Embodied Design for Enhanced Flipper-Based Locomotion in Complex Terrains
Chikere, Nnamdi, McElroy, John, Ozkan-Aydin, Yasemin
–arXiv.org Artificial Intelligence
Despite significant advancements in robotic locomotion, navigating diverse landscapes for tasks such as search and rescue in complex environments (e.g., sandy terrains, wet forests, and regolith-covered landscapes), as well as responding to mudslides and avalanches, remains a formidable challenge for robotic systems (1,2). While conventional wheeled and legged robots excel on solid ground, they often struggle on granular media such as sand, grains, or pebbles (3) due to the non-uniform and deformable nature of the terrain (4). Moreover, factors like high resistance to penetration, instability, and limited load-bearing capacity of granular terrains can impede the mobility of these robots, leading to issues such as entrapment or slippage (5, 6). In addressing the limitations of traditional wheeled and legged robots, flipper-based locomotion offers a promising alternative. This concept draws inspiration from animals such as penguins, with their agile underwater propulsion using flippers (7, 8), and seals, known for their maneuverability in both water and land (9). Similarly, the fin-based locomotion of mudskippers, effective in terrestrial and aquatic settings, mirrors the adaptability of flipper-based systems, offering parallel insights for robotic design (10,11). Drawing inspiration from aquatic and amphibious animals, we can equip robots with flexible and powerful flippers, enhancing adaptable propulsion and maneuverability in diverse environments, from aquatic to granular terrains (12-16). Among the various examples of flipper-based locomotion in nature, sea turtles are particu-2 Figure 1: Biological and robotic sea turtle hatchlings navigating diverse terrains: Sea turtle hatchling (left) and its robotic counterpart (right) are shown traversing dry sand, small and big rocks, wet sand, and vegetation, illustrating the bio-inspired robot's design effectiveness and its capability to adapt to complex environmental conditions.
arXiv.org Artificial Intelligence
May-22-2024
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