Matthew Heverly

A hopping robot for planetary exploration

This paper presents the design and some preliminary analysis of a hopping robot for planetary exploration. The goal of this project is to explore a different mobility paradigm which may present advantages over conventional wheel and leg locomotion. The approach is to achieve mobility by hopping and perform science and imaging via rolling. The device is currently equipped with a single video camera representing the science sensor suite. The hopper is equipped with a simple microprocessor and wireless modem so that it can receive sequences of commands and autonomously execute them, making it suitable for exploration of distant planets, comets and asteroids. One important feature of this hopper is that it uses a single motor for hopping in a specified direction as well as pointing the camera via rolling.

Dynamic surface grasping with directional adhesion

Dynamic surface grasping is applicable to landing of micro air vehicles (MAVs) and to grappling objects in space. In both applications, the grasper must absorb the kinetic energy of a moving object and provide secure attachment to a surface using, for example, gecko-inspired directional adhesives. Functional principles of dynamic surface grasping are presented, and two prototype grasper designs are discussed. Computer simulation and physical testing confirms the expected relationships concerning (i) the alignment of the grasper at initial contact, (ii) the absorption of energy during collision and rebound, and (iii) the force limits of synthetic directional adhesives.

Mars Science Laboratory Curiosity Rover Megaripple Crossings up to Sol 710 in Gale Crater

After landing in Gale Crater on August 6, 2012, the Mars Science Laboratory Curiosity rover traveled across regolith-covered, rock-strewn plains that transitioned into terrains that have been variably eroded, with valleys partially filled with windblown sands, and intervening plateaus capped by well-cemented sandstones that have been fractured and shaped by wind into outcrops with numerous sharp rock surfaces. Wheel punctures and tears caused by sharp rocks while traversing the plateaus led to directing the rover to traverse in valleys where sands would cushion wheel loads. This required driving across a megaripple (windblown, sand-sized deposit covered by coarser grains) that straddles a narrow gap and several extensive megaripple deposits that accumulated in low portions of valleys. Traverses across megaripple deposits led to mobility difficulties, with sinkage values up to approximately 30% of the 0.50 m wheel diameter, resultant high compaction resistances, and rover-based slip up to 77%. Analysis of imaging and engineering data collected during traverses across megaripples for the first 710 sols (Mars days) of the mission, laboratory-based single-wheel soil experiments, full-scale rover tests at the Dumont Dunes, Mojave Desert, California, and numerical simulations show that a combination of material properties and megaripple geometries explain the high wheel sinkage and slip events. Extensive megaripple deposits have subsequently been avoided and instead traverses have been implemented across terrains covered with regolith or thin windblown sand covers and megaripples separated by bedrock exposures.

SPOC: Deep Learning-based Terrain Classification for Mars Rover Missions

This paper presents Soil Property and Object Classification (SPOC), a novel software capability that can visually identify terrain types (e.g., sand, bedrock) as well as terrain features (e.g., scarps, ridges) on a planetary surface. SPOC works on both orbital and ground-bases images. Built upon a deep convolutional neural network (CNN), SPOC employs a machine learning approach, where it learns from a small volume of examples provided by human experts, and applies the learned model to a significant volume of data very efficiently. SPOC is important since terrain type is essential information for evaluating the traversability for rovers, yet manual terrain classification is very labor intensive. This paper presents the technology behind SPOC, as well as two successful applications to Mars rover missions. The first is the landing site traversability analysis for the Mars 2020 Rover (M2020) mission. SPOC identifies 17 terrain classes on full-resolution (25 cm/pixel) HiRISE (High Resolution Imaging Science Experiment) images for all eight candidate landing sites, each of which spans over ∼ 100km. The other application is slip prediction for the Mars Science Laboratory (MSL) mission. SPOC processed several thousand NAVCAM (Navigation camera) images taken by the Curiosity rover. Predicted terrain classes were then correlated with observed wheel slip and slope angles to build a slip prediction model. In addition, SPOC was integrated into the MSL downlink pipeline to automatically process all NAVCAM images. These tasks were impractical, if not impossible, to perform manually. SPOC opens the door for big data analysis in planetary exploration. It has a promising potential for a wider range of future applications, such as the automated discovery of scientifically important terrain features on existing Mars orbital imagery, as well as traversability analysis for future surface missions to small bodies and icy worlds.

Data-driven surface traversability analysis for Mars 2020 landing site selection

The objective of this paper is three-fold: 1) to describe the engineering challenges in the surface mobility of the Mars 2020 Rover mission that are considered in the landing site selection processs, 2) to introduce new automated traversability analysis capabilities, and 3) to present the preliminary analysis results for top candidate landing sites. The analysis capabilities presented in this paper include automated terrain classification, automated rock detection, digital elevation model (DEM) generation, and multi-ROI (region of interest) route planning. These analysis capabilities enable to fully utilize the vast volume of high-resolution orbiter imagery, quantitatively evaluate surface mobility requirements for each candidate site, and reject subjectivity in the comparison between sites in terms of engineering considerations. The analysis results supported the discussion in the Second Landing Site Workshop held in August 2015, which resulted in selecting eight candidate sites that will be considered in the third workshop.