. We compared the success rate of gap negotiation between the cyborg with mounted board and implanted board and found the latter outperformed the former. We demonstrated our cyborg cockroach with implanted board could follow faithfully to the locomotion command via antennal or cercal stimulation and traverse a narrow gap like air vent cover. In contrast to the conventional arrangement, our cyborg insects are suitable for application in a concealed environment.

Over the years, complex control approaches have been developed to control the motion of a bicycle. Reinforcement Learning (RL), a branch of machine learning, promises easy deployment of so-called agents. Deployed agents are increasingly considered as an alternative to controllers for mechanical systems. The present work introduces an RL approach to do path following with a virtual bicycle model while simultaneously stabilising it laterally. The bicycle, modelled as the Whipple benchmark model and using multibody system dynamics, has no stabilisation aids. The agent succeeds in both path following and stabilisation of the bicycle model exclusively by outputting steering angles, which are converted into steering torques via a PD controller. Curriculum learning is applied as a state-of-the-art training strategy. Different settings for the implemented RL framework are investigated and compared to each other. The performance of the deployed agents is evaluated using different types of paths and measurements. The ability of the deployed agents to do path following and stabilisation of the bicycle model travelling between 2m/s and 7m/s along complex paths including full circles, slalom manoeuvres, and lane changes is demonstrated. Explanatory methods for machine learning are used to analyse the functionality of a deployed agent and link the introduced RL approach with research in the field of bicycle dynamics.

We demonstrate the capabilities of an attention-based end-to-end approach for high-speed quadrotor obstacle avoidance in dense, cluttered environments, with comparison to various state-of-the-art architectures. Quadrotor unmanned aerial vehicles (UAVs) have tremendous maneuverability when flown fast; however, as flight speed increases, traditional vision-based navigation via independent mapping, planning, and control modules breaks down due to increased sensor noise, compounding errors, and increased processing latency. Thus, learning-based, end-to-end planning and control networks have shown to be effective for online control of these fast robots through cluttered environments. We train and compare convolutional, U-Net, and recurrent architectures against vision transformer models for depth-based end-to-end control, in a photorealistic, high-physics-fidelity simulator as well as in hardware, and observe that the attention-based models are more effective as quadrotor speeds increase, while recurrent models with many layers provide smoother commands at lower speeds. To the best of our knowledge, this is the first work to utilize vision transformers for end-to-end vision-based quadrotor control.

Reliable offroad autonomy requires low-latency, high-accuracy state estimates of pose as well as velocity, which remain viable throughout environments with sub-optimal operating conditions for the utilized perception modalities. As state estimation remains a single point of failure system in the majority of aspiring autonomous systems, failing to address the environmental degradation the perception sensors could potentially experience given the operating conditions, can be a mission-critical shortcoming. In this work, a method for integration of radar velocity information in a LiDAR-inertial odometry solution is proposed, enabling consistent estimation performance even with degraded LiDAR-inertial odometry. The proposed method utilizes the direct velocity-measuring capabilities of an Frequency Modulated Continuous Wave (FMCW) radar sensor to enhance the LiDAR-inertial smoother solution onboard the vehicle through integration of the forward velocity measurement into the graph-based smoother. This leads to increased robustness in the overall estimation solution, even in the absence of LiDAR data. This method was validated by hardware experiments conducted onboard an all-terrain vehicle traveling at high speed, ~12 m/s, in demanding offroad environments.

Abstract: Flagellated microorganisms can swim at low Reynolds numbers and adapt to changes in their environment. Specifically, the flagella can switch their shapes or modes through gene expression. In the past decade, efforts have been made to fabricate and investigate rigid types of microrobots without any adaptation to the environments. More recently, obtaining adaptive microrobots mimicking real microorganisms is getting more attention. However, even though some adaptive microrobots achieved by hydrogels have emerged, the swimming behaviors of the microrobots before and after the environment-induced deformations are not predicted in a systematic standardized way. In this work, experiments, finite element analysis, and dynamic modeling are presented together to realize a complete understanding of these adaptive microrobots. The above three parts are cross-verified proving the success of using such methods, facilitating the bio-applications with shape-programmable and even swimming performance-programmable microrobots. Moreover, an application of targeted object delivery using the proposed microrobot has been successfully demonstrated. Finally, cytotoxicity tests are performed to prove the potential for using the proposed microrobot for biomedical applications. One-Sentence Summary: A systematic approach to design shape-programable, dual-function, and adaptive microrobots for biomedical applications. Main Text: INTRODUCTION Microorganisms are capable of swimming with flagella to provide motility (1-3). These microorganisms can adapt their flagella into different shapes or modes by altering gene expression to accommodate environmental changes or for other proposes like nutrition, hosting, and invasion (4). For example, the flagella of a spermatozoon of Echinus esculentus will result in a transition from a planar to a helical shape when the viscosity is increased and back to a quasi-planar shape when it is further increased (5). Moreover, recent investigations show that the flagella can deform to wrap around the cell body to escape from traps or to enhance the efficiency of environmental exploration (6, 7). Inspired by these natural living beings, many microrobots have been fabricated to swim in this microscale world. The two strategies most adopted to achieve motility are the helical structures mimicking the flagella of bacterial E. coli and the flexible body replicating the motion of a spermatozoa (8). In the last decade, various helical-type microrobots are realized with fixed shapes, i.e., the structure will not change once it is fabricated (9-11).

In this work, we present an experimental setup and guide to enable the perching of large flapping-wing robots. The combination of forward flight, limited payload, and flight oscillations imposes challenging conditions for localized perching. The described method details the different operations that are concurrently performed within the 4 second perching flight. We validate this experiment with a 700 g ornithopter and demonstrate the first autonomous perching flight of a flapping-wing robot on a branch. This work paves the way towards the application of flapping-wing robots for long-range missions, bird observation, manipulation, and outdoor flight.

Contrary to the stunning feats observed in birds of prey, aerial manipulation and grasping with flying robots still lack versatility and agility. Conventional approaches using rigid manipulators require precise positioning and are subject to large reaction forces at grasp, which limit performance at high speeds. The few reported examples of aggressive aerial grasping rely on motion capture systems, or fail to generalize across environments and grasp targets. We describe the first example of a soft aerial manipulator equipped with a fully onboard perception pipeline, capable of robustly localizing and grasping visually and morphologically varied objects. The proposed system features a novel passively closing tendon-actuated soft gripper that enables fast closure at grasp, while compensating for position errors, complying to the target-object morphology, and dampening reaction forces. The system includes an onboard perception pipeline that combines a neural-network-based semantic keypoint detector with a state-of-the-art robust 3D object pose estimator, whose estimate is further refined using a fixed-lag smoother. The resulting pose estimate is passed to a minimum-snap trajectory planner, tracked by an adaptive controller that fully compensates for the added mass of the grasped object. Finally, a finite-element-based controller determines optimal gripper configurations for grasping. Rigorous experiments confirm that our approach enables dynamic, aggressive, and versatile grasping. We demonstrate fully onboard vision-based grasps of a variety of objects, in both indoor and outdoor environments, and up to speeds of 2.0 m/s -- the fastest vision-based grasp reported in the literature. Finally, we take a major step in expanding the utility of our platform beyond stationary targets, by demonstrating motion-capture-based grasps of targets moving up to 0.3 m/s, with relative speeds up to 1.5 m/s.

This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a Vision-Based Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative to given footholds, but the body pose that maximizes the chances of the legs in reaching safe footholds. ViTAL plans footholds and poses based on skills that characterize the robot's capabilities and its terrain-awareness. We use the 90 kg HyQ and 140 kg HyQReal quadruped robots to validate ViTAL, and show that they are able to climb various obstacles including stairs, gaps, and rough terrains at different speeds and gaits. We compare ViTAL with a baseline strategy that selects the robot pose based on given selected footholds, and show that ViTAL outperforms the baseline.