Quadrotor Morpho-Transition, or the act of transitioning from air to ground through mid-air transformation, involves complex aerodynamic interactions and a need to operate near actuator saturation, complicating controller design. In recent work, morpho-transition has been studied from a model-based control perspective, but these approaches remain limited due to unmodeled dynamics and the requirement for planning through contacts. Here, we train an end-to-end Reinforcement Learning (RL) controller to learn a morpho-transition policy and demonstrate successful transfer to hardware. We find that the RL control policy achieves agile landing, but only transfers to hardware if motor dynamics and observation delays are taken into account. On the other hand, a baseline MPC controller transfers out-of-the-box without knowledge of the actuator dynamics and delays, at the cost of reduced recovery from disturbances in the event of unknown actuator failures. Our work opens the way for more robust control of agile in-flight quadrotor maneuvers that require mid-air transformation. Video; Code.

Multi-modal ground-aerial robots have been extensively studied, with a significant challenge lying in the integration of conflicting requirements across different modes of operation. The Husky robot family, developed at North-eastern University, and specifically the Husky v.2 discussed in this study, addresses this challenge by incorporating posture manipulation and thrust vectoring into multi-modal locomotion through structure repurposing. This quadrupedal robot features leg structures that can be repurposed for dynamic legged locomotion and flight. In this paper, we present the hardware design of the robot and report primary results on dynamic quadrupedal legged locomotion and hovering.

Robot designs can take many inspirations from nature, where there are many examples of highly resilient and fault-tolerant locomotion strategies to navigate complex terrains by using multi-functional appendages. For example, Chukar and Hoatzin birds can repurpose their wings for quadrupedal walking and wing-assisted incline running (WAIR) to climb steep surfaces. We took inspiration from nature and designed a morphing robot with multi-functional thruster-wheel appendages that allows the robot to change its mode of locomotion by transforming into a rover, quad-rotor, mobile inverted pendulum (MIP), and other modes. In this work, we derive a dynamic model and formulate a nonlinear model predictive controller to perform WAIR to showcase the unique capabilities of our robot. We implemented the model and controller in a numerical simulation and experiments to show their feasibility and the capabilities of our transforming multimodal robot.

Investigations of unmanned aerial vehicles (UAV s) for planetary exploration and payload manipulation have become a strong focus of research within space robotics. Among possible solutions, balloon-based systems possess merits that make them extremely attractive, such as their simple operation mechanism and endured operation time. However, there are many hurdles to overcome to achieve robust trajectory tracking performance for balloon-based applications. In this work, in order to facilitate the control and versatile use of balloons for near-surface planetary payload manipulation, a novel robotic platform and control strategy featuring the coordinated servoing of multiple unmanned ground vehicles (UGVs) to actuate a cable-driven balloon and the suspended payload is proposed. An earthbound prototype and dynamic model of this system are designed to allow for the investigation of payload trajectory tracking performance using a tailored Model Predictive Controller in simulation and experiment.

Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.