Navigation of mobile robots using occupancy grids

A local navigation method for autonomous mobile robots with obstacle avoidance is developed in which the dynamics of the robot are considered. Car-like robots are mobile robots with dynamics and kinematics of a car, operating for specific transportation task in indoor and outdoor unknown environment may have to follow pre-defined trajectory or build a map and design trajectory as they travel and have to avoid obstacles discovered on their path during the operation. The only information required about the local environment is the distance between the robot and the angle made by obstacles with respect to robot's frame. Mapping is carried out using occupancy grid method in which the workspace of the robot is divided into square grids and each one is allocated with probability. Based on these probabilities, the occupancy of grids with obstacles is determined. Localization and estimating range and azimuth angle obtained from laser range sensor is carried out using Extended Kalman Filter (EKF). Since localization and simultaneously mapping of the environment is carried out for local path planning, the problem of localization and mapping can also be categorized under Simultaneous localization and Mapping (SLAM). Finally, obstacle avoidance algorithm is developed to fulfill these required tasks. Obstacle avoidance should be done in a way that robot does not diverge much away from the trajectory. Also, after avoiding obstacle it needs to come back on trajectory considering minimum distance. The effectiveness of the technique is demonstrated by means of simulation software examples in 2D environment.