Legged locomotion has high mobility on irregular surfaces by touching the ground at discrete points. Inspired by the creature’s legged locomotion, legged robots have been developed to explore unstructured environments. In this paper, we propose a modular crawler that can easily adjust the number of legs for adapting the environment that the robot should move. One module has a pair of legs, so the number of legs can be adjusted by changing the number of modules. All legs are driven by a single driving motor for simple and compact design, so the driving axle of each module is connected by the universal joint. Universal joints between modules enable the body flexion for steering or overcoming higher obstacles. A prototype of crawler with three modules is built and the driving performance and the effect of module lifting on the ability to overcome obstacles are demonstrated by the experiments.

Inspired by small insects, which perform rapid and stable locomotion based on body softness and tripod gait, various milli-scale six-legged crawling robots were developed to move rapidly in harsh environment. In particular, cockroach’s leg compliance was resembled to enhance the locomotion performance of the crawling robots. In this paper, we investigated the effects of changing leg compliance for the locomotion performance of the small light weight legged crawling robot under various payload condition. First, we developed robust milli-scale six-leg crawling robot which actuated by one motor and fabricated in SCM method with light and soft material. Using this robot platform, we measured the running velocity of the robot depending on the leg stiffness and payload. In result, there was optimal range of the leg stiffness enhancing the locomotion ability at each payload condition in the experiment. It suggests that the performance of the crawling robot can be improved by adjusting stiffness of the legs in given payload condition.

Robots need to understand as much as possible about their environmental situation and react appropriately to any event that provokes changes in their behavior. In this paper, we pay attention to topological relations between spatial objects and propose a model of robotic cognition that represents and infers temporal relations. Specifically, the proposed model extracts specified features of the co-occurrence matrix represents from disparity images of the stereo vision system. More importantly, a habituation model is used to infer intrinsic spatial relations between objects. A preliminary experimental investigation is carried out to verify the validity of the proposed method under real test condition.