A time-power efficient fixed point single purpose processor for mapping using ultrasound sensors

Creation of a world model of the environment as perceived by a robot is central to autonomous navigation. This involves use of multiple sensors and intelligent sensor data fusion techniques. Mapping techniques based on the occupancy grid method use a probabilistic model of the ultrasound sensors to compute probability of presence or absence of obstacles in the environment. Acquiring sensor data and evaluating the probability functions in a real time environment imposes a significant overhead on the processing time which can be better utilised for event handling and affecting control. In this paper, we propose a custom single purpose fixed point processor architecture which supports multi-channel ultrasound sensor interface along with a dedicated memory block for acquiring range readings from up to 24 sensors, evaluating the probability values for empty and occupied cells in the environment around the robot and updating them in the memory block. The architecture is optimised for low power operation keeping in mind the constraints of on-board power on a navigating robot.