Hayabusa2’s Superior Solar Conjunction Phase

Hayabusa2 is the ongoing JAXA’s sample and return mission to the asteroid Ryugu. In late 2018, Ryugu was in superior solar conjunction with the Earth. It is the first time that a spacecraft experiences the blackouts in the communication link with the Earth while hovering around a small celestial body. In this article, the design of the nominal conjunction trajectory flown by the Hayabusa2’s spacecraft is presented. The requirements for the conjunction trajectory were (1) to guarantee a low fuel consumption, (2) to ensure the visibility of the asteroid by the spacecraft’s wide angle camera ($60^{\circ }$ 60 ∘ FoV), and (3) to increase the spacecraft altitude to a safety location ($\sim109~\mbox{km}$ ∼ 109 km ) from the nominal BOX-A operation of 20 km (Home Position - HP). Finally, (4) to return at BOX-A after the conjunction phase. Given the mission constraints, the designed conjunction trajectory appears to have a fish-shape in the Hill coordinates therefore we renamed it as “ayu” (sweetfish in Japanese) trajectory. The optNEAR tool was developed for the guidance ($\Delta V\mbox{s}$ Δ V s planning) and navigation design of the Hayabusa2’s conjunction mission phase. A preliminary sensitivity analysis in the Hill reference frame proved that the ayu trajectory is a good candidate for the conjunction operation of hovering satellite. The solution in the Hill coordinates is refined in the full-body planetary dynamics (optNEAR Tool) before flight. The ayu conjunction trajectory requires (a) two deterministic $\Delta V\mbox{s}$ Δ V s at the Conjunction Orbit Insertion (COI) point and at the Home-position Recovery Maneuver (HRM) point respectively. (b) Two stochastic $\Delta V\mbox{s}$ Δ V s , known as Trajectory Correction Manoeuvres (TCMs), before and after the deep conjunction phase are also required. The constraint linear covariance analysis in the full-body dynamics is here derived and used for the preliminary guidance and navigation planning. The results of the covariance analysis were validated in a nonlinear sense with a Monte Carlo approach which proved the validity of the semi-analytic method for the stochastic $\Delta V\mbox{s}$ Δ V s planning derived in this paper.

Optimization Complete Area Coverage by Reconfigurable hTrihex Tiling Robot

Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.