Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion

Autonomous vehicle guidance in unknown environments

Gaining from significant advances in their performance granted by technological evolution, Autonomous Vehicles are rapidly increasing the number of fields of possible and effective applications. From operations in hostile, dangerous environments (military use in removing unexploded projectiles, survey of nuclear power and chemical industrial plants following accidents) to repetitive 24h tasks (border surveillance), from power-multipliers helping in production to less exotic commercial application in household activities (cleaning robots as consumer electronics products), the combination of autonomy and motion offers nowadays impressive options. In fact, an autonomous vehicle can be completed by a number of sensors, actuators, devices making it able to exploit a quite large number of tasks. However, in order to successfully attain these results, the vehicle should be capable to navigate its path in different, sometimes unknown environments. This is the goal of this dissertation: to analyze and - mainly - to propose a suitable solution for the guidance of autonomous vehicles. The frame in which this research takes its steps is the activity carried on at the Guidance and Navigation Lab of Sapienza – Università di Roma, hosted at the School of Aerospace Engineering. Indeed, the solution proposed has an intrinsic, while not limiting, bias towards possible space applications, that will become obvious in some of the following content. A second bias dictated by the Guidance and Navigation Lab activities is represented by the choice of a sample platform. In fact, it would be difficult to perform a meaningful study keeping it a very general level, independent on the characteristics of the targeted kind of vehicle: it is easy to see from the rough list of applications cited above that these characteristics are extremely varied. The Lab hosted – even before the beginning of this thesis activity – a simple, home-designed and manufactured model of a small, yet performing enough autonomous vehicle, called RAGNO (standing for Rover for Autonomous Guidance Navigation and Observation): it was an obvious choice to select that rover as the reference platform to identify solutions for guidance, and to use it, cooperating to its improvement, for the test activities which should be considered as mandatory in this kind of thesis work to validate the suggested approaches. The draft of the thesis includes four main chapters, plus introduction, final remarks and future perspectives, and the list of references. The first chapter (“Autonomous Guidance Exploiting Stereoscopic Vision”) investigates in detail the technique which has been deemed as the most interesting for small vehicles. The current availability of low cost, high performance cameras suggests the adoption of the stereoscopic vision as a quite effective technique, also capable to making available to remote crew a view of the scenario quite similar to the one humans would have. Several advanced image analysis techniques have been investigated for the extraction of the features from left- and right-eye images, with SURF and BRISK algorithm being selected as the most promising one. In short, SURF is a blob detector with an associated descriptor of 64 elements, where the generic feature is extracted by applying sequential box filters to the surrounding area. The features are then localized in the point of the image where the determinant of the Hessian matrix H(x,y) is maximum. The descriptor vector is than determined by calculating the Haar wavelet response in a sampling pattern centered in the feature. BRISK is instead a corner detector with an associated binary descriptor of 512 bit. The generic feature is identified as the brightest point in a sampling circular area of N pixels while the descriptor vector is calculated by computing the brightness gradient of each of the N(N-1)/2 pairs of sampling points. Once left and right features have been extracted, their descriptors are compared in order to determine the corresponding pairs. The matching criterion consists in seeking for the two descriptors for which their relative distance (Euclidean norm for SURF, Hamming distance for BRISK) is minimum. The matching process is computationally expensive: to reduce the required time the thesis successfully explored the theory of the epipolar geometry, based on the geometric constraint existing between the left and right projection of the scene point P, and indeed limiting the space to be searched. Overall, the selected techniques require between 200 and 300 ms on a 2.4GHz clock CPU for the feature extraction and matching in a single (left+right) capture, making it a feasible solution for slow motion vehicles. Once matching phase has been finalized, a disparity map can be prepared highlighting the position of the identified objects, and by means of a triangulation (the baseline between the two cameras is known, the size of the targeted object is measured in pixels in both images) the position and distance of the obstacles can be obtained. The second chapter (“A Vehicle Prototype and its Guidance System”) is devoted to the implementation of the stereoscopic vision onboard a small test vehicle, which is the previously cited RAGNO rover. Indeed, a description of the vehicle – the chassis, the propulsion system with four electric motors empowering the wheels, the good roadside performance attainable, the commanding options – either fully autonomous, partly autonomous with remote monitoring, or fully remotely controlled via TCP/IP on mobile networks - is included first, with a focus on different sensors that, depending on the scenario, can integrate the stereoscopic vision system. The intelligence-side of guidance subsystem, exploiting the navigation information provided by the camera, is then detailed. Two guidance techniques have been studied and implemented to identify the optimal trajectory in a field with scattered obstacles: the artificial potential guidance, based on the Lyapunov approach, and the A-star algorithm, looking for the minimum of a cost function built on graphs joining the cells of a mesh over-imposed to the scenario. Performance of the two techniques are assessed for two specific test-cases, and the possibility of unstable behavior of the artificial potential guidance, bouncing among local minima, has been highlighted. Overall, A-star guidance is the suggested solution in terms of time, cost and reliability. Notice that, withstanding the noise affecting information from sensors, an estimation process based on Kalman filtering has been also included in the process to improve the smoothness of the targeted trajectory. The third chapter (“Examples of Possible Missions and Applications”) reports two experimental campaigns adopting RAGNO for the detection of dangerous gases. In the first one, the rover accommodates a specific sensor, and autonomously moves in open fields, avoiding possible obstacles, to exploit measurements at given time intervals. The same configuration for RAGNO is also used in the second campaign: this time, however, the path of the rover is autonomously computed on the basis of the way points communicated by a drone which is flying above the area of measurements and identifies possible targets of interest. The fourth chapter (“Guidance of Fleet of Autonomous Vehicles ”) stresses this successful idea of fleet of vehicles, and numerically investigates by algorithms purposely written in Matlab the performance of a simple swarm of two rovers exploring an unknown scenario, pretending – as an example - to represent a case of planetary surface exploration. The awareness of the surrounding environment is dictated by the characteristics of the sensors accommodated onboard, which have been assumed on the basis of the experience gained with the material of previous chapter. Moreover, the communication issues that would likely affect real world cases are included in the scheme by the possibility to model the comm link, and by running the simulation in a multi-task configuration where the two rovers are assigned to two different computer processes, each of them having a different TCP/IP address with a behavior actually depending on the flow of information received form the other explorer. Even if at a simulation-level only, it is deemed that such a final step collects different aspects investigated during the PhD period, with feasible sensors’ characteristics (obviously focusing on stereoscopic vision), guidance technique, coordination among autonomous agents and possible interesting application cases

Efficient Autonomous Navigation for Planetary Rovers with Limited Resources

Rovers operating on Mars are in need of more and more autonomous features to ful ll their challenging mission requirements. However, the inherent constraints of space systems make the implementation of complex algorithms an expensive and difficult task. In this paper we propose a control architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the current ExoMars navigation method, enhancing the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform long traverses by following a roughly safe path planned by operators on ground. The control architecture implementing the proposed navigation mode has been tested during a field test campaign on a planetary analogue terrain. The experiments evaluated the proposed approach, autonomously completing two long traverses while avoiding hazards. The approach only relies on the optical Localization Cameras stereobench, a sensor that is found in all rovers launched so far, and potentially allows for computationally inexpensive long-range autonomous navigation in terrains of medium difficulty

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

Autonomous Surface Site Establishment to Ensure Safe Crew Arrival and Operations

Traditional human Mars missions have relied on crew to support the surface systems. However, for safety, the surface systems will likely need to be setup and capable of operating prior to the arrival of crew. To mitigate risks to the crew, a novel surface architecture has been developed that addresses risks associated with other Mars missions. This architecture relies on a reusable descent and ascent vehicle, extensive in-situ resource utilization, redundant habitation systems, and emerging autonomous capabilities. The resulting surface architecture increases safety for the crew while also providing potential to expand to support longer missions with larger populations in the future

A unified vision and inertial navigation system for planetary hoppers

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (pages 139-146).In recent years, considerable attention has been paid to hopping as a novel mode of planetary exploration. Hopping vehicles provide advantages over traditional surface exploration vehicles, such as wheeled rovers, by enabling in-situ measurements in otherwise inaccessible terrain. However, significant development over previously demonstrated vehicle navigation technologies is required to overcome the inherent challenges involved in navigating a hopping vehicle, especially in adverse terrain. While hoppers are in many ways similar to traditional landers and surface explorers, they incorporate additional, unique motions that must be accounted for beyond those of conventional planetary landing and surface navigation systems. This thesis describes a unified vision and inertial navigation system for propulsive planetary hoppers and provides demonstration of this technology. An architecture for a navigation system specific to the motions and mission profiles of hoppers is presented, incorporating unified inertial and terrain-relative navigation solutions. A modular sensor testbed, including a stereo vision package and inertial measurement unit, was developed to act as a proof-of-concept for this navigation system architecture. The system is shown to be capable of real-time output of an accurate navigation state estimate for motions and trajectories similar to those of planetary hoppers.by Theodore J. Steiner, III.S.M

Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies

This study investigated a mission architecture that allows the systematic and affordable in-situ exploration of small solar system bodies, such as asteroids, comets, and Martian moons (Figure 1). The architecture relies on the novel concept of spacecraft/rover hybrids,which are surface mobility platforms capable of achieving large surface coverage (by attitude controlled hops, akin to spacecraft flight), fine mobility (by tumbling), and coarse instrument pointing (by changing orientation relative to the ground) in the low-gravity environments(micro-g to milli-g) of small bodies. The actuation of the hybrids relies on spinning three internal flywheels. Using a combination of torques, the three flywheel motors can produce a reaction torque in any orientation without additional moving parts. This mobility concept allows all subsystems to be packaged in one sealed enclosure and enables the platforms to be minimalistic. The hybrids would be deployed from a mother spacecraft, which would act as a communication relay to Earth and would aid the in-situ assets with tasks such as localization and navigation (Figure 1). The hybrids are expected to be more capable and affordable than wheeled or legged rovers, due to their multiple modes of mobility (both hopping and tumbling), and have simpler environmental sealing and thermal management (since all components are sealed in one enclosure, assuming non-deployable science instruments). In summary, this NIAC Phase II study has significantly increased the TRL (Technology Readiness Level) of the mobility and autonomy subsystems of spacecraft/rover hybrids, and characterized system engineering aspects in the context of a reference mission to Phobos. Future studies should focus on improving the robustness of the autonomy module and further refine system engineering aspects, in view of opportunities for technology infusion

Enabling All-Access Mobility for Planetary Exploration Vehicles via Transformative Reconfiguration

Effective large-scale exploration of planetary surfaces requires robotic vehicles capable of mobility across chaotic terrain. Characterized by a combination of ridges, cracks and valleys, the demands of this environment can cause spacecraft to experience significant reductions in operating footprint, performance, or even result in total system loss. Significantly increasing the scientific return of an interplanetary mission is facilitated by architectures capable of real-time configuration changes that go beyond that of active suspensions while concurrently meeting system, mass, power, and cost constraints. This Phase 1 report systematically explores how in-service architecture changes can expand system capabilities and mission opportunities. A foundation for concept generation is supplied by four Martian mission profiles spanning chasms, ice fields, craters and rocky terrain. A fifth mission profile centered on Near Earth Object exploration is also introduced. Concept generation is directed using four transformation principles - a taxonomy developed by the engineering design community to explain the cause of an architecture change and existing brainstorming techniques. This allowed early conceptual sketches of architecture changes to be organized by the principle driving the greatest increase in mission performance capability

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