Target-directed navigation using wireless sensor networks and implicit surface interpolation

This paper extends the novel research for event localization and target-directed navigation using a deployed wireless sensor network (WSN) [4]. The goal is to have an autonomous mobile robot (AMR) navigate to a target-location by: (i) producing an artificial magnitude distribution within the WSN-covered region, and (ii) having the AMR use the pseudo-gradient from the interpolated distribution in its neighborhood, as it moves towards the target location. Implicit surfaces are used to interpolate the artificial distribution. This scheme only uses the topology of the WSN and received signal strength (RSS) to estimate an efficient navigation path for the AMR. Here, the AMR does not require global coordinates for the region, as it relies on local, neighborhood information alone to navigate. The performance of the scheme is analyzed with hardware experiments and in simulation, using a variety of node-densities and with increasing levels of noise to ensure robustness.

Authors

• Nikhil Deshpande
• Edward Grant
• Thomas C. Henderson

Keywords

• Interpolation
• Training
• Navigation
• Wireless sensor networks
• Trajectory
• Noise
• Splines (mathematics)
• Sensor Networks
• Wireless Sensor
• Implicit Surface
• Received Signal Strength
• Automated Guided Vehicles
• Hardware Experiments
• Linear System
• Training Phase
• Random Generation
• Global Positioning System
• Radial Basis Function
• Regional Lymph Nodes
• Neighboring Nodes
• Node Positions
• Fitness Landscape
• Directional Antenna
• Thin-plate Spline
• Angle Estimation
• Interpolation Scheme
• Angle Information
• Target-directed navigation
• pseudo-gradient
• spline-interpolated distribution