Multi-environment robotic transitions through adaptive morphogenesis

The current proliferation of mobile robots spans ecological monitoring, warehouse management and extreme environment exploration, to an individual consumer’s home1–4. This expanding frontier of applications requires robots to transit multiple environments, a substantial challenge that traditional robot design strategies have not effectively addressed5,6. For example, biomimetic design—copying an animal’s morphology, propulsion mechanism and gait—constitutes one approach, but it loses the benefits of engineered materials and mechanisms that can be exploited to surpass animal performance7,8. Other approaches add a unique propulsive mechanism for each environment to the same robot body, which can result in energy-inefficient designs9–11. Overall, predominant robot design strategies favour immutable structures and behaviours, resulting in systems incapable of specializing across environments12,13. Here, to achieve specialized multi-environment locomotion through terrestrial, aquatic and the in-between transition zones, we implemented ‘adaptive morphogenesis’, a design strategy in which adaptive robot morphology and behaviours are realized through unified structural and actuation systems. Taking inspiration from terrestrial and aquatic turtles, we built a robot that fuses traditional rigid components and soft materials to radically augment the shape of its limbs and shift its gaits for multi-environment locomotion. The interplay of gait, limb shape and the environmental medium revealed vital parameters that govern the robot’s cost of transport. The results attest that adaptive morphogenesis is a powerful method to enhance the efficiency of mobile robots encountering unstructured, changing environments. A design strategy termed ‘adaptive morphogenesis’ enables a robot inspired by aquatic and terrestrial turtles to adapt its limb morphology and gait to specialize for locomotion in different environments.

» Publication Date: 12/10/2022

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737


                   




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