In this study, we present a novel neural control architecture for gait adaptation and obstacle avoidance of a tailless gecko robot. The control architecture is based on a hierarchical modular structure, consisting of several neural layers and modules. The first layer contains three sensory preprocessing modules which filter sensory noise and generate appropriate descending commands to activate corresponding behaviors through the second and third layers. The second and third layers contain a central pattern generator (CPG) module and CPG postprocessing modules, respectively. The CPG module generates basic rhythmic locomotion patterns, shaped by the CPG postprocessing modules to achieve different gaits (i.e., wave, intermediate, and trot) as well as different climbing directions (i.e., forward and sideways). We use a body inclination sensor to adapt the robot gait while climbing on different slope angles, with infrared sensors to detect an obstacle on its climbing path and activate obstacle avoidance behavior. We successfully tested our control approach on a real tailless gecko robot. As a result, the robot can efficiently climb forward on different slope angles (including 90^∘) and automatically adapt its climbing gait accordingly, to maximize climbing speed and ensure stability. It can also avoid an obstacle by changing its climbing direction from forward to sideways.

在这项研究中，我们提出了一种新型神经控制架构，用于无尾壁虎机器人的步态适应和避障。控制体系结构基于分层的模块化结构，该结构由几个神经层和模块组成。第一层包含三个感官预处理模块，它们过滤感官噪声并生成适当的降序命令以激活通过第二层和第三层的相应行为。第二层和第三层分别包含中央模式生成器（CPG）模块和CPG后处理模块。CPG模块生成基本的节奏运动模式，由CPG后处理模块调整形状，以实现不同的步态（即波浪，中间和小跑）以及不同的攀爬方向（即向前和向侧）。我们使用人体倾斜传感器适应机器人的步态，同时在不同的坡度上爬坡，红外传感器检测爬坡路径上的障碍物并激活避障行为。我们在真正的无尾壁虎机器人上成功地测试了控制方法。结果，机器人可以在不同的倾斜角度（包括90^∘），并自动相应地调整它的攀登步态，最大限度地上升速度和确保稳定性。通过将攀爬方向从向前更改为侧向，也可以避免障碍。