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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Ground control of space station robotics systems, unmanned space platform servicing, earth-based control of lunar and planetary rover missions, and planetary balloon missions.
A new laser-based optical sensor system that provides hazard detection for planetary rovers is presented. It is anticipated that the sensor can support safe travel at speeds up to 6cm/second for large (1m) rovers in full sunlight on Earth or Mars. The system overcomes limitations in an older design that require image differencing ot detect a laser stripe in full sun.
Automated planning and scheduling, including automated path planning, has been integrated with an internet-based distributed operations system for planetary rover operations.
This paper reviews the evolution of NASA's planning for planetary rovers (i.e. robotic vehicles which may be deployed on planetary bodies for exploration, science analysis, and construction) and some of the technology that has been developed to achieve the desired capabilities. The program is comprised of a variety of vehicle sizes and types in order to accommodate a range of potential user needs. This includes vehicles whose weight spans a few kilograms to several thousand kilograms; whose locomotion is implemented using wheels, tracks, and legs; and whose payloads vary from microinstruments to large scale assemblies for construction. We first describe robotic vehicles, and their associated control systems, developed by NASA in the late 1980's as part of a proposed Mars Rover Sample Return (MRSR) mission. Suggested goals at that time for such an MRSR mission included navigating for one to two years across hundreds of kilometers of Martian surface; traversing a diversity of rugged, unknown terrain; collecting and analyzing a variety of samples; and bringing back selected samples to the lander for return to Earth. Subsequently, we present the current plans (considerably more modest) which have evolved both from technological 'lessons learned' in the previous period, and modified aspirations of NASA missions. This paper describes some of the demonstrated capabilities of the developed machines and the technologies which made these capabilities possible.
A variety of technical subjects need to be solved before planetary rover navigation could be a part of future missions. The sensors which will perceive terrain environment around the rover will require critical development efforts. The image laser range finder (ILRF) discussed here is one of the candidate sensors because of its advantage in providing range data required for its navigation. The authors developed a new compact-sized ILRF which is a quarter of the size of conventional ones. Instead of the current two directional scanning system which is comprised of nodding and polygon mirrors, the new ILRF is equipped with the new concept of a direct polygon mirror driving system, which successfully made its size compact to accommodate the design requirements. The paper reports on the design concept and preliminary technical specifications established in the current development phase.
This paper describes the evolution of NASA's planning for planetary rovers and some of the technology that has been developed to achieve the desired capabilities.
The paper proposes a new architecture for autonomously generating and managing movement plans of planetary rovers. The system utilizes the uniform representation of the instantaneous subgoals in the form of virtual sensor states and the autonomous generation of the subsumption type plan network, which are expected to lead to the capability to pursue the overall goal while efficiently managing various unpredicted anomalies in a partially unknown, ill-structured environment such as a planetary surface.
Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computaitonally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA.
This paper presents an overview of the onboard subsystems that will be used in guiding a planetary rover. Particular emphasis is placed on the planning and sensing systems and their associated costs, particularly in computation. Issues that will be used in evaluating trades between the navigation system and mobility system are also presented.
The paper presents some of the design issues and vehicle requirements that need to be addressed for the Mars planetary rovers. Some of the designs currently being investigated, including the JPL rover, the Martin Marietta vehicle, and the French Space Agency's VAP project, are examined. The rover must satisfy such mission requirements as surveying the terrain, preparing the landing sites, loading and unloading components for base operations, and aiding in the recovery of in situ materials.
This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype.The diagnosis is performed by using a model-based method in the context of health management of hybrid systems.In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.
Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed groundbased Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers.
Weakly-coupled Markov decision processes can be decomposed into subprocesses that interact only through a small set of bottleneck states. We study a hierarchical reinforcement learning algorithm designed to take advantage of this particular type of decomposability. To test our algorithm, we use a decision-making problem faced by autonomous planetary rovers. In this problem, a Mars rover must decide which activities to perform and when to traverse between science sites in order to make the best use of its limited resources. In our experiments, the hierarchical algorithm performs better than Q-learning in the early stages of learning, but unlike Q-learning it converges to a suboptimal policy. This suggests that it may be advantageous to use the hierarchical algorithm when training time is limited.
Future small and micro-missions, such as Mars Scouts and Deep Space probes, require a new look at highly integrated, re-configurable, low power avionics. This paper will present our plans for developing a scalable, configurable, and highly integrated 32-bit embedded platform capable of implementing computationally intensive signal processing and control algorithms in space flight instruments and systems. This platform is designed to service the need of both small and large spacecraft and planetary rovers that will operate within moderate radiation environments. Some of the key characteristics of this platform are its small size, low power, high performance, and flexibility. This estimated 10 fold reduction in both size and power over state-of-the-art processing platforms will enable this new product to act as the core of a low-cost mobility system for a wide range of missions.
For electric vehicles, technology for monitoring, diagnosis, and prognosis of the electrical power system (EPS) becomes essential for safe and efficient operation. To this end, we develop a general system-level integrated diagnosis and prognosis framework, which detects, isolates, and identifies EPS faults, and predicts when the EPS will fail to deliver sufficient power. The approach takes advantage of recent work in structural model decomposition in order to distribute the global diagnosis and prognosis problems into local subproblems that can be solved in parallel, thus enabling implementation on distributed computational platforms. The framework is applied to the EPS of a planetary rover testbed, and is demonstrated using data from field experiments.
Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and incorporate it into the predictions so that informed decisions can be made about the system. In this paper, we describe three methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.
As NASAs exploration missions on planetary terrains become more aggressive, a focus on alternative modes of locomotion for rovers is necessary. In addition to climbing steep slopes, the terrain in these extreme environments is often unknown and can be extremely hard to traverse, increasing the likelihood of a vehicle or robot becoming damaged or immobilized. The conventional driving mode in which all wheels are either driven or free-rolling is very efficient on flat hard ground, but does not always provide enough traction to propel the vehicle through soft or steep terrain. This paper presents an alternative mode of travel and investigates the fundamental differences between these locomotion modes. The methods of push-pull locomotion discussed can be used with articulated wheeled vehicles and are identified as walking or inchinginch-worming. In both cases, the braked non-rolling wheels provide increased thrust. An in-depth study of how soil reacts under a rolling wheel vs. a braked wheel was performed by visually observing the motion of particles beneath the surface. This novel technique consists of driving or dragging a wheel in a soil bin against a transparent wall while high resolution, high-rate photographs are taken. Optical flow software was then used to determine shearing patterns in the soil. Different failure modes were observed for the rolling and braked wheel cases. A quantitative comparison of inching vs. conventional driving was also performed on a full-scale vehicle through a series of drawbar pull tests in the Lunar terrain strength simulant, GRC-1. The effect of tire stiffness was also compared; typically compliant tires provide better traction when driving in soft soil, however its been observed that rigid wheels may provide better thrust when non-rolling. Initial tests indicate up to a possible 40 increase in pull force capability at high slip when inching vs. rolling.
Traversability Index is introduced in this paper as a new and simple measure for traversability of planetary surfaces by mobile robots.