Planning is one of the cornerstones of autonomous robot navigation. In this paper we introduce an open source planner called “OpenPlanner” for mobile robot navigation, composed of a global path planner, a behavior state generator and a local planner. OpenPlanner requires a map and a goal position to compute a global path and execute it while avoiding obstacles. It can also trigger behaviors, such as stopping at traffic lights. The global planner generates smooth, global paths to be used as a reference, after considering traffic costs annotated in the map. The local planner generates smooth, obstacle-free local trajectories which are used by a trajectory tracker to achieve low level control. The behavior state generator handles situations such as path tracking, object following, obstacle avoidance, emergency stopping, stopping at stop signs and traffic light negotiation. OpenPlanner is evaluated in simulation and field experimentation using a non-holonomic Ackerman steering-based mobile robot. Results from simulation and field experimentation indicate that OpenPlanner can generate global and local paths dynamically, navigate smoothly through a highly dynamic environments and operate reliably in real time. OpenPlanner has been implemented in the Autoware open source autonomous driving framework’s Robot Operating System (ROS).

The purpose of this research is to develop a system which gives blind people information of the environment around them. A person is equipped with a 3D scanner and a small sized PC while walking. The scanner scans and acquires 3D range data map of the environment. The PC analizes the range data map and detects objects which are useful for blind people in orde to walk. The PC gives environmental information to them by synthesized sound. This paper first introduces the concept of the whole system and clarify the tasks for realizing the system. Secondly the method for acquisition of 3D range data and detecting objects and obstacles are described. Then the usefulness of our proposed system is examined by an experiment in which our trial system detects bumps and trenches in the experimental environment.

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