Trajectory Planning with Control Horizon Based on Narrow Local Occupancy Grid Perception

The occupancy grid framework provides a robust and unified approach to a variety of problems in spatial robot perception and navigation [1]. The occupancy field can be depicted by a probability density function that relates the sensor measures with the real cell state. The Bayesian estimation formulation is proposed by researchers; in this sense not only the use of the last sensor observation measure but also the consideration of the last occupancy estimate are proposed for occupancy. The use of the 2D grids for static indoor mapping is proposed in [2]. Other works propose multidimensional grids for multi target tracking by using obstacle state space with Bayesian filtering techniques [3]. The integration of perception and planning is an interesting topic; hence planning cycles are reported in [4], in which it is considered a time horizon where partial trajectories are planned until the robot state is close to the goal. This work proposes a methodology that obtains a local cell and trajectory, within a narrow grid provided by the on robot system perception, which approaches the robot to the final desired objective. The local data grid is presented as a horizon for making the trajectory planning. A practical approach for differential driven WMR (wheeled mobile robots) is shown by using monocular information and model based control strategies.