Twisting for soft intelligent autonomous robot in unstructured environments

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 22; pp. 1 - 10
• Main Authors: Zhao, Yao, Chi, Yinding, Hong, Yaoye, Li, Yanbin, Yang, Shu, Yin, Jie
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 31.05.2022
• Subjects: Barriers; Confined spaces; Decision Making; Elastomers; Energy harvesting; Finite element method; Intelligence; Liquid crystals; Locomotion; Mathematical models; Physical Sciences; Robotics; Robots; Sand; Soft robotics; Substrates; Thermal energy; Twisting; autonomous soft robot; snapping; physical intelligence; unstructured environment; liquid crystal elastomer
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2200265119

Soft robots that can harvest energy from environmental resources for autonomous locomotion is highly desired; however, few are capable of adaptive navigation without human interventions. Here, we report twisting soft robots with embodied physical intelligence for adaptive, intelligent autonomous locomotion in various unstructured environments, without on-board or external controls and human interventions. The soft robots are constructed of twisted thermal-responsive liquid crystal elastomer ribbons with a straight centerline. They can harvest thermal energy from environments to roll on outdoor hard surfaces and challenging granular substrates without slip, including ascending loose sandy slopes, crossing sand ripples, escaping from burying sand, and crossing rocks with additional camouflaging features. The twisting body provides anchoring functionality by burrowing into loose sand. When encountering obstacles, they can either self-turn or self-snap for obstacle negotiation and avoidance. Theoretical models and finite element simulation reveal that such physical intelligence is achieved by spontaneously snapping-through its soft body upon active and adaptive soft bodyobstacle interactions. Utilizing this strategy, they can intelligently escape from confined spaces and maze-like obstacle courses without any human intervention. This work presents a de novo design of embodied physical intelligence by harnessing the twisting geometry and snap-through instability for adaptive soft robot-environment interactions.