A growing soft robot with climbing plant–inspired adaptive behaviors for navigation in unstructured environments

Abstract

Self-growing robots are an emerging solution in soft robotics for navigating, exploring, and colonizing unstructured environments. However, their ability to grow and move in heterogeneous three-dimensional (3D) spaces, comparable with real-world conditions, is still developing. We present an autonomous growing robot that draws inspiration from the behavioral adaptive strategies of climbing plants to navigate unstructured environments. The robot mimics climbing plants’ apical shoot to sense and coordinate additive adaptive growth via an embedded additive manufacturing mechanism and a sensorized tip. Growth orientation, comparable with tropisms in real plants, is dictated by external stimuli, including gravity, light, and shade. These are incorporated within a vector field method to implement the preferred adaptive behavior for a given environment and task, such as growth toward light and/or against gravity. We demonstrate the robot’s ability to navigate through growth in relation to voids, potential supports, and thoroughfares in otherwise complex habitats. Adaptive twining around vertical supports can provide an escape from mechanical stress due to self-support, reduce energy expenditure for construction costs, and develop an anchorage point to support further growth and crossing gaps. The robot adapts its material printing parameters to develop a light body and fast growth to twine on supports or a tougher body to enable self-support and cross gaps. These features, typical of climbing plants, highlight a potential for adaptive robots and their on-demand manufacturing. They are especially promising for applications in exploring, monitoring, and interacting with unstructured environments or in the autonomous construction of complex infrastructures.