Open simulation tool models drone with soft robotic arm attached
An Open MuJoCo Simulation Framework for a Quadrotor Carrying a Tendon-Driven Soft Continuum Arm
Robotics
Summary
Flying robots with soft, flexible arms could safely interact with delicate objects or people, but their combined movements are hard to predict. The authors built a computer simulation that models a drone carrying a bendy, tendon-controlled arm to better understand how they move together. They tested the simulation with different flight paths and arm movements, showing the drone can follow commands while the soft arm swings realistically. This tool can help designers create better flying robots with soft arms without needing physical prototypes.
What this means in practice
- •For robotics engineers: Test and develop flying robots with soft arms using an open-source simulation that models coupled dynamics and tendon actuation.
- •For drone developers: Simulate multimodal control strategies quickly to improve and tune drone flight stability when carrying flexible payloads.
Authors
Haitham El-Hussieny
Abstract
Combining aerial robots with soft continuum manipulators promises safe, compliant interaction with the environment, but the coupled dynamics of a free-flying base and a highly deformable, underactuated arm are difficult to study without a suitable simulator. This paper describes an open-source simulation framework, built on the MuJoCo physics engine, for a quadrotor that carries a tendon-driven soft continuum arm underneath its body. The arm is modelled as a pseudo-rigid-body chain of twelve segments joined by pairs of orthogonal compliant hinges. Three tendons, spaced \SI{120}{\degree} apart and routed through every segment, bend it in any direction. A geometric tracking controller on $SE(3)$ flies the vehicle; its rotor allocation matrix is generated directly from the model description. A closed-form tendon-tension allocation keeps every tension within its limits and gives the same bending moment in every direction. We report the full set of modelling choices and parameters, and we evaluate the framework in four simulated scenarios: a \SI{36}{\second} mission with a figure-eight and a tendon-driven tip sweep, an attitude-gain study, a quasi-static characterization of the bending workspace, and an impulsive disturbance applied to the arm tip. With the nominal gains the vehicle tracks the mission with \SI{3.1}{\centi\meter} RMS position error while the arm tip swings up to \SI{37}{\centi\meter} from the body axis. The gain study shows that a low-bandwidth attitude loop lets the arm excite sustained oscillations. The simulation runs about $15\times$ faster than real time on a laptop CPU. All models, controllers and scripts that reproduce the figures are released.