Autonomous robot navigation is becoming an increasingly important research topic for mobile robots. In the last few years, significant progress has been made towards stable robotic bipedal walking. This is creating an increased research interest in developing autonomous navigation strategies which are tailored specifically to humanoid robots. Efficient approaches to perception and motion planning, which are suited to the unique characteristics of biped humanoid robots and their typical operating environments, are receiving special interest. In this paper, we present a time-efficient motion planning system for a Fujitsu HOAP-2 humanoid robot. The sampling based algorithm is used to provide the robot with minimal free configuration space which is sampled to extract the robot path. For collision detection, a cylinder model is used to approximate the trajectory for the body center of the humanoid robot during navigation. It calculates the actual distances required to execute different actions of the robot and compares them with the distances to the nearest obstacles. The A* search algorithm is then implemented to find smooth and low-cost footstep placements of the humanoid robot within the resulting configuration space. The proposed hybrid algorithm reduces searching time and produces a smoother path for the humanoid robot at a low cost.

In this paper we present a psycholinguistically motivated architecture and its prototypical implementation for an incremental conceptualizer, which monitors dynamic changes in the world and simultaneously generates warnings for (possibly) safety-critical developments. It does so by conceptualizing events and building up a hierarchical knowledge representation of the perceived states of affairs. If it detects a safety problem, it selects suitable elements from the representation for a warning, brings them into an appropriate order, and generates incremental preverbal messages (propositional structures) from them, which can be taken by a subsequent component to encode them linguistically.