Introducing a globally consistent orbital‐based localization system

In spite of the good performance of space exploratory missions, open issues still await to be solved. In autonomous or composite semi‐autonomous exploration of planetary land surfaces, rover localization is such an issue. The rovers of these missions (e.g., the MER and MSL) navigate relatively to their landing spot, ignoring their exact position on the coordinate system defined for the celestial body they explore. However, future advanced missions, like the Mars Sample Return, will require the localization of rovers on a global frame rather than the arbitrarily defined landing frame. In this paper we attempt to retrieve the absolute rover's location by identifying matching Regions of Interest (ROIs) between orbital and land images. In particular, we propose a system comprising two parts, an offline and an onboard one, which functions as follows: in advance of the mission a Global ROI Network (GN) is built offline by investigating the satellite images near the predicted touchdown ellipse, while during the mission a Local ROI Network (LN) is constructed counting on the images acquired by the vision system of the rover along its traverse. The last procedure relies on the accurate VO‐based relative rover localization. The LN is then paired with the GN through a modified 2D DARCES algorithm. The system has been assessed on real data collected by the ESA at the Atacama desert. The results demonstrate the system's potential to perform absolute localization, on condition that the area includes discriminative ROIs. The main contribution of this work is the enablement of global localization performed on contemporary rovers without requiring any additional hardware, such as long range LIDARs.     

Mission-level path planning and re-planning for rover exploration

The Life in the Atacama (LITA) project seeks to develop technologies for robotic life detection and apply them to the investigation of the Atacama Desert. Its field investigation in 2005 will demonstrate highly autonomous robotic science and daily multi-kilometer traverses over several weeks. A key component is mission-level path planning, which combines large-scale route selection, path and activity timing, and predictive energy management. Its purpose is to yield high-level plans for locomotion, solar charging and hibernation. We describe this new level of robotic autonomy and illustrate its performance from the field experiments in 2003.     

Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges

Future lunar/planetary exploration missions will demand mobile robots with the capability of reaching more challenging science targets and driving farther per day than the current Mars rovers. Among other improvements, reliable slippage estimation and compensation strategies will play a key role in enabling a safer and more efficient navigation. This paper reviews and discusses this body of research in the context of planetary exploration rovers. Previously published state‐of‐the‐art methods that have been validated through field testing are included as exemplary results. Limitations of the current techniques and recommendations for future developments and planetary missions close the survey.     

ARDEA—An MAV with skills for future planetary missions

We introduce a prototype flying platform for planetary exploration: autonomous robot design for extraterrestrial applications (ARDEA). Communication with unmanned missions beyond Earth orbit suffers from time delay, thus a key criterion for robotic exploration is a robot's ability to perform tasks without human intervention. For autonomous operation, all computations should be done on‐board and Global Navigation Satellite System (GNSS) should not be relied on for navigation purposes. Given these objectives ARDEA is equipped with two pairs of wide‐angle stereo cameras and an inertial measurement unit (IMU) for robust visual‐inertial navigation and time‐efficient, omni‐directional 3D mapping. The four cameras cover a $240^{°}$ vertical field of view, enabling the system to operate in confined environments such as caves formed by lava tubes. The captured images are split into several pinhole cameras, which are used for simultaneously running visual odometries. The stereo output is used for simultaneous localization and mapping, 3D map generation and collision‐free motion planning. To operate the vehicle efficiently for a variety of missions, ARDEA's capabilities have been modularized into skills which can be assembled to fulfill a mission's objectives. These skills are defined generically so that they are independent of the robot configuration, making the approach suitable for different heterogeneous robotic teams. The diverse skill set also makes the micro aerial vehicle (MAV) useful for any task where autonomous exploration is needed. For example terrestrial search and rescue missions where visual navigation in GNSS‐denied indoor environments is crucial, such as partially collapsed man‐made structures like buildings or tunnels. We have demonstrated the robustness of our system in indoor and outdoor field tests.     

Space robotics supporting exploration missions: vision, force control and coordination strategy for crew assistants

Many research activities are being carried out about space exploration missions, in order to strongly improve man’s working conditions in planetary environments. Along this line, this work presents some results obtained within the development of an effective robotic crew assistant. This robot can execute a number of operations, both in a completely autonomous manner (i.e. without requiring human supervision) and in a strict cooperation with the astronauts; thus supporting and helping them in executing operations otherwise very difficult or unfeasible to be accomplished. The developed and then adopted functional and algorithmic control architecture for the considered robot assistant, also including vision and force feedback, is described with some details in the present paper.     

Planetary exploration by a mobile robot: Mission teleprogramming and autonomous navigation

Sending mobile robots to accomplish planet exploration missions is scientifically promising and technologically challenging. We present in this paper a complete approach that encompasses the major aspects involved in the design of a robotic system for planetary exploration. It includes mission teleprogramming and supervision at a ground station, and autonomous mission execution by the remote mobile robot. We have partially implemented and validated these concepts. Experimental results illustrate the approach and the results.     

Recipes for Spatial Statistics with Global Datasets: A Martian Case Study

The Mars Odyssey Gamma Ray Spectrometer has yielded planetary data of global extent. Such remote-sensing missions usually assign the value of a continuous-valued geospatial attribute to a uniform latitude-longitude grid of bins. Typical attributes include elemental-mass fraction, areal fraction of a mineral type, areal fraction of rocks, thermal inertia, etc. The fineness of the grid is chosen according to the spatial resolution of the orbiter and concomitant data processing. We describe methods to maximize the information extracted from both bin and regional data. Rigorous use of statistical parameters and related methods for inter- and intra- regional comparisons are also discussed. While we discuss results from the Mars Odyssey mission, the techniques we describe are applicable whenever continuous-valued attributes of a planet’s surface are characterized with bins and regions. Our goal is to distill the simplest statistical methods for regional comparisons that would be intuitively accessible to planetary scientists.     

Miniature robots for space and military missions

Miniature robots enable low-cost planetary surface exploration missions as well as new military missions in urban terrain where small robots provide critical assistance to human operations. These space and military missions have many similar technological challenges. This article describes the technologies under development, the applications where these technologies are relevant to both space and military missions, and the status of the most recent technology demonstrations in terrestrial scenarios     

Current Status and Development Trend of Surface Penetrating Radarin the Application of Lunar and Deep Space Exploration

Surface Penetrating Radar(SPR)，a kind of geophysical method to survey the internal structure and change of properties of lossy dielectric material by using electromagnetic wave，is only able to penetrate the desolate surface of the planet for finding hidden signs of water ice.In addition，SPR is also the most eligible non destructive method for imaging planetary shallow layer soil structure and rock changes caused by electromagnetic discontinuous.This paper reviews the decades history of SPR in the application of extraterrestrial planetary exploration，and summarizes the scientific objectives，performance and results of SPRs configured in lunar and deep space missions.Then on this basis，combined with the latest news on radar technology，the main development trend of SPR in lunar and deep space exploration is given.     

Slippage and immobilization detection for planetary exploration rovers via machine learning and proprioceptive sensing

This paper presents a new methodology where machine learning is used for detecting various levels of slip in the context of planetary exploration robotic missions. This methodology aims at employing proprioceptive rover sensor signals. Consequently, no operational complexity is added to the rover's commanding and it is independent of lighting conditions. Two supervised learning methods (Support Vector Machines and Artificial Neural Networks) are compared to two unsupervised learning approaches (K‐means and Self‐Organizing Maps (SOM)). Physical experiments using a single‐wheel testbed equipped with an MSL spare wheel and a real planetary exploration rover validate the implemented methodology. Performance is evaluated in terms of well‐known metrics both considering single data points and subsets of consecutive data points (moving median filter). Computation time and storage requirements are also examined. One of the SOM‐based algorithms, semantic SOM method, demonstrates a proper balance between the benefits of supervised learning algorithms (high success rate, >96%) and the advantages of unsupervised learning methods (low storage requirements, 5 kb, and no need of manually‐labeled training data). This paper also addresses the most convenient placement of IMU sensors on the rover chassis such that slippage detection is maximized.     

Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing

In this paper, we present the vision-aided inertial navigation (VISINAV) algorithm that enables precision planetary landing. The vision front-end of the VISINAV system extracts 2-D-to-3-D correspondences between descent images and a surface map (mapped landmarks), as well as 2-D-to-2-D feature tracks through a sequence of descent images (opportunistic features). An extended Kalman filter (EKF) tightly integrates both types of visual feature observations with measurements from an inertial measurement unit. The filter computes accurate estimates of the lander's terrain-relative position, attitude, and velocity, in a resource-adaptive and hence real-time capable fashion. In addition to the technical analysis of the algorithm, the paper presents validation results from a sounding-rocket test flight, showing estimation errors of only 0.16 m/s for velocity and 6.4 m for position at touchdown. These results vastly improve current state of the art for terminal descent navigation without visual updates, and meet the requirements of future planetary exploration missions.     

Planetary rovers�� wheel�Csoil interaction mechanics: new challenges and applications for wheeled mobile robots

With the increasing challenges facing planetary exploration missions and the resultant increase in the performance requirements for planetary rovers, terramechanics (wheel–soil interaction mechanics) is playing an important role in the development of these rovers. As an extension of the conventional terramechanics theory for terrestrial vehicles, the terramechanics theory for planetary rovers, which is becoming a new research hotspot, is unique and puts forward many new challenging problems. This paper first discusses the significance of the study of wheel–soil interaction mechanics of planetary rovers and summarizes the differences between planetary rovers and terrestrial vehicles and the problems arising thereof. The application of terramechanics to the development of planetary rovers can be divided into two phases (the R&D phase and exploration phase for rovers) corresponding to the high-fidelity and simplified terramechanics models. This paper also describes the current research status by providing an introduction to classical terramechanics and the experimental, theoretical, and numerical researches on terramechanics for planetary rovers. The application status of the terramechanics for planetary rovers is analyzed from the aspects of rover design, performance evaluation, planetary soil parameter identification, dynamics simulation, mobility control, and path planning. Finally, the key issues for future research are discussed. The current planetary rovers are actually advanced wheeled mobile robots (WMRs), developed employing cutting-edge technologies from different fields. The terramechanics for planetary rovers is expected to present new challenges and applications for WMRs, making it possible to develop WMRs using the concepts of mechanics and dynamics.     

Distributed Control of Multi-Robot Systems Engaged in Tightly Coupled Tasks

NASA mission concepts for the upcoming decades of this century include exploration of sites such as steep cliff faces on Mars, as well as infrastructure deployment for a sustained robotic/manned presence on planetary and/or the lunar surface. Single robotic platforms, such as the Sojourner rover successfully flown in 1997 and the Mars Exploration Rovers (MER) which landed on Mars in January of 2004, have neither the autonomy, mobility, nor manipulation capabilities for such ambitious undertakings. One possible approach to these future missions is the fielding of cooperative multi-robot systems that have the required onboard control algorithms to more or less autonomously perform tightly coordinated tasks. These control algorithms must operate under the constrained mass, volume, processing, and communication conditions that are present on NASA planetary surface rover systems. In this paper, we describe the design and implementation of distributed control algorithms that build on our earlier development of an enabling architecture called CAMPOUT (Control Architecture for Multi-robot Planetary Outposts). We also report on some ongoing physical experiments in tightly coupled distributed control at the Jet Propulsion Lab in Pasadena, CA where in the first study two rovers acquire and carry an extended payload over uneven, natural terrain, and in the second three rovers form a team for cliff access.     

Multi-Robot Task Allocation in Uncertain Environments

Multiple cooperating robots hold the promise of improved performance and increased fault tolerance for large-scale problems such as planetary survey and habitat construction. Multi-robot coordination, however, is a complex problem. We cast this problem in the framework of multi-robot dynamic task allocation under uncertainty. We then describe an empirical study that sought general guidelines for task allocation strategies in multi-robot systems. We identify four distinct task allocation strategies, and demonstrate them in two versions of the multi-robot emergency handling task. We describe an experimental setup to compare results obtained from a simulated grid world to those obtained from physical mobile robot experiments. Data resulting from eight hours of experiments with multiple mobile robots are compared to the trend identified in simulation. The data from the simulations show that there is no single strategy that produces best performance in all cases, and that the best task allocation strategy changes as a function of the noise in the system. This result is significant, and shows the need for further investigation of task allocation strategies and their application to planetary exploration.     

Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers

Future planetary exploration missions will require wheeled mobile robots ("rovers") to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper, an online estimation method that identifies key terrain parameters using on-board robot sensors is presented. These parameters can be used for traversability prediction or in a traction control algorithm to improve robot mobility and to plan safe action plans for autonomous systems. Terrain parameters are also valuable indicators of planetary surface soil composition. The algorithm relies on a simplified form of classical terramechanics equations and uses a linear-least squares method to compute terrain parameters in real time. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for various soil types.     

Robotic vehicles for planetary exploration

Future missions to the moon, Mars, or other planetary surfaces will use planetary rovers for exploration or other tasks. Operation of these rovers as unmanned robotic vehicles with some form of remote or semi-autonomous control is desirable to reduce the cost and increase the capability and safety of many types of missions. However, the long time delays and relatively low bandwidths associated with radio communications between planets precludes a total telepresence approach to controlling the vehicle. A program to develop planetary rover technology has been initiated at the Jet Propulsion Laboratory (JPL) under sponsorship of the National Aeronautics and Space Administration (NASA). Developmental systems with the necessary sensing, computing, power, and mobility resources to demonstrate realistic forms of control for various missions have been developed and initial testing has been completed. These testbed systems, the associated navigation techniques currently used and planned for implementation, and long-term mission strategies employing them are described.     

Cognitive Maps for Planetary Rovers

This essay explores the problem of providing both autonomy and safety for a planetary rover too far away for continuous teleoperated control. Evidence from animal and human spatial behavior suggests useful characteristics for the sensory system and cognitive map of a planetary rover. I then sketch a design for a mixed-initiative exploration strategy for a semi-intelligent rover with a remote human operator, and describe some questions to be resolved by further research.     

Autonomous science during large‐scale robotic survey

Today's planetary exploration robots rarely travel beyond the yesterday imagery. However, advances in autonomous mobility will soon permit single‐command site surveys of multiple kilometers. Here scientists cannot see the terrain in advance, and explorer robots must navigate and collect data autonomously. Onboard science data understanding can improve these surveys with image analysis, pattern recognition, learned classification, and information‐theoretic planning. We report on field experiments near Amboy Crater, California, that demonstrate fundamental capabilities for autonomous surficial mapping of geologic phenomena with a visible near‐infrared spectrometer. We develop an approach to “science on the fly'' that adapts the robot's exploration using collected instrument data. We demonstrate feature detection and visual servoing to acquire spectra from dozens of targets without human intervention. The rover interprets spectra onboard, learning spatial models of science phenomena that guide it toward informative areas. It discovers spatial structure (correlations between neighboring regions) and cross‐sensor structure (correlations between different scales). The rover uses surface observations to reinterpret satellite imagery and improve exploration efficiency. © 2011 Wiley Periodicals, Inc.     

Enabling collaborative engineering and science at JPL

We examine the issue of distributed collaboration at the Jet Propulsion Laboratory (JPL). With the goals of “faster, better, cheaper” missions, an efficient, seamless collaboration capability is critical to future JPL space exploration missions. We assert that the current capabilities for distributed collaboration internal to JPL, as well as external, are unsatisfactory. This paper provides a vision of greatly enhanced distributed collaboration capabilities in the near future and into the far future. Our vision focuses primarily on distributed collaborative engineering and science.We believe enhanced capabilities are necessary in two collaborative paradigms: the virtual conference and the shared virtual workspace. We describe these collaborative paradigms, and discuss the tools that exist and the additional capability necessary to achieve the collaborative vision with respect to the types of data we typically work with at JPL. We identify the following types of data frequently exchanged in collaborative activities: project planning data, design data, notes/documentation, communication data, analysis/performance data, verification data and scientific data. Our analysis shows that at present there is good GroupWare support for project planning data and notes/documentation data. Support is improving for design data. Other data types have no or sporadic support at best.     

Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path

An evolutionary technique with a Fuzzy Inference System (FIS) is offered for planning time-optimal trajectories on a predefined Visibility Graph Method Dijkstra (VGM-D) path of a Nomad 200 mobile robot (MR). First of all, the segmented trajectory is generated by the VGM-D algorithm. Line and curve segments are the components of the trajectory. The number of intersections of the segmented VGM-D path determines the curve segments number. It is assumed that, at each curve segment, translation velocity v _t is taken as constant. The Differential Evolution (DE) algorithm finds v _t values of all the curve segments, which minimize the trajectory tracking time. Line segments lengths are used to calculate the constraints of the problem according to the Nomad 200's limitations on the translation velocity and acceleration/deceleration. The structures of the curve segments are modeled by FIS to decrease the DE's execution time. Another FIS model is used to define the upper bound of the translation velocities on the curve segments for the same purpose. Both FIS models are trained by the adapted-network-based fuzzy inference system (ANFIS). Experiments are successfully implemented on the Nomad 200 MR.