{"id":216220,"date":"2024-12-18T10:40:40","date_gmt":"2024-12-18T14:40:40","guid":{"rendered":"https:\/\/inspenet.com\/noticias\/bio-inspired-quadruped-robot-masters-complex-terrain-without-sensors\/"},"modified":"2024-12-18T11:00:50","modified_gmt":"2024-12-18T15:00:50","slug":"bio-inspired-quadruped-robot-masters-complex-terrain-without-sensors","status":"publish","type":"noticias","link":"https:\/\/inspenet.com\/en\/news\/bio-inspired-quadruped-robot-masters-complex-terrain-without-sensors\/","title":{"rendered":"Bio-inspired quadruped robot masters complex terrain without sensors"},"content":{"rendered":"\n

A group of researchers has made a significant breakthrough in quadruped robotics<\/a><\/strong> , creating a robot dog model that emulates the adaptability of animals when moving through complex terrain.<\/strong> This innovation is based on a bio-inspired locomotion framework, which integrates gait strategies, procedural memory, and real-time adjustments.<\/p>\n\n

A learning-based approach <\/h2>\n\n

The research team from the University of Leeds and University College London has developed a system that uses a deep reinforcement learning (DRL) model. This framework allows a quadruped robot<\/a><\/strong> to autonomously navigate uneven terrain, without the need for external sensors or prior training. <\/p>\n\n

Unlike conventional systems that rely on a single gait strategy, the new model is capable of performing complex gait transitions<\/strong> and adapting to changing environmental conditions, improving its stability and resilience.<\/p>\n\n

Robot dog adapts to animal movements<\/h2>\n\n

The secret of this innovation lies in the inspiration taken from animal biomechanics<\/strong> . Throughout evolution, quadruped mammals have developed a remarkable ability to adapt to different types of terrain through different gait modes, such as trotting, running or jumping. <\/p>\n\n

This approach allows robots to mimic these capabilities, adapting their movement to the needs of the moment. The model developed by the researchers integrates three key attributes<\/a><\/strong> : advanced gait selection, procedural memory for rapid transition, and precise movement adjustments, offering a level of adaptability never seen before. <\/p>\n\n

Current locomotion systems, although robust, cannot perform gait transitions fluidly, as they rely on predefined strategies that cannot adjust to out-of-the-ordinary conditions. In contrast, the system developed in this study employs an algorithm that enables autonomous gear shifting<\/strong> , similar to how animals do in the wild, making it a highly adaptable solution. <\/p>\n\n

One step closer to autonomous robots<\/h2>\n\n

This bio-inspired locomotion framework has the potential to greatly improve the performance of robots in a variety of applications, from exploring difficult terrain to use in rescue tasks. Furthermore, by not relying on external sensors, the model presents a significant advantage by reducing the complexity and costs associated with traditional robotic systems<\/a><\/strong> . <\/p>\n\n

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