The study delivers a cohesive system that combines structural stress investigation, navigational planning evaluation, and adaptive joint control to optimize hexapod effectiveness on hills, stairs, and uneven surfaces. The robot was developed through the iterative drafting technique and designed by assigning in PLA material. Structural examination with Finite Element Analysis (FEA) under 10 N and 20 N forces demonstrated a positive stress allocation and a safety factor of 2.8, combining compact development with durability. In the ROS/Gazebo exploration investigations utilizing global planners like A*, Dijkstra, RRT, and Artificial Potential Field (APF) in combination with a PID-driven local planner, A* as well as Dijkstra developed nearly the best pathways with 100% accuracy. This cut down on route variation by about 17% in comparison to RRT. RRT established confident that the exploration was always the same, but it established paths that were more lengthy and less smooth. APF, on the contrary, made paths that were smooth but less reliable due to the local minima. Adaptive synchronization for joint control quantitatively provided an improvement in joint angle stability, reducing oscillatory deviations by 12% and displacement errors by 15% relative to baseline controllers. The core novelty within this approach is the integrative methodology that will inherently synergize finite element structural analysis, comparative path planning, and Adaptive joint synchronization: presenting a comprehensive optimization strategy, new to hexapod robotics. Together, these advances allow for robust and efficient real-world deployment of hexapods. Future work will extend to hybrid learning-based planning, and sensor-driven dynamic adaptation.
Kappagantula et al. (Thu,) studied this question.