Development Environment for Optimized Locomotion System of Planetary Rovers

This paper addresses the first steps that have been undergone to set up the development environement w.r.t optimization and to modelling and simulation of overall dynamics of the rover driving behaviour under all critical surface terrains, like soft and hard soils, slippage, bulldozing effect and digging in soft soil. Optimization is based on MOPS (Multi-Objective Prameter Synthesis), that is capable for handling several objective functions such as mass reduction, motor power reduction, increase of traction forces, rover stability guarantee, and more. The tool interferes with Matlab/Simulink and with Modelica/Dymola for dynamics model implementation. For modelling and simulation of the overall rover dynamics and terramechanical behaviour in all kind of soils we apply a Matlab based tool that takes advantage of the multibody dynamics tool Simpack. First results of very promising rover optimizations 6 wheels are presented that improve ExoMars rover type wheel suspension systems. Performance of driveability behaviour in different soils is presented as well. The next steps are discusses in order to achieve the planned overall development environment

Adams-Based Rover Terramechanics and Mobility Simulator - ARTEMIS

The Mars Exploration Rovers (MERs), Spirit and Opportunity, far exceeded their original drive distance expectations and have traveled, at the time of this reporting, a combined 29 kilometers across the surface of Mars. The Rover Sequencing and Visualization Program (RSVP), the current program used to plan drives for MERs, is only a kinematic simulator of rover movement. Therefore, rover response to various terrains and soil types cannot be modeled. Although sandbox experiments attempt to model rover-terrain interaction, these experiments are time-intensive and costly, and they cannot be used within the tactical timeline of rover driving. Imaging techniques and hazard avoidance features on MER help to prevent the rover from traveling over dangerous terrains, but mobility issues have shown that these methods are not always sufficient. ARTEMIS, a dynamic modeling tool for MER, allows planned drives to be simulated before commands are sent to the rover. The deformable soils component of this model allows rover-terrain interactions to be simulated to determine if a particular drive path would take the rover over terrain that would induce hazardous levels of slip or sink. When used in the rover drive planning process, dynamic modeling reduces the likelihood of future mobility issues because high-risk areas could be identified before drive commands are sent to the rover, and drives planned over these areas could be rerouted. The ARTEMIS software consists of several components. These include a preprocessor, Digital Elevation Models (DEMs), Adams rover model, wheel and soil parameter files, MSC Adams GUI (commercial), MSC Adams dynamics solver (commercial), terramechanics subroutines (FORTRAN), a contact detection engine, a soil modification engine, and output DEMs of deformed soil. The preprocessor is used to define the terrain (from a DEM) and define the soil parameters for the terrain file. The Adams rover model is placed in this terrain. Wheel and soil parameter files can be altered in the respective text files. The rover model and terrain are viewed in Adams View, the GUI for ARTEMIS. The Adams dynamics solver calls terramechanics subroutines in FORTRAN containing the Bekker-Wong equations

A system architecture for a planetary rover

Each planetary mission requires a complex space vehicle which integrates several functions to accomplish the mission and science objectives. A Mars Rover is one of these vehicles, and extends the normal spacecraft functionality with two additional functions: surface mobility and sample acquisition. All functions are assembled into a hierarchical and structured format to understand the complexities of interactions between functions during different mission times. It can graphically show data flow between functions, and most importantly, the necessary control flow to avoid unambiguous results. Diagrams are presented organizing the functions into a structured, block format where each block represents a major function at the system level. As such, there are six blocks representing telecomm, power, thermal, science, mobility and sampling under a supervisory block called Data Management/Executive. Each block is a simple collection of state machines arranged into a hierarchical order very close to the NASREM model for Telerobotics. Each layer within a block represents a level of control for a set of state machines that do the three primary interface functions: command, telemetry, and fault protection. This latter function is expanded to include automatic reactions to the environment as well as internal faults. Lastly, diagrams are presented that trace the system operations involved in moving from site to site after site selection. The diagrams clearly illustrate both the data and control flows. They also illustrate inter-block data transfers and a hierarchical approach to fault protection. This systems architecture can be used to determine functional requirements, interface specifications and be used as a mechanism for grouping subsystems (i.e., collecting groups of machines, or blocks consistent with good and testable implementations)

Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems

We present a light-weight body-terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst-case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments

Slip prediction using visual information

This paper considers prediction of slip from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering a particular terrain can be very useful for better planning and avoiding terrains with large slip. The proposed method is based on learning from experience and consists of terrain type recognition and nonlinear regression modeling. After learning, slip prediction is done remotely using only the visual information as input. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The slip prediction error is about 20% of the step size

Learning to Predict Slip for Ground Robots

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicle's own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting trapped before entering a particular terrain. For example, a planning algorithm can utilize slip information by taking into consideration that a slippery terrain is costly or hazardous to traverse. A generic nonlinear regression framework is proposed in which the terrain type is determined from appearance and then a nonlinear model of slip is learned for a particular terrain type. In this paper we focus only on the latter problem and provide slip learning and prediction results for terrain types, such as soil, sand, gravel, and asphalt. The slip prediction error achieved is about 15% which is comparable to the measurement errors for slip itself

System Design, Motion Modelling and Planning for a Recon figurable Wheeled Mobile Robot

Over the past ve decades the use of mobile robotic rovers to perform in-situ scienti c investigations on the surfaces of the Moon and Mars has been tremendously in uential in shaping our understanding of these extraterrestrial environments. As robotic missions have evolved there has been a greater desire to explore more unstructured terrain. This has exposed mobility limitations with conventional rover designs such as getting stuck in soft soil or simply not being able to access rugged terrain. Increased mobility and terrain traversability are key requirements when considering designs for next generation planetary rovers. Coupled with these requirements is the need to autonomously navigate unstructured terrain by taking full advantage of increased mobility. To address these issues, a high degree-of-freedom recon gurable platform that is capable of energy intensive legged locomotion in obstacle-rich terrain as well as wheeled locomotion in benign terrain is proposed. The complexities of the planning task that considers the high degree-of-freedom state space of this platform are considerable. A variant of asymptotically optimal sampling-based planners that exploits the presence of dominant sub-spaces within a recon gurable mobile robot's kinematic structure is proposed to increase path quality and ensure platform safety. The contributions of this thesis include: the design and implementation of a highly mobile planetary analogue rover; motion modelling of the platform to enable novel locomotion modes, along with experimental validation of each of these capabilities; the sampling-based HBFMT* planner that hierarchically considers sub-spaces to better guide search of the complete state space; and experimental validation of the planner with the physical platform that demonstrates how the planner exploits the robot's capabilities to uidly transition between various physical geometric con gurations and wheeled/legged locomotion modes

Path Planning for Reconfigurable Rovers in Planetary Exploration

This paper introduces a path planning algorithm that takes into consideration different locomotion modes in a wheeled reconfigurable rover. Power consumption and traction are estimated by means of simplified dynamics models for each locomotion mode. In particular, wheel-walking and normaldriving are modeled for a planetary rover prototype. These models are then used to define the cost function of a path planning algorithm based on fast marching. It calculates the optimal path, in terms of power consumption, between two positions, providing the most appropriate locomotion mode to be used at each position. Finally, the path planning algorithm was implemented in V-REP simulation software and a Martian area was used to validate it. Results of this contribution also demonstrate how the use of these locomotion modes would reduce the power consumption for a particular area.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

XTerramechanics: Integrated Simulation of Planetary Surface Missions

Are there contemporary habitats elsewhere in the solar system with necessary conditions, organic matter, water, energy, and nutrients to support or sustain life. Are there habitats that have experienced conditions similar to those on Earth when life emerged ,an abode of possible lifelong past. Mars and Europa(Jupiter’s icy moon)have been identified as the most relevant and immediate in the quest to answer these questions. Beyond Mars and Europa, every celestial body of interest appears to have its own geological history and every new discovery accentuates the overall complexity of our solar system. The exploration of Mars and Europa, and others, both remotely and in situ, is a central priority as part of NASA’s current and future goals for understanding the building of new worlds, the requirements for planetary habitats, and the workings of the solar system

Planetary rovers and data fusion

This research will investigate the problem of position estimation for planetary rovers. Diverse algorithmic filters are available for collecting input data and transforming that data to useful information for the purpose of position estimation process. The terrain has sandy soil which might cause slipping of the robot, and small stones and pebbles which can affect trajectory. The Kalman Filter, a state estimation algorithm was used for fusing the sensor data to improve the position measurement of the rover. For the rover application the locomotion and errors accumulated by the rover is compensated by the Kalman Filter. The movement of a rover in a rough terrain is challenging especially with limited sensors to tackle the problem. Thus, an initiative was taken to test drive the rover during the field trial and expose the mobile platform to hard ground and soft ground(sand). It was found that the LSV system produced speckle image and values which proved invaluable for further research and for the implementation of data fusion. During the field trial,It was also discovered that in a at hard surface the problem of the steering rover is minimal. However, when the rover was under the influence of soft sand the rover tended to drift away and struggled to navigate. This research introduced the laser speckle velocimetry as an alternative for odometric measurement. LSV data was gathered during the field trial to further simulate under MATLAB, which is a computational/mathematical programming software used for the simulation of the rover trajectory. The wheel encoders came with associated errors during the position measurement process. This was observed during the earlier field trials too. It was also discovered that the Laser Speckle Velocimetry measurement was able to measure accurately the position measurement but at the same time sensitivity of the optics produced noise which needed to be addressed as error problem. Though the rough terrain is found in Mars, this paper is applicable to a terrestrial robot on Earth. There are regions in Earth which have rough terrains and regions which are hard to measure with encoders. This is especially true concerning icy places like Antarctica, Greenland and others. The proposed implementation for the development of the locomotion system is to model a system for the position estimation through the use of simulation and collecting data using the LSV. Two simulations are performed, one is the differential drive of a two wheel robot and the second involves the fusion of the differential drive robot data and the LSV data collected from the rover testbed. The results have been positive. The expected contributions from the research work includes a design of a LSV system to aid the locomotion measurement system. Simulation results show the effect of different sensors and velocity of the robot. The kalman filter improves the position estimation process