Integrated Navigation System Design for Micro Planetary Rovers: Comparison of Absolute Heading Estimation Algorithms and Nonlinear Filtering

Abstract

This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level.

High-Precision Heading Determination Based on the Sun for Mars Rover

“…Yang et al use a large-field-of-view sun sensor for heading determination, and the precision reaches 0.1123 ∘ when observing the sun for 30 minutes, which is the best reported precision so far [8]. Illyas et al present a novel algorithm for micro-planetary rover-heading determination using a low-cost sun sensor, and a large number of experiments show that the heading precision reaches 0.09 ∘ (1 ), which plays an important role in reducing the accumulated heading error of MEMS sensors [9]. In a GPS-denied environment, visual navigation can provide accurate localization [10], but the error grows sharply with the distance travelled.…”

INS/GNSS Integrated Rover Navigation Designed With MDPO-Based Dual-Satellite Lunar Global Navigation Systems

“…(σ DOP jj ) 2 ×σ △∇ωr (20) where U P E represents user position error, which is the distance between the true rover position and an estimated rover position.…”

“…Those measurements are commonly used as navigational information in many planetary rovers. For example, rover's orientation can be derived from a gyro [20], a sun sensor [21], [22] or star tracker [23], [24], and traveling distance can be derived from a wheel odometry [25], [26], lidar [27], [28] or visual odometry [29]- [31].…”

“…These sensor error parameters, σ d , σ △θ , σ θ , δd, δθ ob , and δθ ob , assume micro-sized rovers and were created by adding safety margins to the values reported in other micro-rover navigation studies, namely [29] for the traveling distance measurement error and [20] for the reorientation and orientation measurement error. It is important to note that these values are conservative and can be improved for larger rovers.…”