Rover Simulation Research Articles

Rapid SLAM Method for Star Surface Rover in Unstructured Space Environments

The space environment is characterized by unstructured features, sparsity, and poor lighting conditions. The difficulty in extracting features from the visual frontend of traditional SLAM methods results in poor localization and time-consuming issues. This paper proposes a rapid and real-time localization and mapping method for star chart surveyors in unstructured space environments. Improved localization is achieved using multiple sensor fusion to sense the space environment. We replaced the traditional feature extraction module with an enhanced SuperPoint feature extraction network to tackle the challenge of challenging feature extraction in unstructured space environments. By dynamically adjusting detection thresholds, we achieved uniform detection and description of image keypoints, ultimately resulting in robust and accurate feature association information. Furthermore, we minimized redundant information to achieve precise positioning with high efficiency and low power consumption. We established a star surface rover simulation system and created simulated environments resembling Mars and the lunar surface. Compared to the LVI-SAM system, our method achieved a 20% improvement in localization accuracy for lunar scenarios. In Mars scenarios, our method achieved a positioning accuracy of 0.716 m and reduced runtime by 18.682 s for the same tasks. Our approach exhibits higher localization accuracy and lower power consumption in unstructured space environments.

Calibration of an expeditious terramechanics model using a higher‐fidelity model, Bayesian inference, and a virtual bevameter test

AbstractThe soil contact model (SCM) is widely used in practice for off‐road wheeled vehicle mobility studies when simulation speed is important and highly accurate results are not a main concern. In practice, the SCM parameters are obtained via a bevameter test, which requires a complex apparatus and experimental procedure. Here, we advance the idea of running a virtual bevameter test using a high‐fidelity terramechanics simulation. The latter employs the “continuous representation model” (CRM), which regards the deformable terrain as an elasto‐plastic continuum that is spatially discretized using the smoothed particle hydrodynamics method. The approach embraced is as follows: a virtual bevameter test is run in simulation using CRM terrain to generate “ground truth” data; in a Bayesian framework, this data is subsequently used to calibrate the SCM terrain. We show that (i) the resulting SCM terrain, while leading to fast terramechanics simulations, serves as a good proxy for the more complex CRM terrain; and (ii) the SCM‐over‐CRM simulation speedup is roughly one order of magnitude. These conclusions are reached in conjunction with two tests: a single wheel test, and a full rover simulation. The SCM and CRM simulations are run in the open‐source software Chrono. The calibration is performed using PyMC, which is a Python package that interactively communicates with Chrono to calibrate SCM. The models and scripts used in this contribution are available as open source for unfettered use and distribution in a public repository.

LunarSim: Lunar Rover Simulator Focused on High Visual Fidelity and ROS 2 Integration for Advanced Computer Vision Algorithm Development

Autonomous lunar exploration is a complex task that requires the development of sophisticated algorithms to control the movement of lunar rovers in a challenging environment, based on visual feedback. To train and evaluate these algorithms, it is crucial to have access to both a simulation framework and data that accurately represent the conditions on the lunar surface, with the main focus on providing the visual fidelity necessary for computer vision algorithm development. In this paper, we present a lunar-orientated robotic simulation environment, developed using the Unity game engine, built on top of robot operating system 2 (ROS 2), which enables researchers to generate quality synthetic vision data and test their algorithms for autonomous perception and navigation of lunar rovers in a controlled environment. To demonstrate the versatility of the simulator, we present several use cases in which it is deployed on various efficient hardware platforms, including FPGA and Edge AI devices, to evaluate the performance of different vision-based algorithms for lunar exploration. In general, the simulation environment provides a valuable tool for researchers developing lunar rover systems.

Vision-Based Virtual Simulation Platform for Planetary Rovers

In order to evaluate the feasibility of planetary exploration missions, it is imperative to construct planetary terrain environments on the ground. Nevertheless, the implementation of this method is characterized by time-consuming and challenging factors. By contrast, resorting to simulation approaches represents a cost-effective and high-efficiency alternative, which can facilitate the simulation validation of planetary rover exploration missions in an effective manner. In this paper, a modular planet rover simulation platform is proposed; by building and overlaying multiple feature layers corresponding to the surface of the planet, we realize the simulation of high-resolution fine terrain and through adjusting the terrain parameters to meet the needs of different simulated terrain. The experimental results show that we have built a scene to satisfy the requirements of visual effects and physically realistic characteristics of simulation. Using an improved PatchMatch stereomethod to recover images captured real time by a planetary rover navigation camera in a virtual environment provided more complete three-dimensional terrain data for subsequent simulation validation of local path planning. Finally, a simulation environment that combines high-fidelity visual effects and kinematic characteristics supports visualizing the simulation platform: we propose a path planning method using global planning combined with local obstacle avoidance, and we obtain the optimal path that satisfies the kinematic constraints.

Training augmentation using additive sensory noise in a lunar rover navigation task.

The uncertain environments of future space missions means that astronauts will need to acquire new skills rapidly; thus, a non-invasive method to enhance learning of complex tasks is desirable. Stochastic resonance (SR) is a phenomenon where adding noise improves the throughput of a weak signal. SR has been shown to improve perception and cognitive performance in certain individuals. However, the learning of operational tasks and behavioral health effects of repeated noise exposure aimed to elicit SR are unknown. We evaluated the long-term impacts and acceptability of repeated auditory white noise (AWN) and/or noisy galvanic vestibular stimulation (nGVS) on operational learning and behavioral health. Subjects (n = 24) participated in a time longitudinal experiment to access learning and behavioral health. Subjects were assigned to one of our four treatments: sham, AWN (55 dB SPL), nGVS (0.5 mA), and their combination to create a multi-modal SR (MMSR) condition. To assess the effects of additive noise on learning, these treatments were administered continuously during a lunar rover simulation in virtual reality. To assess behavioral health, subjects completed daily, subjective questionnaires related to their mood, sleep, stress, and their perceived acceptance of noise stimulation. We found that subjects learned the lunar rover task over time, as shown by significantly lower power required for the rover to complete traverses (p < 0.005) and increased object identification accuracy in the environment (p = 0.05), but this was not influenced by additive SR noise (p = 0.58). We found no influence of noise on mood or stress following stimulation (p > 0.09). We found marginally significant longitudinal effects of noise on behavioral health (p = 0.06) as measured by strain and sleep. We found slight differences in stimulation acceptability between treatment groups, and notably nGVS was found to be more distracting than sham (p = 0.006). Our results suggest that repeatedly administering sensory noise does not improve long-term operational learning performance or affect behavioral health. We also find that repetitive noise administration is acceptable in this context. While additive noise does not improve performance in this paradigm, if it were used for other contexts, it appears acceptable without negative longitudinal effects.

Motion Simulation and Human–Computer Interaction System for Lunar Exploration

When planning lunar rover missions, it is important to develop intuition and driving skills for unfamiliar environments before incurring the costs of reaching the moon. Simulators make it possible to operate in environments that have the physical characteristics of target locations without the expense of extensive physical tests. This paper proposes a motion simulation and human–computer interaction system based on a parallel mechanism to realize high-fidelity manned lunar rover simulations. The system consists of an interactive operating platform and a lunar surface simulation environment based on Unity3D. To make the 6-DOF platform simulate the posture changes of the rover, we improved the motion simulation algorithm. We designed a posture adjustment system and built virtual sensors to help astronauts perceive the lunar environment. Finally, this paper discusses the method for the realization of the multi-channel human–computer interaction system; astronauts can interactively control the rover through five channels. Experiments show that this system can realize high-fidelity rover simulation and improve the efficiency of human-computer interaction.

Wheel’s Slip Ratio and Sinkage Estimation for Planetary Rovers Moving on Deformable Terrain

Planetary rover plays a significant role in the lunar and Mars exploration missions and wheel’s move status such as sinkage and slip ratio are the critical Factor to estimate the moving ability of the Mars rover. A method is proposed to estimate wheel’s sinkage and skid angle based on terramechanic model and estimate slip ratio based on rover’s dynamic model using Extended Kalman Filter algorithm. The proposed method can avoid the errors caused by Visual Odometry and improves the estimation accuracy of rover state through the fusion with other sensors’ data. The effectiveness of the estimation method for planetary rovers is verified by comparing with the results obtained from dynamic simulation of Mars rovers.

MarsSim: A high-fidelity physical and visual simulation for Mars rovers

Simulation has deeply infiltrated into the development and validation of planetary rover technologies concerning mobility and autonomy, by providing large amounts of data in a variety of conditions and environments, as well as high-fidelity prototypes. Prevalent approaches for rover simulation focus either on physical behaviors or visual scenarios. In this paper, we present a simulator named MarsSim developed upon ROS/Gazebo platform that supports both physical and visual realistic simulations. The interaction between the wheel and the rough deformable terrain is modeled with improved contact solutions, and the model is integrated into a refined physical simulation architecture as an independent terramechanics plugin. Using multi-scale planetary data and representation models, Martian scenarios are constructed, considering characteristics in terms of the surface, rock and atmosphere. Compared with experiments, the terramechanics plugin shows great modelling accuracy in fitting interactive force/torque and slip-sinkage phenomenon. The simulation of traverses with a six- wheeled planetary rover on Martian landscape are demonstrated with near-photoreal images, coupled with physical-realistic locomotive data under different terrains.

Bridging the gap between single- and multi-model predictive runtime verification

This paper presents an extension of the Predictive Runtime Verification (PRV) paradigm to consider multiple models of the System Under Analysis (SUA). We call this extension Multi-Model PRV. Typically, PRV attempts to predict the satisfaction or violation of a property based on a trace and a (single) formal model of the SUA. However, contemporary node- or component-based systems (e.g. robotic systems) may benefit from monitoring based on a model of each component. We show how a Multi-Model PRV approach can be applied in either a centralised or a compositional way (where the property is compositional), as best suits the SUA. Crucially, our approach is formalism-agnostic. We demonstrate our approach using an illustrative example of a Mars Curiosity rover simulation and evaluate our contribution via a prototype implementation.

Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints.

Path planning is an essential technology for lunar rover to achieve safe and efficient autonomous exploration mission, this paper proposes a learning-based end-to-end path planning algorithm for lunar rovers with safety constraints. Firstly, a training environment integrating real lunar surface terrain data was built using the Gazebo simulation environment and a lunar rover simulator was created in it to simulate the real lunar surface environment and the lunar rover system. Then an end-to-end path planning algorithm based on deep reinforcement learning method is designed, including state space, action space, network structure, reward function considering slip behavior, and training method based on proximal policy optimization. In addition, to improve the generalization ability to different lunar surface topography and different scale environments, a variety of training scenarios were set up to train the network model using the idea of curriculum learning. The simulation results show that the proposed planning algorithm can successfully achieve the end-to-end path planning of the lunar rover, and the path generated by the proposed algorithm has a higher safety guarantee compared with the classical path planning algorithm.

Integrating the Sam Sensor with the Mars Rover Simulator

A computer simulation of complex hardware and software amalgamated bot is often a better alternative to traditional scaled-up or scaled-down physical replications of the bots. In accordance, our project encompasses the simulation of the Mars Rover in a local machine. In its implementation, it consists of two parts, the Rover and the Mission Control and Command Center. The Rover and Mission Control communicate with each other through a common communications’ protocol. The simulation also incorporates logging for status and analysis, making debugging possible. The simulation takes into account sensors such as the REMS, radio, spectrometer, Apxs, DAN and a radiation detector. In addition, there is a service layer that fetches relevant data for various rover sensors. The paper throws light on the implementation of the SAM(Sample Analysis on Mars) suite of instruments for the Mars Rover simulation

A Multibody Dynamics Framework for Simulation of Rovers on Soft Terrain

A new framework is developed for efficient implementation of semi-empirical terramechanics models in multibody dynamics environments. In this approach, for every wheel in contact with soft soil, unilateral contact constraints are added for both the normal direction and the tangent plane. The forces associated with the latter, like traction and rolling resistance, are formulated in this approach as set-valued force laws, their properties being determined by deregularization of the terramechanics relations. As shown in the paper, this leads to the dynamics representation in the form of a linear complementarity problem (LCP). With this formulation, stable simulation of rovers is achieved even at relatively large time steps. In addition, a high-resolution height-field (HF) is employed to model terrain-surface deformation and changes in hardening of soil under the wheel. As a result, the multipass effect is captured in the presented approach. In addition, an extensive set of experiments was conducted using a version of the Juno rover (Juno II). The experimental results are analyzed and compared with the model developed in the paper.

Multibody Simulation of Planetary Rover Mobility in Condition of Uncertain soft Terrain

Mobility analysis by computer multi-body simulation of wheeled rovers plays a fundamental role in planetary exploration programs. Considering that in space missions systems have to operate within an environment often times largely unknown, it turns out that embedding numerical analyses with stochastic methods can be beneficial. This work presents results from applying nondeterministic methods to mobility analysis of a planetary rover in condition of uncertain terrain properties. The terrain uncertainty is quantified by experimental measurements of the soil properties by a dedicated equipment. Next, the soil uncertainty is propagated through a multi-body model in order to assess the uncertainty of the rover position when traveling on soft soil. Probabilistic and non-probabilistic approaches are carried out and compared.

Wheel–Soil Interaction Model for Rover Simulation and Analysis Using Elastoplasticity Theory

A novel approach is proposed for the modeling of rigid-wheel and soft-soil interaction to efficiently compute normal and shear stress distributions in the contact area. The authors propose a velocity field in the vicinity of the contact area based on the physical nature of the problem. Thereupon, the incremental changes to the stress field are computed by resorting to elastoplasticity theory and an appropriate already existing constitutive relation for soil. The proposed approach leads to results that agree well with those obtained using well-established terramechanics models, while addressing some of their shortcomings. In addition, the proposed approach uses generalized velocities of the wheel as inputs, which makes it compatible with dynamic models of multibody systems. The dynamic slip-sinkage behavior of the wheel and the semielliptical shape of the normal stress distribution under the wheel are natural outcomes of the proposed model. Experimental investigation under various ranges of wheel slippage shows good agreement with the data available in the literature.

ROSTDyn: Rover simulation based on terramechanics and dynamics

In recent years, rover-based planetary exploration missions have induced some new challenges related to both the speed and the fidelity of rover simulation. This paper introduces ROSTDyn (rover simulation based on terramechanics and dynamics), a good-fidelity (in a linear motion without side forces and related torques), real-time (with an Intel Core2 CPU and ATI Radeon HD 4650 GPU) simulation platform for planetary rovers developed using C++ on the basis of the Vortex physics engine. The inherent trade-off between high fidelity and high speed is overcome by using an improved and simplified terramechanics model and Vortex. This paper presents the key technologies and algorithms constituting ROSTDyn, including the creation of the rover model and terrain model, computation of contact-area parameters, computation of interactive force/torque model, and ROSTDyn’s implementation. Speed tests confirm that ROSTDyn can perform a real-time simulation when the display frequency is less than 45Hz and the computation frequency is less than 450Hz. A comparison of the simulation and experiment results for an example involving a six-wheel rover climbing a series of slopes confirms the good fidelity of ROSTDyn.

A11 惑星探査ローバーの軟弱斜面横断シミュレータの開発(M-OS13-3:探査ローバの走行力学解析I,M-OS13 宇宙機・宇宙ロボットのダイナミクスと制御,「海を越え,国を越え,世代を超えて!」)

A11 惑星探査ローバーの軟弱斜面横断シミュレータの開発(M-OS13-3:探査ローバの走行力学解析I,M-OS13 宇宙機・宇宙ロボットのダイナミクスと制御,「海を越え,国を越え,世代を超えて!」)

A method for on-line soil parameters modification to planetary rover simulation

In planetary exploratory rover simulation, the contact model between wheel and terrain inevitably has some differences in contrast with the real one, which can make rover depart the planned track. To eliminate the dynamic errors caused by it, this paper presents a method for on-line soil parameters modification. This paper classifies data errors between virtual rover and real rover as model errors and asynchronous errors. Before modification, data identification is utilized to eliminate asynchronous errors and get a group of effective data with least additional errors. Based on the simplified terramechanics model, the origins of model errors are analyzed in detail from static status and kinetic status; meanwhile, some soil parameters are decoupled from the complicated model, and it makes on-line soil parameters modification feasible. An effective coefficient is also proposed to maintain the stability and convergence of modification. Lastly, through simulations on ROSTDyn (ROver Simulation based on Terramechanics and Dynamics), it is demonstrated that the soil parameters modification method is effective and useful for rover simulation to eliminate dynamic errors of predictive model.

A Distributed Approach to System-Level Prognostics

Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component life- times that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative lack of scalability and efficiency of typical prognostics approaches. In order to address these issues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resulting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.

Double Closed-Loop Motor Modeling and Analysis for Lunar Rover Simulation

Motor drive control is the major study field in the development of lunar rover. Based on the double-closed DC loop adjustable-speed system, a motor drive simulation module using a position recursive PID control algorithm is developed, which is integrated into the multi-body dynamics simulation system, to carry out the whole lunar rover simulation. And the cruise process of lunar rover locomotion in the complex lunar terrain is simulated in a virtual environment.

Virtual simulation system with path-following control for lunar rovers moving on rough terrain

Virtual simulation technology is of great importance for the teleoperation of lunar rovers during the exploration phase, as well as the design of locomotion systems, performance evaluation, and control strategy verification during the R&D phase. The currently used simulation methods for lunar rovers have several disadvantages such as poor fidelity for wheel-soil interaction mechanics, difficulty in simulating rough terrains, and high complexity making it difficult to realize mobility control in simulation systems. This paper presents an approach for the construction of a virtual simulation system that integrates the features of 3D modeling, wheel-soil interaction mechanics, dynamics analysis, mobility control, and visualization for lunar rovers. Wheel-soil interaction experiments are carried out to test the forces and moments acted on a lunar rover’s wheel by the soil with a vertical load of 80 N and slip ratios of 0, 0.03, 0.05, 0.1, 0.2, 0.3, 0.4, and 0.6. The experimental results are referenced in order to set the parameters’ values for the PAC2002 tire model of the ADAMS/Tire module. In addition, the rough lunar terrain is simulated with 3DS Max software after analyzing its characteristics, and a data-transfer program is developed with Matlab to simulate the 3D reappearance of a lunar environment in ADAMS. The 3D model of a lunar rover is developed by using Pro/E software and is then imported into ADAMS. Finally, a virtual simulation system for lunar rovers is developed. A path-following control strategy based on slip compensation for a six-wheeled lunar rover prototype is researched. The controller is implemented by using Matlab/Simulink to carry out joint simulations with ADAMS. The designed virtual lunar rover could follow the planned path on a rough terrain. This paper can also provide a reference scheme for virtual simulation and performance analysis of rovers moving on rough lunar terrains.