Performance Analysis on a Navigation Method of Multiple AUVs for Wide Area Survey

Abstract

In this paper, we propose a navigation method of multiple AUVs and give a performance analysis on position estimation for a wide area survey. The key idea of the proposed method is alternating roles of the AUVs between Moving and Measuring Role (MMR) and Landmark Role (LR). AUVs in the MMR move based on the AUVs in the LR. On the other hand, AUVs in the LR keep their positions on the seabed to act as landmarks for the moving AUVs. By alternating their roles, the two groups of AUVs can observe in a wide area while maintaining a good position estimate.AUV position is estimated by mutual acoustic measurements of relative distance among AUVs and direction among them. These position measurements are fused with other on-board sensors such as Doppler velocity logger (DVL), fiber optic gyro (FOG) and depth sensor by particle filter for a robust position estimate against sensor noises and lack of measurements.We verified the performance of the method through navigation simulation based on the performance of sensors obtained during various experiments performed in a pool environment and at sea. In the simulation, the proposed method achieved a positioning error considerably smaller than dead reckoning and conventional navigation methods of a single AUV. In the proposed method, each AUV also achieved state estimation, suppressing deviation from its true position and heading due to alternating roles and evaluating outliers of positioning measurements by particle filter. Therefore, the proposed method is suitable for a wide area survey without sensor drifts and deviation from true state compared with conventional navigation methods.

State Estimation and Compression Method for the Navigation of Multiple Autonomous Underwater Vehicles With Limited Communication Traffic

“…Several particle-filter-based navigation algorithms for vehicular navigation have been proposed, some of which have been implemented in AUVs [4], [6], [7], [19]. The particle filter updates the states through two phases: the prediction phase and the observation phase.…”

“…Table II shows the success rate and standard deviation of positioning measured through a series of sea trials [7]. In these trials, one ALOC system was mounted on an AUV and the other was mounted on a stationary seafloor station.…”

“…The ground velocity sensor and one-axis heading rate gyro provide the ground velocity and the angular velocity , respectively. In the observation phase, the states are updated from relative acoustical positioning measurements between the moving and landmark AUVs [7], [19]. The positioning measurements are the relative distance and the relative directions and .…”

“…In the CPS approach, a group of moving vehicles locate themselves relative to a group of stationary vehicles. This technique was applied to multiple AUV navigation for the first time (to our knowledge) in the previous study [7]. Since the acoustic channel severely restricts communication, offering only very low bandwidths, cooperative surveying through information sharing of their individual states is not easily achieved by underwater vehicles.…”

“…Vehicles often fail to reach even precisely located targets because they are misled by underwater currents, positioning errors, and other factors. To overcome these problems, our previous work has proposed a positioning method for multiple AUVs, in which a moving AUV estimates the states of both moving and landmark AUVs relative to a stationary landmark AUV on the seafloor [7]. The central concept is to alternate the moving and landmark roles among the AUVs.…”