2016 journal article
Autonomous navigation using received signal strength and bearing-only pseudogradient interpolation
ROBOTICS AND AUTONOMOUS SYSTEMS, 75, 129–144.
Autonomous mobile robots (AMRs) interacting with an a priori distributed wireless sensor network (WSN) in a region can address the three-tier challenge of navigating in unknown environments: (i) identifying target locations, (ii) planning paths to the targets, and (iii) efficiently executing the navigation paths to the targets. This paper presents low-complexity algorithms to address the second-tier and third-tier challenges, i.e., efficiently planning and executing paths to target locations. These novel approaches use only the information inherent in WSNs, i.e., received signal strength (RSS). The objective is to have the AMR navigate to a target location by: (i) producing an RSS-based artificial magnitude distribution in the navigation region, (ii) using particle filtering based bearing estimation for orientation information, and (iii) using interpolated pseudogradient for efficient path planning and navigation. Here, the AMR does not require: (i) the global location information for itself or the WSN, (ii) a priori information of the direction of a target location, or (iii) sophisticated ranging equipment for prior mapping. The AMR relies only on local, neighborhood information and low-cost wireless directional antennas for navigation. Real-world and simulation experiments, using a variety of node-densities, demonstrate the effectiveness of the proposed schemes. The low-cost, low-complexity advantages of the WSN-AMR interactive navigation provide for efficient map-less and ranging-less navigation methods. Efficient WSN-assisted AMR navigation by only 1 modality, received signal strength.Novel use of standard artificial potential field for way-point estimation.Introducing implicit surfaces for inter-node pseudogradient interpolation in WSN.Novel use of standard particle filtering for RSS-based WSN-node bearing estimation.Extensive simulation and hardware experimental validation.