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This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.
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http://dx.doi.org/10.3390/s21227609 | DOI Listing |
Sci Adv
August 2025
Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun, Jilin, 130022, China.
Biomimetics (Basel)
July 2025
College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Hydraulic actuated legged robots display bright prospects and significant research value in areas such as unmanned area surveying, disaster rescue, military fields, and other scenarios owing to their excellent bionic characteristics, particularly their heavy payload capabilities and high power density. To realize the all-terrain adaptation locomotion of the hydraulic hexapod robot (HHR) with a heavy payload, one alternative control framework is position-posture control based on joint position control. As the foundation for the steady locomotion of HHRs, it is imperative to realize high-precision joint position control to improve the robustness under external disturbances during the walking process and to complete the attitude control task.
View Article and Find Full Text PDFSustaining the robot's longevity becomes challenging in dynamic deployments characterized by new unknown environments and embodiments outside of the prior knowledge. Hence, the knowledge of robot-environment interactions needs to be continually updated for system adaptation. It can be implemented through self-verification as a continual comparison of predictions with observations using the predictive coding (PC) principle.
View Article and Find Full Text PDFBiomimetics (Basel)
June 2025
Department of Organizational Studies, University of Guanajuato, Fraccionamiento 1, Col. El Establo S/N, Marfil 36250, Mexico.
In this work, we propose the integration of a mechanism to enable smooth transitions between different locomotion patterns in a hexapod robot. Specifically, we utilize a spiking neural network (SNN) functioning as a Central Pattern Generator (CPG) to generate three distinct locomotion patterns, or gaits: walk, jog, and run. This network produces coordinated spike trains, mimicking those generated in the brain, which are translated into synchronized robot movements via PWM signals.
View Article and Find Full Text PDFSci Robot
June 2025
QUT Centre for Robotics, Queensland University of Technology, 2 George St, Brisbane, QLD 4001, Australia.
Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms, which still require accurate long-endurance localization. Although neuromorphic approaches offer potential for greater efficiency, real-time edge deployment remains constrained by the complexity of biorealistic networks.
View Article and Find Full Text PDF