Planetary Rovers Research Articles

LiDAR-Inertial SLAM with DEM-driven Global Constraints for Planetary Rover Exploration

LiDAR-Inertial SLAM with DEM-driven Global Constraints for Planetary Rover Exploration

Review Paper on Agricultural Terrain Traversal Rocker Bogie Mechanism

Abstract: This paper details the design and implementation of a terrain traversal system leveraging the rocker-bogie mechanism, specifically tailored for agricultural applications where uneven terrain presents significant challenges. The rocker-bogie suspension system, renowned for its application in planetary rovers, is innovatively adapted to navigate the complexities of agricultural fields. The primary objective of this research is to create a prototype that minimizes soil disturbance while effectively maneuvering over obstacles such as rocks, furrows, and varying soil conditions. Through this approach, the prototype aims to enhance operational efficiency in critical agricultural tasks, including planting, monitoring, and maintaining crops. The system's design incorporates advanced navigation capabilities, potentially integrating sensors for obstacle detection and GPS for precise positioning. This research addresses the pressing need for low-cost, scalable solutions in the realm of agricultural mechanization, ultimately contributing to improved productivity and reduced labor costs in farming operations. The findings from this study are anticipated to provide significant insights into the future of agricultural technology, paving the way for further innovations in automated systems that can adapt to the dynamic challenges of modern agriculture. The successful deployment of this prototype not only underscores the feasibility of the rocker-bogie mechanism in agricultural settings but also highlights its potential to revolutionize practices in the industry.

Current-Based Analysis and Validation of a Wheel–Soil Interaction Model for the Trafficability of a Planetary Rover

The assessment of trafficability for planetary rovers in relation to non-geometric hazards is a crucial issue in deep space exploration. This study relies on terramechanics theory and incorporates actual data from Mars soil and rover parameters to develop a model that accurately represents the interaction between the rover’s wheels and Martian soil. Through numerical simulations, this model specifically investigates the relationship between the current of the rover’s wheel drive motor and factors such as slip ratio, soil pressure parameters, and soil shear parameters. Terrestrial experiments are also conducted to verify the precision of certain numerical calculations. The proposed wheel–soil interaction model, based on wheel motor current, provides a foundation for assessing non-geometric trafficability and the inversion of planetary soil parameters.

Semantic Terrain Traversability Analysis Based on Deep Learning Aimed at Planetary Rover Navigation

Semantic Terrain Traversability Analysis Based on Deep Learning Aimed at Planetary Rover Navigation

Landmark-aware autonomous odometry correction and map pruning for planetary rovers

Planetary rover autonomous localization is paramount for a planetary surface exploration mission. However, existing methods demonstrate limited localization accuracy, mostly due to the unstructured texture characterization of planetary surface. In response, this study presents a novel Neural Radiance Field (NeRF) driven visual odometry correction method that allows for high-precision 6-DoF rover pose estimation and local map pruning. First, an innovative image saliency evaluation approach, combining binarization and feature detection, is introduced to meticulously select landmarks that are conducive to rover re-localization. Subsequently, we conduct 3D reconstruction and rendering of the chosen landmarks based on a-priori knowledge of planetary surface images and their Neural Radiance Field (NeRF) models. High-precision odometry correction is achieved through the optimization of photometric loss between NeRF rending images and real images. Simultaneously, the odometry correction mechanism is employed in an autonomous manner to refine the NeRF model of the corresponding landmark, leading to an improved local map and gradually enhanced rover localization accuracy. Numerical simulation and experiment trials are carried out to evaluate the performance of the proposed method, results of which demonstrate state-of-the-art rover re-localization accuracy and local map pruning.

State Analysis and Emergency Control of Planetary Rover with Faulty Drive Wheel

Wheel failure is one of the worst problems for a planetary rover working on Mars or the Moon, which may lead to the interruption of the exploration mission and even the loss of mobility. In this study, a driving test of a planetary rover prototype with a faulty drive wheel was conducted, and state analysis and dynamics modeling were carried out. The drag motion relationship between the faulty drive wheel and the normal wheels on the same suspension was established based on the targeted single wheel test (faulty wheel-soil bin). In order to maintain the subsequent basic detection capability of the planetary rover, an emergency control system is proposed that integrates the path planning strategy with faulty wheel priority and the motion control method of correcting heading and coordinating allocation. The experimental results and emergency strategies of this study on simulating Martian soil and terrain can provide researchers with ideas to solve such problems.

Semi-empirical terramechanics modelling of rough terrain represented by a height field

Dynamic simulations of various types of off-road vehicles, from planetary rovers to agricultural equipment, have long relied on well-established semi-empirical terramechanics models. While these models do have drawbacks and reliability issues that have been addressed by numerous works in the decades since the models were first introduced, semi-empirical approaches remain one of the few ways to simulate realistic wheel-soil interaction in real-time. One of their drawbacks is their assumption that the terrain is a flat plane. The models work by integrating normal and shear stresses along the wheel-terrain contact patch. The normal stress at each point along the contact patch is determined using an equation that computes soil pressure based on semi-empirical parameters, the dimensions of the wheel and the sinkage, which is determined based on the distance between the point and the plane that defines the terrain. Other works simplify the rough terrain contact problem by defining an equivalent contact plane at each time step in order to continue to be able to use semi-empirical models - modified to work with slanted planes - to compute the interaction forces. In this work, we propose a new, modified version of the semi-empirical model in which interaction forces for a wheel travelling on rough terrain can be computed without the need to use an equivalent contact plane. To highlight the advantages of our proposed approach, we compare our simulation results to the results of simulations using an existing approach for modelling a wheel travelling over rough terrain using traditional semi-empirical models.

An innovative joint-space multi-axis dynamics modeling theory for planetary rover

To address high degree of freedom planetary rover modeling and enhance dynamic performance, an innovative joint-space multi-axis dynamics modeling theory is proposed. The approach aims to streamline the dynamic modeling process by introducing concepts such as topology, natural coordinate system and kinematic iterations. Through an analysis of closed subtrees’ properties and characteristics, an improved Lagrange equation with topology-operable properties is derived and presented. Subsequently, by analyzing the backward iteration process of external forces and torque and eliminating redundant variables, canonical dynamic equations are derived. These equations feature full parameterized modeling and explicit expression. The correctness and accuracy of this method are validated through dynamic simulations of the Mars rover. The proposed method offers the advantage of significantly reducing modeling and calculation complexity, as demonstrated through comparisons with other mature dynamics methods.

Subspace graph networks for real-time granular flow simulation with applications to machine-terrain interactions

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 6×10−5 and 3×10−6 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.

Towing an Object With a Rover

Abstract The present study investigates the problem of towing an object that is lying on a surface in a given workspace and the applicability to a planetary rover with four steering wheels. A quasi-static method has been introduced and used for path planning and for the synthesis of both object and rover trajectories. The rover uses a tether as the towing medium which is modeled as an elastic unilateral constraint. Moreover, a kinematic model of the rover which includes steering asymmetrical joint limits is taken into account. The dynamics model of the overall system is then derived and a sensitivity analysis is performed over a finite number of different trajectories, in order to evaluate the quasi-static assumption, the effects of the model, and the influence of the elastic constraint. Finally, experiments have been performed using the novel Archimede rover prototype and compared with dynamics simulations; the remarkable adherence shown with the model validates the overall approach.

Uncertainty-Aware Trajectory Planning: Using Uncertainty Quantification and Propagation in Traversability Prediction of Planetary Rovers

Uncertainty-Aware Trajectory Planning: Using Uncertainty Quantification and Propagation in Traversability Prediction of Planetary Rovers

An online optimization escape entrapment strategy for planetary rovers based on Bayesian optimization

AbstractPlanetary rovers may become stuck due to the soft terrain on Mars and other planetary surface. The escape entrapment control strategy is of great significance for planetary rover traversing loosely consolidated granular terrain. After analyzing the performance of the published quadrupedal rotary sequence gait, a “sweeping‐spinning” gait was proposed to improve escape entrapment capability. And the forward distance of planetary rovers with “sweeping‐spinning” gait was modeled as a function of six control parameters. An online optimization escape entrapment strategy for planetary rover was proposed based on the Bayesian Optimization algorithm. Single‐factor experiments were conducted to investigate the effect of each control parameter on forward distance, and determine the parameter ranges. The average forward distance with randomly selected control parameters is 89.64 cm, while that is 136.93 cm with Bayesian optimized control parameters, which verifies the effectiveness of the escape entrapment strategy. Moreover, compared with the trajectory of a planetary rover prototype with the published quadrupedal rotary sequence gait, the trajectory of a planetary rover prototype with “sweeping‐spinning” gait is more accurate. Furthermore, the online estimated equivalent terrain mechanical parameters can be used to determine the running state of the planetary rover prototype, which was verified using experiments.

An Autonomous Navigation Method for Planetary Rover Based on Multi-modal Fusion and Multi-factor Graph Optimization

Planetary surface is a complex unstructured environment with strong light, strong shadows, weak textures and numerous obstacles. There is no GPS signal support and satellite networking support on the planetary surface. The limited computer and sensor capabilities of planetary rover have a significant gap from the ground autonomous driving configuration, which presents a great challenge to the autonomous navigation methods of planetary rover. Extreme lighting conditions will increase the camera mismatching rate, and poor texture will increase the lidar matching error, which will reduce the estimation accuracy. Moreover, camera and lidar produce great motion distortion in the rugged terrain and bumpy environment, which leads to a large increase in the cumulative error of odometer. Camera and lidar are not suitable as the main sensor of SLAM algorithm in unstructured environment because they are sensitive to the change of environment. Therefore, an autonomous navigation method for planetary rover based on multi-modal fusion and multi-factor graph optimization is presented in this paper. The IMU (Inertial Measurement Unit) odometer node is added to the lidar odometer and visual odometer thread, and the IMU is taken as the central node to build a multi-factor graph optimization model. A multi-factor graph optimization model and a strong adaptive constraint strategy with reasonable weights are constructed. The pose estimation of other sensors is used to constrain IMU bias, meanwhile historical rut tracking, obstacle contour matching and skyline feature matching are introduced. Finally, motion prediction is achieved in the IMU odometer node. Simulation results and field tests show that this method can effectively cope with the unstructured environment on planetary surface, which achieves adaptive and robust autonomous navigation of planetary rover.

Lunar ground segmentation using a modified U-net neural network

Semantic segmentation plays a significant role in unstructured and planetary scene understanding, offering to a robotic system or a planetary rover valuable knowledge about its surroundings. Several studies investigate rover-based scene recognition planetary-like environments but there is a lack of a semantic segmentation architecture, focused on computing systems with low resources and tested on the lunar surface. In this study, a lightweight encoder-decoder neural network (NN) architecture is proposed for rover-based ground segmentation on the lunar surface. The proposed architecture is composed by a modified MobilenetV2 as encoder and a lightweight U-net decoder while the training and evaluation process were conducted using a publicly available synthetic dataset with lunar landscape images. The proposed model provides robust segmentation results, allowing the lunar scene understanding focused on rocks and boulders. It achieves similar accuracy, compared with original U-net and U-net-based architectures which are 110–140 times larger than the proposed architecture. This study, aims to contribute in lunar landscape segmentation utilizing deep learning techniques, while it proves a great potential in autonomous lunar navigation ensuring a safer and smoother navigation on the moon. To the best of our knowledge, this is the first study which propose a lightweight semantic segmentation architecture for the lunar surface, aiming to reinforce the autonomous rover navigation.

Design methodology, synthesis, and control strategy of the high-speed planetary rover

Design methodology, synthesis, and control strategy of the high-speed planetary rover

Sequential track fusion in multi-sensor networks of unscented Kalman filters: A case of slip estimation in planetary mobile robots

Accurate wheel slip estimation facilitates Wheeled Mobile Robots (WMRs) with improved localization and traversability monitoring which are crucial to their autonomy in challenging planetary environments. Although distributed track-level fusion offers better computational efficiency, often sensor-level fusion is adopted in slip estimators of planetary rovers. Extending upon our earlier work, we develop a novel explicit track-to-track fusion algorithm for multi-sensor networks of unscented Kalman filters that has immediate application to slip ratio estimation in WMRs. The algorithm demonstrates better consistency compared to the existing fusion techniques since it implements a sub-optimal fusion rule to sequentially combine local tracks. It also saves computational power due to the recursive propagation of cross-covariance matrices based on the statistical linearization technique, instead of performing online optimizations. We prove that this recursion only requires the information of the first and last tracks in a sequence if all local tracks are unbiased. We rigorously study various key properties of the developed fusion algorithm and show its superior level of confidence when compared to the two prominent fusion methods of sequential and batch covariance intersection. The slip ratio estimation in a six-wheel WMR is considered as a case study, where the steerable wheel sets act as a network of sensors. The proposed slip estimator works based on the rigid body kinematics and readings of purely proprioceptive sensors, i.e., an inertial measurement unit and encoders. The slip estimator’s performance is evaluated in a high-fidelity software-in-the-loop simulation environment connecting MATLAB and CM Lab’s Vortex Studio software. In a comparison study that considers four rival strategies, we show the superiority of the proposed fusion method offering a balance between consistency, accuracy, and speed in real-time slip estimations.

A Real-Time Sinkage Detection Method for the Planetary Robotic Wheel-on-Limb System via a Monocular Camera

When traversing soft and rugged terrain, a planetary rover is susceptible to slipping and sinking, which impedes its movement. The real-time detection of wheel sinkage in the planetary wheel-on-limb system is crucial for enhancing motion safety and passability on such terrain. Initially, this study establishes a measurement of wheel sinkage under complex terrain conditions. Subsequently, a monocular vision-based wheel sinkage detection method is presented by combining the wheel–terrain boundary with the wheel center position (WTB-WCP). The method enables the efficient and accurate detection of wheel sinkage through two-stage parallel computation of the wheel–terrain boundary fitting and wheel center localization. Finally, this study establishes an experimental platform based on a monocular camera and the planetary rover wheel-on-limb system to experimentally validate and comparatively analyze the proposed method. The experimental results demonstrate that the method effectively provides information on the wheel sinkage of the planetary rover wheel-on-limb system, and the relative errors of the method do not exceed 4%. The method has high accuracy and reliability and is greatly significant for the safety and passability of planetary rovers in soft and rugged terrain.

Development and Design of an Innovative and Lightweight Reconnaissance Rover Using Composite Materials

Modern planetary rovers are based on aluminum chassis, but thanks to carbon fiber composites, the weight of such mobility platforms can be dramatically lowered. This paper describes the design of a 6-wheeled rover with a rocker-bogie suspension, adopting carbon fiber laminates and short-fiber-reinforced polymers. This last feature is an innovative approach that fits well with the use of additive manufacturing, allowing for both highly optimized parts and rapid fabrication of spares in future manned missions on the Lunar surface. The structural design was validated against a set of boundary conditions (static analyses) and requirements of launch and space environments (dynamic analyses) as prescribed by European Cooperation for Space Standardization standards. The composite rover was designed to lighten the structure itself and increase the payload-to-total-mass ratio: the composite solution offers a ratio three times higher than the typical rover (ratio from 0.12 to 0.38).

Series of advanced SLAM processes and applications

Simultaneous Localization and Mapping (SLAM) is an increasingly important field in robotics. SLAM enables the processes of localization and mapping to run simultaneously by using methods like feature extraction, feature matching, extended Kalman Filter, and probability distribution models, allowing robots to have more autonomy and greater efficiency. SLAM has been applied in industry robots, autonomous vehicles, augmented reality, UAVs, humanoid robots, and planetary rovers. With an increasing number of applications of SLAM, the demand for higher-performance SLAM algorithms has driven innovation and advancement in the field every year. SLAM is a highly promising algorithm for the future of robotics, but problems like uncertainty, correspondence, and time complexity prevent the full use of SLAM in robotics applications. It is essential to analyze SLAM in all aspects to apply it to future work. This article provides a detailed insight into the SLAM process, considers previous advancements and current problems, and discusses the future of SLAM.

Energy system and resource utilization in space: A state-of-the-art review

<p>Deep space exploration expands our understanding about the evolution history of solar system, while the future development heavily relies on the construction of energy systems and utilization of resources on the planet. This paper systematically reviewed the progress in the environmental control and construction technologies of space bases, extraterrestrial in situ resource utilization technology, energy systems, key technologies for planetary transportation platforms, and geological explorations. The current status, pros and cons of these technologies and systems are introduced and discussed. As an important artificial microenvironment in the space base, the environmental control and life support system (ECLSS) provides necessary resources for human. Sintering and additive manufacturing technologies demonstrate the potential to construct a space base with lunar regolith or simulants. The extraction and in situ utilization of resources on the Moon, including water ice, oxygen, and helium-3, are crucial to maintain life support for lunar exploration. Typical energy systems that can be used on the Moon include photovoltaic cell, Stirling power generation technology, closed Brayton cycle (CBC) system, Rankine cycle system, heat storage system, and integrated energy system. The CBC system has the highest thermal efficiency (39%) among them, making it suitable for late-period energy supply. The performance of various planetary rovers, the most important transportation platforms, are summarized. Through geological explorations, the resource distribution, content, and occurrence can be obtained. Perspectives on the future, promotions of environment adaptation, resource recovery, energy efficiency, and intelligence of the existing technologies are still needed to move forward on space explorations.</p>