Learning and prediction of slip from visual information

Slip Estimation for Mars Rover Zhurong Based on Data Drive

China’s Mars rover Zhurong successfully landed on Mars on 15th May 2021, and it is currently conducting an exploration mission on the Red Planet. This paper develops slip estimation models for the Mars rover Zhurong based on the data drive approach. Data were obtained by Zhurong’s validator ground indoor tests, and the test site was equipped with a low-gravity simulation device and simulated Mars soil to simulate the Mars conditions as much as possible. The obtained slip models trained by BP and GA-BP algorithms were applied to estimate Zhurong’s longitudinal (slip_x) and lateral slip (slip_y) on Mars, and the slip estimation values were used to display Zhurong’s actual driving path. The analyzed results prove that the GA-BP slip models perform better than the BP models, and can both be applied for correcting Zhurong’s path. The proposed models have high potential in guiding the path planning and monitoring of the slip for the Mars rover Zhurong.

Terrain-Dependent Slip Risk Prediction for Planetary Exploration Rovers

Wheel slip prediction on rough terrain is crucial for secure, long-term operations of planetary exploration rovers. Although rough, unstructured terrain hampers mobility, prediction by modeling wheel–terrain interactions remains difficult owing to unclear terrain conditions and complexities of terramechanics models. This study proposes a vision-based approach with machine learning for predicting wheel slip risk by estimating the slope from 3D information and classifying terrain types from image information. It considers the slope estimation accuracy for risk prediction under sharp increases in wheel slip due to inclined ground. Experimental results obtained with a rover testbed on several terrain types validate this method.

On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions.

On the Road: Route Proposal from Radar Self-Supervised by Fuzzy LiDAR Traversability

This is motivated by a requirement for robust, autonomy-enabling scene understanding in unknown environments. In the method proposed in this paper, discriminative machine-learning approaches are applied to infer traversability and predict routes from Frequency-Modulated Contunuous-Wave (FMCV) radar frames. Firstly, using geometric features extracted from LiDAR point clouds as inputs to a fuzzy-logic rule set, traversability pseudo-labels are assigned to radar frames from which weak supervision is applied to learn traversability from radar. Secondly, routes through the scanned environment can be predicted after they are learned from the odometry traces arising from traversals demonstrated by the autonomous vehicle (AV). In conjunction, therefore, a model pretrained for traversability prediction is used to enhance the performance of the route proposal architecture. Experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community. Our key finding is that joint learning of traversability and demonstrated routes lends itself best to a model which understands where the vehicle should feasibly drive. We show that the traversability characteristics can be recovered satisfactorily, so that this recovered representation can be used in optimal path planning, and that an end-to-end formulation including both traversability feature extraction and routes learned by expert demonstration recovers smooth, drivable paths that are comprehensive in their coverage of the underlying road network. We conclude that the proposed system will find use in enabling mapless vehicle autonomy in extreme environments.

Terrain Estimation for Planetary Exploration Robots

A planetary exploration rover’s ability to detect the type of supporting surface is critical to the successful accomplishment of the planned task, especially for long-range and long-duration missions. This paper presents a general approach to endow a robot with the ability to sense the terrain being traversed. It relies on the estimation of motion states and physical variables pertaining to the interaction of the vehicle with the environment. First, a comprehensive proprioceptive feature set is investigated to evaluate the informative content and the ability to gather terrain properties. Then, a terrain classifier is developed grounded on Support Vector Machine (SVM) and that uses an optimal proprioceptive feature set. Following this rationale, episodes of high slippage can be also treated as a particular terrain type and detected via a dedicated classifier. The proposed approach is tested and demonstrated in the field using SherpaTT rover, property of DFKI (German Research Center for Artificial Intelligence), that uses an active suspension system to adapt to terrain unevenness.

The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset

Future exploratory missions to the Moon and to Mars will involve solar-powered rovers; careful vehicle energy management is critical to the success of such missions. This article describes a unique dataset gathered by a small, four-wheeled rover at a planetary analog test facility in Canada. The rover was equipped with a suite of sensors designed to enable the study of energy-aware navigation and path planning algorithms. The sensors included a colour omnidirectional stereo camera, a monocular camera, an inertial measurement unit, a pyranometer, drive power consumption monitors, wheel encoders, and a GPS receiver. In total, the rover drove more than 1.2 km over varied terrain at the analog test site. All data is presented in human-readable text files and as standard-format images; additional Robot Operating System (ROS) parsing tools and several georeferenced aerial maps of the test environment are also included. A series of potential research use cases is described.

Wheel Slip Classification Method for Mobile Robot in Sandy Terrain Using In-Wheel Sensor

This paper proposes a method that can estimate and classify the magnitude of wheel slippage for a mobile robot in sandy terrains. The proposed method exploits a sensor suite, called an in-wheel sensor, which measures the normal force and contact angle at the wheel-sand interaction boundary. An experimental test using the in-wheel sensor reveals that the maximum normal force and exit angle of the wheel explicitly vary with the magnitude of the wheel slippage. These characteristics are then fed into a machine learning algorithm, which classifies the wheel slippage into three categories: non-stuck wheel, quasi-stuck wheel, and stuck wheel. The usefulness of the proposed method for slip classification is experimentally evaluated using a four-wheel-drive test bed rover.

Autonomy for ground-level robotic space exploration: framework, simulation, architecture, algorithms and experiments

Robotic surface planetary exploration is a challenging endeavor, with critical safety requirements and severe communication constraints. Autonomous navigation is one of the most crucial and yet risky aspects of these operations. Therefore, a certain level of local autonomy for onboard robots is an essential feature, so that they can make their own decisions independently of ground control, reducing operational costs and maximizing the scientific return of the mission. In addition, existing tools to support research in this domain are usually proprietary to space agencies, and out of reach of most researchers. This paper presents a framework developed to support research in this field, a modular onboard software architecture design and a series of algorithms that implement a visual-based autonomous navigation approach for robotic space exploration. It allows analysis of algorithms' performance and functional validation of approaches and autonomy strategies, data monitoring and the creation of simulation models to replicate the vehicle, sensors, terrain and operational conditions. The framework and algorithms are partly supported by open-source packages and tools. A set of experiments and field testing with a physical robot and hardware are described as well, detailing results and algorithms' processing time, which experience an incremented of one order of magnitude when executed in space-certified like hardware, with constrained resources, in comparison to using general purpose hardware.

Combined visual odometry and visual compass for off-road mobile robots localization

In this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.

Non-parametric Learning to Aid Path Planning over Slopes

In this paper we address the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric learning technique is presented to generate a mobility representation where the maximum feasible speed is used as a criterion to classify the world. The inputs to the algorithm are terrain gradients derived from an elevation map and past observations of wheel slip. It is argued that such a representation can aid in path planning with improved selection of vehicle heading and velocity in off-road slopes. In addition, an information theoretic test is proposed to validate a chosen proprioceptive representation (such as slip) for mobility map generation. Results of mobility map generation and its benefits to path planning are shown.