Lunar surface soft-landing analysis of a novel six-legged mobile lander with repetitive landing capacity

The autonomous robots consisting of an immovable lander and a rover are widely deployed to explore extraterrestrial planets. Two main drawbacks limit the development of this cooperative work mode: (1) it cannot perform soft-landing missions repeatedly on the planet, owing to the damage of buffer structure during soft-landing. (2) the rover’s detection area is restricted to the vicinity of the immovable lander. To overcome these problems, we have designed an innovative six-legged mobile lander with repetitive landing capacity, called “HexaMRL”, which integrates the functions of a lander and a rover including folding, deploying, repetitive landing, and walking. This novel robot’s legs adopted hybrid mechanism with active and passive compliance. Therefore, it remains to be a great challenge to analyze the robot soft-landing capacity which is determined by the parameters such as spring stiffness coefficient, damper damping coefficient, and initial tiptoe position. In order to solve the problem, the dynamic modeling and assessment criteria were established. The soft-landing process was analyzed through three numerical simulations using three sets of representative parameters based on dynamic model and the set of best effective parameters was chosen to apply in soft-landing experiment on a 5-DOF lunar gravity testing platform (5-DOF LGTP). The experiments were further verified that the selected parameters met the requirement of soft landing on the lunar surface. The HexaMRL provides novel insight for the next generation equipment for lunar exploration, which may be an efficient solution to the extraterrestrial planet exploration.

Aerothermal Modeling Challenges for Ice Giant Entry Probes

<p>In June 2017, NASA published the Ice Giants Pre-Decadal Survey Mission Study Report which took a fresh look at science priorities and mission concepts for missions to the Uranus and Neptune systems. In addition to science objectives, the team explored the state of required technologies for remote and in-situ science exploration. Notably, three of the four mission architectures considered in the study included an atmospheric probe. More recently, interest has grown within ESA for outer planet exploration. In support of this objective, ESA has performed two CFD studies (January & July 2019) which analyzed the feasibility of stand-alone elements (orbiter and probes) provided by ESA as a part of a NASA led mission to the Uranus or Neptune systems. The first study was carried out by ESA experts with active participation of NASA/JPL. ESA highlighted the necessity to deepen the knowledge characterizing the aerothermal environment of the probes.</p><p> </p><p>Entry environments for the NASA study were estimated using an aeroheating correlation that was calibrated to data returned from the Galileo probe entry to Jupiter. For the ESA concept study, aeroheating estimates were made using correlations employed during the design of the Galileo probe. Importantly, these correlations show large discrepancies in predicted total aeroheating (in some cases more than 100%), largely due to differences in the predicted radiative heat load. The magnitude of the disagreement is disconcerting in and of itself, but the problem is made worse by the fact that both correlations are being extrapolated from the extreme Galileo entry conditions to the (relatively) more benign Uranus and Neptune entry. It is likely that neither correlation is providing an accurate assessment of the true aeroheating loads at this time. Given that current NASA predictions are near the limits of existing TPS test capability, and that ESA predictions are more severe, improving the accuracy and associated margins of the prediction is critical to better assess mission feasibility.</p><p> </p><p>Recent work in NASA by Cruden (AIAA Paper No. 2015-0380) and Erb (AIAA Paper No. 2019-3360) have substantially improved our fundamental understanding of aerothermodynamics in Hydrogen-Helium atmospheres. Similar work is planned in ESA as well. However, these recent data have not been incorporated into updated design models for Outer Planet probes. In addition, this work does not address the problem of trace atmospheric constituents (such as Methane) that are known to be present in Ice Giant atmospheres and may substantially alter the resulting shock layer radiation signal by providing a ready source of free electrons to initiate excitation processes. The proposed presentation will review the current status of aerothermal modeling for Ice Giant entries and propose a path forward to reduce key uncertainties and enable optimized thermal protection system designs.</p>

A C-Legged Monopedal Robot and Its Transition From Multiple Locomotion Modes

This paper introduces a minimalistic design of a monopedal robot (monobot) with C-shaped legs which can achieve multiple locomotion modes (multi-mode) such as walking, leaping, as well as backward and forward flipping. The monobot contains an actuator, speed controller, 3D-printed base frame and legs, and battery set. The weight of the whole robot is less than 80 g. Dimensional parameters are optimized to simplify the design process and to identify effective factors for locomotion. Potential locomotion modes of the robot are analyzed by dynamics simulation. A simplified virtual prototype is tested within the multibody simulation software. An experimental platform of the monobot is also developed. The speed of the platform is adjusted to verify the correspondence between the actuator speed and locomotion mode as obtained by simulation. Potential applications of the multi-mode monobot include disaster rescue, planet exploration, and reconnaissance.

Genetic Algorithm for Mobile Robot Route Planning with Obstacle Avoidance

Nowadays many public and private institutions begin space studies projects. Among many problems to solve there is a planet exploration. Now rovers are controlled directly from the Earth, e.g. Opportunity. Missions must be planned on the Earth using simulators. Much better will be when the mission planner could set the target area and work to do and the rover will perform it independently. The solution is to make it autonomous. Without need of external path planning the rover can cover a much longer distance. To make autonomous rovers real it is necessary to implement a target leaded obstacle avoidance algorithm. Solutions based on graph algorithms use a lot of computing power. The others use intelligent methods such as neural networks or fuzzy logic but their efficiency in a very complex environment is quite low. This work presents an obstacle avoidance algorithm which uses the genetic path finding algorithm. The actual version is based on the 2D map which is built by the robot and the 2nd degree B-spline is used for the path model. The performance in the most cases is high using only one processor thread. The GA can be also easily multithreaded. Another feature of the algorithm is that, due to the GA random nature, the chosen path can differ each time on the same map. The paper shows the results of the simulation tests. The maps have the various complexity levels. On every map one hundred tests were carried out. The algorithm brought the robot to the target successfully in the majority of runs.