Hexapod Locomotion Across Structured and Unstructured Terrains: A Data‐Driven Review of Modeling, Control, and Validation

ABSTRACT

The design of a hexapod is complex and requires integration between kinematic models, control systems, and sensing. Existing literature has reviewed these sub‐systems in isolation. Since 2021, there has been no review of the field despite significant advancements in soft soil, lunar traversal, and artificial intelligence. This paper addresses that gap by establishing a novel terrain‐coded locomotion layer scaffold via taxonomy creation. A subset of 58 primary studies were deeply analyzed and synthesized to quantify method choices across hexapod locomotion. Findings show that the DH‐Parameter system and geometric inverse kinematics models are most widely used across all terrain types. Jacobian‐based inverse kinematic models prove to be computationally demanding, yet more accurate. Jacobian approaches also result in multiple end‐effector positions and unnatural poses. Bio‐inspired control systems and proprioceptive methods show promise for smoother gait switching and real‐time adaptability. Approaches related to reinforcement learning, convolutional neural networks and long short‐term memory models were also introduced in recent years. These contribute toward increasing performance, terrain adaptability and path planning. The primary contribution of this paper is a set of data‐driven design patterns linking mathematical models, layered controls, and simulator‐terrain couplings for structured and unstructured environments. These patterns support more defensible deployment‐oriented design choices. Future directions and generalized guidelines based on these design patterns for hexapod locomotion are also highlighted. Thus documenting future‐facing research which shows the hexapod becoming a fully autonomous system capable of harsh terrain traversal (lunar surfaces, ice, soft soil, underwater).