System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Terramechanics and Machine Learning for the Characterization of Terrain

An instrumented rover wheel can collect vast amounts of data about a planetary surface. Planetary surfaces are changed by complex geological processes which can be better understood with an abundance of surface data and the use of terramechanics. Identifying terrain parameters such as cohesion and angle of friction hold importance for both the rover driver and the planetary scientist. Knowledge of terrain characteristics can warn of unsafe terrain and flag potential interesting scientific sites. The instrumented wheel in this research utilizes a pressure pad to sense load and sinkage, a string potentiometer to measure slip, and records motor current draw. This thesis demonstrates the utilization of the instrumented wheel\u27s data to estimate cohesion, angle of friction and grain size and demonstrates a machine learning solution for classifying terrain types with the same data. Mars simulants available at NASA-JPL were used for the collection of the data. Two machine learning classifiers were explored: Random Forest and Support Vector Machine. Binary and multi-class classification were both demonstrated and it is proposed that the classification model can identify terrain types based on the instrumented wheel data. The Random Forest model performed best in all classification types

In Situ Soil Property Estimation for Autonomous Earthmoving Using Physics-Infused Neural Networks

A novel, learning-based method for in situ estimation of soil properties using a physics-infused neural network (PINN) is presented. The network is trained to produce estimates of soil cohesion, angle of internal friction, soil-tool friction, soil failure angle, and residual depth of cut which are then passed through an earthmoving model based on the fundamental equation of earthmoving (FEE) to produce an estimated force. The network ingests a short history of kinematic observations along with past control commands and predicts interaction forces accurately with average error of less than 2kN, 13% of the measured force. To validate the approach, an earthmoving simulation of a bladed vehicle is developed using Vortex Studio, enabling comparison of the estimated parameters to pseudo-ground-truth values which is challenging in real-world experiments. The proposed approach is shown to enable accurate estimation of interaction forces and produces meaningful parameter estimates even when the model and the environmental physics deviate substantially.Comment: 10 pages, 6 figures, to be published in proceedings of 16th European-African Regional Conference of the International Society for Terrain-Vehicle Systems (ISTVS

Modeling and Control of the UGV Argo J5 with a Custom-Built Landing Platform

This thesis aims to develop a detailed dynamic model and implement several navigation controllers for path tracking and dynamic self-leveling of the Argo J5 Unmanned Ground Vehicle (UGV) with a custom-built landing platform. The overall model is derived by combining the Argo J5 driveline system with the wheelsterrain interaction (using terramechanics theory and mobile robot kinetics), while the landing platform model follows the Euler-Lagrange formulation. Different controllers are, then, derived, implemented to demonstrate: i.) self-leveling accuracy of the landing platform, ii.) trajectory tracking capabilities of the Argo J5 when moving in uneven terrains. The novelty of the Argo J5 model is the addition of a vertical load on each wheel through derivation of the shear stress depending on the point’s position in 3D space on each wheel. Static leveling of the landing platform within one degree of the horizon is evaluated by implementing Proportional Derivative (PD), Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR), feedback linearization, and Passivity Based Adaptive Controller (PBAC) techniques. A PD controller is used to evaluate the performance of the Argo J5 on different terrains. Further, for the Argo J5 - landing platform ensemble, PBAC and Neural Network Based Adaptive Controller (NNBAC) are derived and implemented to demonstrate dynamic self-leveling. The emphasis is on different controller implementation for complex real systems such as Argo J5 - Landing platform. Results, obtained via extensive simulation studies in a Matlab/Simulink environment that consider real system parameters and hardware limitations, contribute to understanding navigation performance in a variety of terrains with unknown properties and illustrate the Argo J5 velocity, wheel rolling resistance, wheel turning resistance and shear stress on different terrains

PHYSICS-BASED AND DATA-DRIVEN MODELING OF HYBRID ROBOT MOVEMENT ON SOFT TERRAIN

Navigating an unmapped environment is one of the ten biggest challenges facing the robotics community. Wheeled robots can move fast on flat surfaces but suffer from loss of traction and immobility on soft ground. However, legged machines have superior mobility over wheeled locomotion when they are in motion over flowable ground or a terrain with obstacles but can only move at relatively low speeds on flat surfaces. A question to answer is as follows: If legged and wheeled locomotion are combined, can the resulting hybrid leg-wheel locomotion enable fast movement in any terrain condition? To investigate the rich physics during dynamic interactions between a robot and a granular terrain, a physics-based computational framework based on the smoothed particle hydrodynamics (SPH) method has been developed and validated by using experimental results for single robot appendage interaction with the granular system. This framework has been extended and coupled with a multi-body simulator to model different robot configurations. Encouraging agreement is found amongst the numerical, theoretical, and experimental results, for a wide range of robot leg configurations, such as curvature and shape. Real-time navigation in a challenging terrain requires fast prediction of the dynamic response of the robot, which is useful for terrain identification and robot gait adaption. Therefore, a data-driven modeling framework has also been developed for the fast estimation of the slippage and sinkage of robots. The data-driven model leverages the high-quality data generated from the offline physics-based simulation for the training of a deep neural network constructed from long short-term memory (LSTM) cells. The results are expected to form a good basis for online robot navigation and exploration in unknown and complex terrains