Distributed Processing of Load and Movement Feedback in the Premotor Network Controlling an Insect Leg Joint

In legged animals integration of information from various proprioceptors in and on the appendages by local premotor networks in the central nervous system is crucial for controlling motor output. To ensure posture maintenance and precise active movements, information about limb loading and movement is required. In insects, various groups of campaniform sensilla (CS) measure forces and loads acting in different directions on the leg, and the femoral chordotonal organ (fCO) provides information about movement of the femur-tibia (FTi) joint. In this study, we used extra- and intracellular recordings of extensor tibiae (ExtTi) and retractor coxae (RetCx) motor neurons (MNs) and identified local premotor nonspiking interneurons (NSIs), and mechanical stimulation of the fCO and tibial or trochanterofemoral CS (tiCS, tr/fCS), to investigate the premotor network architecture underlying multimodal proprioceptive integration. We found that load feedback from tiCS altered the strength of movement-elicited resistance reflexes and determined the specificity of ExtTi and RetCx MN responses to various load and movement stimuli. These responses were mediated by a common population of identified NSIs into which synaptic inputs from the fCO, tiCS, and tr/fCS are distributed, and whose effects onto ExtTi MNs can be antagonistic for both stimulus modalities. Multimodal sensory signal interaction was found at the level of single NSIs and MNs. The results provide evidence that load and movement feedback are integrated in a multimodal, distributed local premotor network consisting of antagonistic elements controlling movements of the FTi joint, thus substantially extending current knowledge on how legged motor systems achieve fine-tuned motor control.

Research on the Posture Control Method of Hexapod Robot for Rugged Terrain

This paper proposes a hexapod robot posture control method for rugged terrain to solve the problem of difficulty in realizing the posture control of a foot robot in rough terrain. The walking gait and original position of a six-legged robot is planned, and the Layer Identification of Tracking (LIT) strategy is developed to enable the robot to distinguish mild rugged terrain and severe rugged terrains automatically. The virtual suspension dynamic model is established. In mild rugged terrain, the posture maintenance strategy is adopted to keep the stability of the torso. In severe rugged terrain, the posture adjustment strategy is adopted to ensure the leg workspace and make it more widely adapt to the changing terrain, and a gravity center position adjustment method based on foot force distribution is designed to use foot force as feedback to control the position and attitude. The experiment of posture control in rough terrain and climbing experiment in the ladder terrain shows that the hexapod robot has good posture maintenance and posture adjustment effects when traversing complex terrain through the posture maintenance strategy and the posture adjustment strategy. Combined with the terrain identification method based on LIT, the hexapod robot can successfully climb the ladder terrain through the identification of the changing ladder terrain, and the movement of the posture adjustment process is stable.