Methods for Wheel Slip and Sinkage Estimation in Mobile Robots

Future outdoor mobile robots will have to explore larger and larger areas, performing difficult tasks, while preserving, at the same time, their safety. This will primarily require advanced sensing and perception capabilities. Video sensors supply contact-free, precise measurements and are flexible devices that can be easily integrated with multi-sensor robotic platforms. Hence, they represent a potential answer to the need of new and improved perception capabilities for autonomous vehicles. One of the main applications of vision in mobile robotics is localization. For mobile robots operating on rough terrain, conventional dead reckoning techniques are not well suited, since wheel slipping, sinkage, and sensor drift may cause localization errors that accumulate without bound during the vehicle’s travel. Conversely, video sensors are exteroceptive devices, that is, they acquire information from the robot’s environment; therefore, vision-based motion estimates are independent of the knowledge of terrain properties and wheel-terrain interaction. Indeed, like dead reckoning, vision could lead to accumulation of errors; however, it has been proved that, compared to dead reckoning, it allows more accurate results and can be considered as a promising solution to the problem of robust robot positioning in high-slip environments. As a consequence, in the last few years, several localization methods using vision have been developed. Among them, visual odometry algorithms, based on the tracking of visual features over subsequent images, have been proved particularly effective. Accurate and reliable methods to sense slippage and sinkage are also desirable, since these effects compromise the vehicle’s traction performance, energy consumption and lead to gradual deviation of the robot from the intended path, possibly resulting in large drift and poor results of localization and control systems. For example, the use of conventional dead-reckoning technique is largely compromised, since it is based on the assumption that wheel revolutions can be translated into correspondent linear displacements. Thus, if one wheel slips, then the associated encoder will register revolutions even though these revolutions do not correspond to a linear displacement of the wheel. Conversely, if one wheel skids, fewer encoder pulses will be counted. Slippage and sinkage measurements are also valuable for terrain identification according to the classical terramechanics theory. This chapter investigates vision-based onboard technology to improve mobility of robots on natural terrain. A visual odometry algorithm and two methods for online measurement of vehicle slip angle and wheel sinkage, respectively, are discussed. Tests results are presented showing the performance of the proposed approaches using an all-terrain rover moving across uneven terrain

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

Adaptive and intelligent navigation of autonomous planetary rovers - A survey

The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors

Planetary Rover Inertial Navigation Applications: Pseudo Measurements and Wheel Terrain Interactions

Accurate localization is a critical component of any robotic system. During planetary missions, these systems are often limited by energy sources and slow spacecraft computers. Using proprioceptive localization (e.g., using an inertial measurement unit and wheel encoders) without external aiding is insufficient for accurate localization. This is mainly due to the integrated and unbounded errors of the inertial navigation solutions and the drifted position information from wheel encoders caused by wheel slippage. For this reason, planetary rovers often utilize exteroceptive (e.g., vision-based) sensors. On the one hand, localization with proprioceptive sensors is straightforward, computationally efficient, and continuous. On the other hand, using exteroceptive sensors for localization slows rover driving speed, reduces rover traversal rate, and these sensors are sensitive to the terrain features. Given the advantages and disadvantages of both methods, this thesis focuses on two objectives. First, improving the proprioceptive localization performance without significant changes to the rover operations. Second, enabling adaptive traversability rate based on the wheel-terrain interactions while keeping the localization reliable. To achieve the first objective, we utilized the zero-velocity, zero-angular rate updates, and non-holonomicity of a rover to improve rover localization performance even with the limited available sensor usage in a computationally efficient way. Pseudo-measurements generated from proprioceptive sensors when the rover is stationary conditions and the non-holonomic constraints while traversing can be utilized to improve the localization performance without any significant changes to the rover operations. Through this work, it is observed that a substantial improvement in localization performance, without the aid of additional exteroceptive sensor information. To achieve the second objective, the relationship between the estimation of localization uncertainty and wheel-terrain interactions through slip-ratio was investigated. This relationship was exposed with a Gaussian process with time series implementation by using the slippage estimation while the rover is moving. Then, it is predicted when to change from moving to stationary conditions by mapping the predicted slippage into localization uncertainty prediction. Instead of a periodic stopping framework, the method introduced in this work is a slip-aware localization method that enables the rover to stop more frequently in high-slip terrains whereas stops rover less frequently for low-slip terrains while keeping the proprioceptive localization reliable

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

On Advanced Mobility Concepts for Intelligent Planetary Surface Exploration

Surface exploration by wheeled rovers on Earth's Moon (the two Lunokhods) and Mars (Nasa's Sojourner and the two MERs) have been followed since many years already very suc-cessfully, specifically concerning operations over long time. However, despite of this success, the explored surface area was very small, having in mind a total driving distance of about 8 km (Spirit) and 21 km (Opportunity) over 6 years of operation. Moreover, ESA will send its ExoMars rover in 2018 to Mars, and NASA its MSL rover probably this year. However, all these rovers are lacking sufficient on-board intelligence in order to overcome longer dis-tances, driving much faster and deciding autonomously on path planning for the best trajec-tory to follow. In order to increase the scientific output of a rover mission it seems very nec-essary to explore much larger surface areas reliably in much less time. This is the main driver for a robotics institute to combine mechatronics functionalities to develop an intelligent mo-bile wheeled rover with four or six wheels, and having specific kinematics and locomotion suspension depending on the operational terrain of the rover to operate. DLR's Robotics and Mechatronics Center has a long tradition in developing advanced components in the field of light-weight motion actuation, intelligent and soft manipulation and skilled hands and tools, perception and cognition, and in increasing the autonomy of any kind of mechatronic systems. The whole design is supported and is based upon detailed modeling, optimization, and simula-tion tasks. We have developed efficient software tools to simulate the rover driveability per-formance on various terrain characteristics such as soft sandy and hard rocky terrains as well as on inclined planes, where wheel and grouser geometry plays a dominant role. Moreover, rover optimization is performed to support the best engineering intuitions, that will optimize structural and geometric parameters, compare various kinematics suspension concepts, and make use of realistic cost functions like mass and consumed energy minimization, static sta-bility, and more. For self-localization and safe navigation through unknown terrain we make use of fast 3D stereo algorithms that were successfully used e.g. in unmanned air vehicle ap-plications and on terrestrial mobile systems. The advanced rover design approach is applica-ble for lunar as well as Martian surface exploration purposes. A first mobility concept ap-proach for a lunar vehicle will be presented

Study of Mobile Robot Operations Related to Lunar Exploration

Mobile robots extend the reach of exploration in environments unsuitable, or unreachable, by humans. Far-reaching environments, such as the south lunar pole, exhibit lighting conditions that are challenging for optical imagery required for mobile robot navigation. Terrain conditions also impact the operation of mobile robots; distinguishing terrain types prior to physical contact can improve hazard avoidance. This thesis presents the conclusions of a trade-off that uses the results from two studies related to operating mobile robots at the lunar south pole. The lunar south pole presents engineering design challenges for both tele-operation and lidar-based autonomous navigation in the context of a near-term, low-cost, short-duration lunar prospecting mission. The conclusion is that direct-drive tele-operation may result in improved science data return. The first study is on demonstrating lidar reflectance intensity, and near-infrared spectroscopy, can improve terrain classification over optical imagery alone. Two classification techniques, Naive Bayes and multi-class SVM, were compared for classification errors. Eight terrain types, including aggregate, loose sand and compacted sand, are classified using wavelet-transformed optical images, and statistical values of lidar reflectance intensity. The addition of lidar reflectance intensity was shown to reduce classification errors for both classifiers. Four types of aggregate material are classified using statistical values of spectral reflectance. The addition of spectral reflectance was shown to reduce classification errors for both classifiers. The second study is on human performance in tele-operating a mobile robot over time-delay and in lighting conditions analogous to the south lunar pole. Round-trip time delay between operator and mobile robot leads to an increase in time to turn the mobile robot around obstacles or corners as operators tend to implement a `wait and see\u27 approach. A study on completion time for a cornering task through varying corridor widths shows that time-delayed performance fits a previously established cornering law, and that varying lighting conditions did not adversely affect human performance. The results of the cornering law are interpreted to quantify the additional time required to negotiate a corner under differing conditions, and this increase in time can be interpreted to be predictive when operating a mobile robot through a driving circuit

Effect of gravity in wheel/terrain interaction models

[Abstract] Predicting the motion of wheeled robots in unstructured environments is an important and challenging problem. The study of planetary exploration rovers on soft terrain introduces the additional need to consider the effect of non-terrestrial gravitational fields on the forces and torques developed at the wheel/terrain interface. Simply reducing the wheel load under earth gravity overestimates the travelled distance and predicts better performance than is actually observed in reduced-gravity measurements. In this paper, we study the effect of gravity on wheel/terrain interaction. Experiments were conducted to assess the effect of reduced gravity on the velocity profile of the soil under the wheel, as well as on the traction force and sinkage developed by the wheel. It was shown that in the velocity field of the soil, the decay of the tangential velocity component becomes gradual with reducing gravity, and the decay of the normal to rim velocity is slower in Lunar gravity. It was also found that wheel flexibility can have an important effect on the dynamics as the contact patch and effective radius varies periodically. These results were then used together with traditional semi-empirical terramechanics models to determine and validate the simulated drawbar pull values. The developed simulation model includes the effect of wheel flexibility, dynamic sinkage and gravity.MINECO; RYC-2016-2022