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

Traverse Planning with Temporal-Spatial Constraints

We present an approach to planning rover traverses in a domain that includes temporal-spatial constraints. We are using the NASA Resource Prospector mission as a reference mission in our research. The objective of this mission is to explore permanently shadowed regions at a Lunar pole. Most of the time the rover is required to avoid being in shadow. This requirement depends on where the rover is located and when it is at that location. Such a temporal-spatial constraint makes traverse planning more challenging for both humans and machines. We present a mixed-initiative traverse planner which addresses this challenge. This traverse planner is part of the Exploration Ground Data Systems (xGDS), which we have enhanced with new visualization features, new analysis tools, and new automation for path planning, in order to be applicable to the Re-source Prospector mission. The key concept that is the basis of the analysis tools and that supports the automated path planning is reachability in this dynamic environment due to the temporal-spatial constraints

System Design, Motion Modelling and Planning for a Recon figurable Wheeled Mobile Robot

Over the past ve decades the use of mobile robotic rovers to perform in-situ scienti c investigations on the surfaces of the Moon and Mars has been tremendously in uential in shaping our understanding of these extraterrestrial environments. As robotic missions have evolved there has been a greater desire to explore more unstructured terrain. This has exposed mobility limitations with conventional rover designs such as getting stuck in soft soil or simply not being able to access rugged terrain. Increased mobility and terrain traversability are key requirements when considering designs for next generation planetary rovers. Coupled with these requirements is the need to autonomously navigate unstructured terrain by taking full advantage of increased mobility. To address these issues, a high degree-of-freedom recon gurable platform that is capable of energy intensive legged locomotion in obstacle-rich terrain as well as wheeled locomotion in benign terrain is proposed. The complexities of the planning task that considers the high degree-of-freedom state space of this platform are considerable. A variant of asymptotically optimal sampling-based planners that exploits the presence of dominant sub-spaces within a recon gurable mobile robot's kinematic structure is proposed to increase path quality and ensure platform safety. The contributions of this thesis include: the design and implementation of a highly mobile planetary analogue rover; motion modelling of the platform to enable novel locomotion modes, along with experimental validation of each of these capabilities; the sampling-based HBFMT* planner that hierarchically considers sub-spaces to better guide search of the complete state space; and experimental validation of the planner with the physical platform that demonstrates how the planner exploits the robot's capabilities to uidly transition between various physical geometric con gurations and wheeled/legged locomotion modes

Multi-Robot Coordination and Scheduling for Deactivation & Decommissioning

Large quantities of high-level radioactive waste were generated during WWII. This waste is being stored in facilities such as double-shell tanks in Washington, and the Waste Isolation Pilot Plant in New Mexico. Due to the dangerous nature of radioactive waste, these facilities must undergo periodic inspections to ensure that leaks are detected quickly. In this work, we provide a set of methodologies to aid in the monitoring and inspection of these hazardous facilities. This allows inspection of dangerous regions without a human operator, and for the inspection of locations where a person would not be physically able to enter. First, we describe a robot equipped with sensors which uses a modified A* path-planning algorithm to navigate in a complex environment with a tether constraint. This is then augmented with an adaptive informative path planning approach that uses the assimilated sensor data within a Gaussian Process distribution model. The model\u27s predictive outputs are used to adaptively plan the robot\u27s path, to quickly map and localize areas from an unknown field of interest. The work was validated in extensive simulation testing and early hardware tests. Next, we focused on how to assign tasks to a heterogeneous set of robots. Task assignment is done in a manner which allows for task-robot dependencies, prioritization of tasks, collision checking, and more realistic travel estimates among other improvements from the state-of-the-art. Simulation testing of this work shows an increase in the number of tasks which are completed ahead of a deadline. Finally, we consider the case where robots are not able to complete planned tasks fully autonomously and require operator assistance during parts of their planned trajectory. We present a sampling-based methodology for allocating operator attention across multiple robots, or across different parts of a more sophisticated robot. This allows few operators to oversee large numbers of robots, allowing for a more scalable robotic infrastructure. This work was tested in simulation for both multi-robot deployment, and high degree-of-freedom robots, and was also tested in multi-robot hardware deployments. The work here can allow robots to carry out complex tasks, autonomously or with operator assistance. Altogether, these three components provide a comprehensive approach towards robotic deployment within the deactivation and decommissioning tasks faced by the Department of Energy

Mission planning and navigation support for lunar and planetary exploration

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Page 221 blank.Includes bibliographical references (p. 217-220).When mankind returns to the moon and eventually voyages to Mars, the ability to effectively carry out surface extra-vehicular activities (EVAs) ill be critical to overall mission success. This thesis investigates improving planetary EVAs via a support system to enable optimized mission operations. In order to develop a robustly effective aid capable of performing under the high time pressure, risk, and uncertainty inherent in space exploration, key surface operation factors are examined to understand to best fit role of automated support within complex, changing exploration situations. A detailed characterization of the makeup and challenges of planetary surface EVAs was used to establish a specific framework for maximizing the productivity of these missions. Recognizing the need for automated support in achieving such optimal performance, the presentation of methods by which all pertinent mission factors may be quantitatively modeled led to creation of a comprehensive automated mission support architecture. Based on this analysis and motivated by ongoing field testing, a prototype mission support system was developed with twofold intent: both for pre-mission planning and simulation as well as for real-time explorer navigation and re-planning. The prototype presents an intuitive interface where controllers may quickly represent a broad range of mission parameters and scenarios in order to determine a best course of action for immediate execution. Specifically, this system optimizes explorer traverses with respect to given cost functions via a novel implementation of the A* search algorithm. Developed plans may further be linked to a global positioning system to empower real-time team navigation.(cont.) Through the completion of experimental EVA simulations involving physical explorers on a remote terrain jointly controlled by a multi-university team, the developed system was shown to robustly respond to situational updates and contingencies to maintain optimal mission performance in near real-time, offering enhanced functionality where preceding systems fell short. The analysis closes with a discussion on the opportunities for such a system as well as potential areas for further improvement.by Joseph R. Essenburg.S.M

Coordinated Motion Planning for On-Orbit Satellite Inspection using a Swarm of Small-Spacecraft

This paper addresses the problem of how to plan optimal motion for a swarm of on-orbit servicing (OOS) small-spacecraft remotely inspecting a non-cooperative client spacecraft in Earth orbit. With the goal being to maximize the information gathered from the coordinated inspection, we present an integrated motion planning methodology that is a) fuel-efficient to ensure extended operation time and b) computationally-tractable to make possible on-board re-planning for improved exploration. Our method is decoupled into first offline selection of optimal orbits, followed by online coordinated attitude planning. In the orbit selection stage, we numerically evaluate the upper and lower bounds of the information gain for a discretized set of passive relative orbits (PRO). The algorithm then sequentially assigns orbits to each spacecraft using greedy heuristics. For the attitude planning stage, we propose a dynamic programming (DP) based attitude planner capable of addressing vehicle and sensor constraints such as attitude control system specifications, sensor field of view, sensing duration, and sensing angle. Finally, we validate the performance of the proposed algorithms through simulation of a design reference mission involving 3U CubeSats inspecting a satellite in low Earth orbit

Coordinated Motion Planning for On-Orbit Satellite Inspection using a Swarm of Small-Spacecraft

This paper addresses the problem of how to plan optimal motion for a swarm of on-orbit servicing (OOS) small-spacecraft remotely inspecting a non-cooperative client spacecraft in Earth orbit. With the goal being to maximize the information gathered from the coordinated inspection, we present an integrated motion planning methodology that is a) fuel-efficient to ensure extended operation time and b) computationally-tractable to make possible on-board re-planning for improved exploration. Our method is decoupled into first offline selection of optimal orbits, followed by online coordinated attitude planning. In the orbit selection stage, we numerically evaluate the upper and lower bounds of the information gain for a discretized set of passive relative orbits (PRO). The algorithm then sequentially assigns orbits to each spacecraft using greedy heuristics. For the attitude planning stage, we propose a dynamic programming (DP) based attitude planner capable of addressing vehicle and sensor constraints such as attitude control system specifications, sensor field of view, sensing duration, and sensing angle. Finally, we validate the performance of the proposed algorithms through simulation of a design reference mission involving 3U CubeSats inspecting a satellite in low Earth orbit

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Human-automation collaboration : decision support for lunar and planetary exploration

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, February 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 219-224).Balancing task allocation between humans and computers is crucial to the development of effective decision support systems. This thesis investigates the appropriate balance between humans and automation for geospatial path problem solving within the high-risk domain of human planetary surface exploration, where decisions are time critical and humans must adapt to uncertainty. In order to develop flexible and robust decision support systems for Lunar and planetary exploration, human-automation role allocations are examined to understand how humans conduct complex optimizations under different degrees of automated assistance. A work domain analysis of human planetary extravehicular activities (EVA) resulted in a framework for human-robotic planning, including key input variables, constraints, and outputs. Based on this analysis, a prototype path planning aid was developed. Under investigation was the use of partial automatic path generation and a visualization called levels of equal cost (LOEC, an aggregate cost map). Human-in-the-loop testing was employed to understand the effects of the automated assistance and different visualizations on path planning performance across multivariate cost functions. In two separate experiments, participants were tasked to make obstacle-free, least-costly paths based on given cost functions.(cont.) Analysis of the experimental results indicated that knowledge-based reasoning is best supported when operators conduct manual sensitivity analysis, a strategy that was absent when path generation was allocated to automation. Leveraging computer-generated paths resulted in overall better path performance but also led to automation bias and decreased situation awareness. With respect to visualizations, participants using elevation contours had lower cost paths and short task times when automation was reliable. However, the LOEC visualization helped participants initially create least-costly paths for the most complex cost function. Furthermore, LOEC visualization appears to promote manual sensitivity analysis, which was beneficial in the degraded automation condition, where low cost and time was observed for participants with this visualization. Finally, two types of sensitivity analysis were observed, one that leveraged the available "what if" tools and the other that created whole paths. While there was no difference in path costs across the strategies, the increase use of the manual sensitivity analysis (i.e., path modifications) led to decrease path cost errors. Supported by: NASA Harriett Jenkins Predoctoral Fellowship, Office of Naval Research, National Space Biomedical Research Institute (NSBRI/NASA) and the American Associate of University Women Dissertation Fellowship.by Jessica J. Márquez.Ph.D