Abstract

Robots, as a form of technology with various designs, have infinite potential to serve in multiple areas. Hexapod robots are a design that mimics the six-legged structure of many insects. The six-legged structure offers significant advantages in stability, adaptability to diverse environments, and fault tolerance compared to other widely used robots. A well-designed algorithm for gait planning is required to control this complex system. Fixed pattern gait planning is limited in multiple ways, so advanced gait planning methods are developed. The paper compares the advantages of two commonly used gait planning algorithms: Reinforcement Learning (RL) and Central Pattern Generators (CPGs). RL enables hexapod robots to discover the best gaits by employing a process of trial and error. This enables them to adapt dynamically to complex and changing environments. However, flexibility comes at the cost of requiring significant computational resources and extensive training time. CPGs leverage biological principles to produce rhythmic and stable movement patterns through simpler, oscillatory control mechanisms, offering robust and energy-efficient gait generation. While CPGs provide quick and reliable solutions with minimal computation, they lack the adaptability to sudden disturbance without enough data for the environment. Future work will focus on developing hybrid approaches that effectively combine the strengths of both methods.