Toward Energy Autonomy in Heterogeneous Modular Plant-Inspired Robots through Artificial Evolution
| Autor: | Kasper Stoy, Frank Veenstra, Sebastian Risi, Chloe Metayer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
0301 basic medicine Self-reconfiguring modular robot Engineering Primary energy lcsh:Mechanical engineering and machinery Evolutionary algorithm Context (language use) 7. Clean energy 01 natural sciences lcsh:QA75.5-76.95 03 medical and health sciences lcsh:TJ1-1570 evolutionary algorithms business.industry Control engineering Robotics Energy consumption Modular design artificial intelligence modular robots Computer Science Applications 030104 developmental biology plant-inspired robots Robot Artificial intelligence lcsh:Electronic computers. Computer science energy autonomy business 010606 plant biology & botany |
| Zdroj: | Frontiers in Robotics and AI, Vol 4 (2017) Veenstra, F, Metayer, C, Risi, S & Stoy, K 2017, ' Toward energy Autonomy in heterogeneous Modular Plant-Inspired Robots through Artificial evolution ', Frontiers in Robotics and AI, vol. 4, 43 . https://doi.org/10.3389/frobt.2017.00043 Frontiers in Robotics and AI |
| ISSN: | 2296-9144 |
| Popis: | Contemporary robots perform energy intensive tasks -- e.g. manipulation and locomotion -- making the development of energy autonomous robots challenging. Since plants are primary energy producers in natural ecosystems, we took plants as a source of inspiration for designing our robotics platform. This led us to investigate energy autonomy in robots through employing solar panels. As plants move slowly compared to other large terrestrial organisms, it is expected that plant-inspired robots can enable robotic applications such as long-term monitoring and exploration where energy consumption could be minimized. Since it is difficult to manually design robotic systems that adheres to full energy autonomy, we utilize evolutionary algorithms to automate the design and evaluation of energy harvesting robots. We demonstrate how artificial evolution can lead to the design and control of a modular plant-like robot. Robotic phenotypes were acquired through implementing an evolutionary algorithm, a generative encoding and modular building blocks in a simulation environment. The generative encoding is based on a context sensitive Lindemayer-System (L-System) and the evolutionary algorithm is used to optimize compositions of heterogeneous modular building blocks in the simulation environment. Phenotypes that evolved from the simulation environment are in turn transferred to a physical robot platform. The robotics platform consists of five different types of modules: (1) a base module; (2) a cube module; (3) servo modules; and (4,5) two types of solar panel modules that are used to harvest energy. The control system for the platform is initially evolved in the simulation environment and afterwards transferred to an actual physical robot. A few experiments were done showing the relationship between energy cost and the amount of light tracking that evolved in the simulation. The reconfigurable modular robots are eventually used to harvest light with the possibility to be reconfigured based on the needs of the designer, the type of usable modules and/or the optimal configuration derived from the simulation environment. Long term energy autonomy has not been tested in this robotics platform. However, we think our robotics platform can serve as a stepping stone towards full energy autonomy in modular robots. |
| Databáze: | OpenAIRE |
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