The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization performance with ZUPT. To do this, we propose a 3D dead-reckoning approach that predicts wheel slippage while the rover is in motion and forecasts the appropriate time to stop without changing any rover hardware or major rover operations. We validate with field tests that our approach is viable on different terrain types and achieves a 3D localization accuracy of ~ 97 % over 650 m drives on rough terrain.

This work aims at developing an efficient path planning algorithm for the driving objective of a Martian day (sol) that can take into account terrain information for application to the proposed Mars Sample Return (MSR) mission. To prepare the planning process for one sol (i.e., with a limited time allocated to driving), a map of expected rover velocity over a chosen area is constructed, obtained by combining traversability classes, rock abundance and slope at that location. The planning phase starts offline by computing several potential paths that can be traversed in one sol (i.e., a few hours), which will later provide suitable options to the rover if replanning is necessary due to unexpected mobility difficulties. Online, the rover gains information about its environment as it drives and updates the map locally if major discrepancies are found. If an update is made, the remaining driving time along the different options is recalculated and the most efficient path is chosen. The online process is repeated until the rover has reached its daily goal. When simulated on different areas at Gusev Crater, Mars, the algorithm correctly captured changes of terrain initially not mapped, and rerouted the rover to a more efficient path when necessary, in which case it effectively complied with the time constraint to reach the goal.

This paper presents the design, implementation, and evaluation of four filters for the estimation of angle of attack (AOA) and angle of sideslip (AOS) of small unmanned aerial vehicles (UAVs). Specifically, two novel filters (a complementary filter and an extended Kalman filter) are proposed and evaluated without using direct flow angle and GPS measurements; two existing AOA/AOS filters are also implemented and evaluated. All filters are designed with minimal inputs and states to ensure the ease of implementation, simplicity of tuning, and computation efficiency. Both simulation and UAV flight test results show the performance of the proposed filters. Especially, flight test results from two different UAVs (a T-tail UAV and a flying wing UAV) show that the root mean square errors of estimated inertial AOA and AOS are less than 1.5 degrees under nominal flight conditions and around 2 degrees under aggressive maneuvers compared with direct flow angle measurements.

This paper presents modifications to the stochastic stability lemma which is then used to estimate the convergence rate and persistent error of the linear Kalman filter online without using knowledge of the true state. Unlike previous uses of the stochastic stability lemma for stability proof, this new convergence analysis technique considers time-varying parameters, which can be calculated online in real-time to monitor the performance of the filter. Through simulation of an example problem, the new method was shown to be effective in determining a bound on the estimation error that closely follows the actual estimation error. Different cases of assumed process and measurement noise covariance matrices were considered in order to study their effects on the convergence and persistent error of the Kalman filter.

Robotic pollinators not only can aid farmers by providing more cost effective and stable methods for pollinating plants but also benefit crop production in environments not suitable for bees such as greenhouses, growth chambers, and in outer space. Robotic pollination requires a high degree of precision and autonomy but few systems have addressed both of these aspects in practice. In this paper, a fully autonomous robot is presented, capable of precise pollination of individual small flowers. Experimental results show that the proposed system is able to achieve a 93.1% detection accuracy and a 76.9% ‘pollination’ success rate tested with high-fidelity artificial flowers.