Due to the prospect of using walking robots in an impassable environment for tracked or wheeled vehicles, walking locomotion is one of the most remarkable accomplishments in robotic history. Walking robots, however, are still being deeply researched and created. Locomotion over irregular terrain and energy consumption are among the major problems. Walking robots require many actuators to cross different terrains, leading to substantial consumption of energy. A robot must be carefully designed to solve this problem, and movement parameters must be correctly chosen. We present a minimization of the hexapod robot’s energy consumption in this paper. Secondly, we investigate the reliance on power consumption in robot movement speed and gaits along with the Cost of Transport (CoT). To perform optimization of the hexapod robot energy consumption, we propose two algorithms. The heuristic algorithm performs gait switching based on the current speed of the robot to ensure minimum energy consumption. The Red Fox Optimization (RFO) algorithm performs a nature-inspired search of robot gait variable space to minimize CoT as a target function. The algorithms are tested to assess the efficiency of the hexapod robot walking through real-life experiments. We show that it is possible to save approximately 7.7–21% by choosing proper gaits at certain speeds. Finally, we demonstrate that our hexapod robot is one of the most energy-efficient hexapods by comparing the CoT values of various walking robots.

Locomotion over rough terrain is still a problem yet to be solved for legged robots. One of the problems arises from the inability to identify terrain roughness during locomotion, which could be crucial for decision-making and successful task completion. Our proposed terrain roughness method is inspired by the observation that humans can sense their limb position in space without looking at them, which allows us to estimate obstacle heights. This method is based on robot feet coordinate standard deviation (further referred to as $SD$) parameter evaluation. $SD$ values could be categorized to represent different terrain roughness, and such categories could be useful for selecting different gaits for different terrains. In this paper, we investigate the possibility of using already known feet coordinates to evaluate terrain roughness by calculating their standard deviation ($SD$). We present simulation results that show that the $SD$ value only depends on terrain roughness and is not influenced by large terrain slopes. Experiments were conducted with real robots while walking over obstacles with different gaits to validate the method. This research mainly aims to test how robot gaits influence $SD$ parameters for terrain roughness evaluation. The experimental results showed that the $SD$ parameter calculated from the robot’s foot coordinates can be used to evaluate terrain roughness. The robot’s gaits have little to no influence on the $SD$ parameter.