Granular flows interacting with rigid bodies are key to machine-terrain interactions in robotics and related fields, and still pose several open problems. Continuum methods and deep learning, separately and in combination, show promise. This research advances machine learning methods for modeling rigid body-driven granular flows, for terrestrial industrial machine and planetary rover (where gravity is an important factor) applications. The original contribution of this research is the application of a subspace machine learning simulation approach for these engineering problems. To generate training datasets, a high-fidelity continuum method, the material point method (MPM) is utilized. Principal component analysis (PCA) is used to reduce the dimensionality of data. It is shown that the first few (8 in our datasets) principal components of our high-dimensional data keep almost the entire variance in data. A graph network simulator (GNS) is trained to learn the underlying subspace dynamics. The learned GNS is then able to predict particle positions and interaction forces with good accuracy, averaging 6 × 1 0 − 5 and 3 × 1 0 − 6 mean squared position error and 7% and 17% mean percentage force error, for excavation and wheel data, respectively. More importantly, PCA significantly enhances the time and memory efficiency of GNS in both training and rollout. This enables GNS to be trained using a single desktop graphics processing unit (GPU) with moderate video random-access memory (VRAM). This also makes the GNS real-time on large-scale 3D physics configurations (700x faster than our continuum method), which is the first demonstration of such significantly accelerated high-fidelity granular flow engineering simulations.

A skid-steer rover's power consumption is highly dependent on the turning radius of its path. For example, a point turn consumes a lot of power compared to a straight-line motion. Thus, in path planning for this kind of rover, turning radius is a factor that should be considered explicitly. There is a lack of any analytical approach in literature for finding energy-optimal paths for skid-steer rovers. The key contribution of this work is an energy-time equivalency theorem, for skid-steer rovers on obstacle-free hard ground. The theorem converts the energy-optimal problem into an equivalent time-optimal problem. This non-intuitive result stems from the fact that with this model of the system the total energy is fully parameterized by the geometry of the path alone. Hence, instead of directly solving the energy-optimal path planning problem, which is highly nonlinear, the equivalent time-optimal problem can be solved. Furthermore, experimental results are provided to experimentally prove the equivalency theorem while using Husky UGV skid-steer rover.

This article presents a viable approach to mobile 3-D printing in which a large object is printed in segments. The printer's motion between printing each segment is localized precisely using the novel procedure of simultaneous localization and additive manufacturing (SLAAM), enabling the joining of subsequent segments while also maintaining overall geometric compliance of the printed part. SLAAM achieves sub-mm accuracy on objects over 400 mm long. SLAAM demonstrates the importance of fusing local (3-D scanner), and global (total-station range finder) sensing, and of maintaining, and updating estimates of the printed part geometry in a global frame. A six-parameter representation for a printed object's planar surfaces, consisting of the plane's normal vector, and a 3-D point on the planar patch itself, demonstrates good performance even in the presence of high odometry error.

Mitigating potential hazards for planetary rovers posed by soft soils requires testing in representative environments such as with Martian soil simulants in reduced gravity. This work describes the experimentation, methods, and results of a rover-soil visualization technique that produced rich datasets of reduced gravity wheel-terrain interaction. The activities are linked to the upcoming ExoMars space mission, through the use of ExoMars wheel prototype and Martian soil simulant in simulated Martian gravity produced in parabolic flights. The results indicate that, with wheel normal load held equal between experiments, the amount of soil mobilized by wheel-soil interaction increases as gravity decreases. Moreover, the amount of soil mobilized is more sensitive to slip in lower gravity. The results of the visualization analysis suggest a deterioration in the soil resistance and weaker soil bonding at lower gravities, which undermines the rover mobility by reducing the net traction. The results have important implications regarding the practice of using a reduced-mass rover on Earth to assess the performance of a full-mass rover in similar soil on an extraterrestrial surface.

Planetary rovers face mobility hazards associated with various classes of terrains they traverse: sand, bedrock, and rock-strewn terrain. This work develops visual classifiers for these 3 terrain types for single monochrome navigation images from the NASA Mars Exploration Rover missions. The classifiers are based primarily on visual texture, captured in histograms of edges filter responses at various scales and orientations. Monochrome image intensity is further used to distinguish between confusing rock and bedrock cases. Three approaches are investigated: a gradient-based simplified HOG descriptor, a simplified GIST descriptor, and MR8 textons. Local rotational invariance is implemented in each approach, as validation tests demonstrate its benefit to performance. K-Nearest Neighbors is used for the final classification. No major differences in performance are observed between the three approaches, leading to the adoption of the HOG approach due to its lower computational complexity and thus highest applicability to planetary missions. Final tests demonstrate an accuracy between 70% and 93% (81% average) for the 3-way classification using the simplified HOG descriptor.

Grousers, sometimes called lugs, are recognized as a way to improve wheel performance and traction, but there have been, to date, no comprehensive guidelines for choosing grouser patterns. This work presents a quantitative expression for determining appropriate grouser spacing for rigid wheels. Past empirical studies have shown that increasing grouser height and number can improve performance, to a point. The newly proposed grouser spacing equation is based on observations that wheels with an inadequate number of grousers induce forward soil flow ahead of the wheel, and thus rolling resistance. The equation relates geometric wheel parameters (wheel radius, grouser height and spacing) and operating parameters (slip and sinkage), and predicts a maximum allowable grouser spacing (or, equivalently, a minimum number of grousers). Experiments with various grouser heights and numbers demonstrate good correspondence to the proposed equation, as increases in number of grousers beyond the predicted minimum number stop improving performance. A grouser spacing equation is particularly useful for designing efficient wheels. The proposed relation includes slip and sinkage, parameters that cannot be assumed constant or known a priori, but this work shows that wheels designed using the proposed equation are robust to changing operating scenarios even if they degrade beyond estimated nominal conditions.