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Key optimal elements for space qualification plans of photonic devices are overviewed. Device parameters and qualifying procedures were discussed to assure the reliability of newly developed photonic devices needed for potential usage in space environments.
In this paper we describe an automated planning system that selects the optimal target location to drop a communication node in an unknown cave environment to maximize communication coverage while minimizing the risk of violating safety constraints for all robots traversing the area based on local environmental and operational constraints.
The OSIRIS-REx sample return capsule landed safely in the Utah desert on September 24, 2023. Starting with the departure from asteroid Bennu in May 2021 through the release of the sample capsule and its arrival at the edge of Earth’s atmosphere hours later, this phase of the mission was planned and analyzed thoroughly to optimize the probability of a successful sample return in a large number of nominal and off-nominal scenarios, all while emphasizing spacecraft safety. This paper discusses the spacecraft and sample capsule Earth return and atmospheric entry trajectory design, maneuver analysis, and flight performance, which ultimately helped lead to a successful sample return and initiation of the OSIRIS-APEX extended mission.
As the nation plans manned missions that go far beyond Earth orbit to Mars, there is an urgent need for a robust, disciplined systems engineering methodology that can identify an optimized Environmental Control and Life Support (ECLSS) architecture for long duration deep space missions. But unlike the previously used Equivalent System Mass (ESM), the method must be inclusive of all driving parameters and emphasize the economic analysis of life support system design. The key parameter for this analysis is Life Cycle Cost (LCC). LCC takes into account the cost for development and qualification of the system, launch costs, operational costs, maintenance costs and all other relevant and associated costs. Additionally, an effective methodology must consider system technical performance, safety, reliability, maintainability, crew time, and other factors that could affect the overall merit of the life support system.
This paper describes a time-sensitive approach to climate change projections, developed as part of New York City's climate change adaptation process, that has provided decision support to stakeholders from 40 agencies, regional planning associations, and private companies. The approach optimizes production of projections given constraints faced by decision makers as they incorporate climate change into long-term planning and policy. New York City stakeholders, who are well-versed in risk management, helped pre-select the climate variables most likely to impact urban infrastructure, and requested a projection range rather than a single 'most likely' outcome. The climate projections approach is transferable to other regions and consistent with broader efforts to provide climate services, including impact, vulnerability, and adaptation information. The approach uses 16 Global Climate Models (GCMs) and three emissions scenarios to calculate monthly change factors based on 30-year average future time slices relative to a 30- year model baseline. Projecting these model mean changes onto observed station data for New York City yields dramatic changes in the frequency of extreme events such as coastal flooding and dangerous heat events. Based on these methods, the current 1-in-10 year coastal flood is projected to occur more than once every 3 years by the end of the century, and heat events are projected to approximately triple in frequency. These frequency changes are of sufficient magnitude to merit consideration in long-term adaptation planning, even though the precise changes in extreme event frequency are highly uncertain
This paper describes the operational design and execution of the Artemis I trajectory. It was an operationally complex trajectory with powered lunar flybys and insertion into a Distant Retrograde Orbit (DRO). A joint team of trajectory analysts at the NASA Johnson Space Center (JSC) were responsible for the design and operation of nominal and off-nominal in-space trajectories. A process was developed to convert optimized reference trajectories into Orion burn plans that could be uplinked to the vehicle. During the mission, the joint flight controller and engineering team continuously evaluated upcoming translational burns using actual vehicle conditions, monitored the trajectory for opportunities to re-optimize the trajectory in order to reduce propellant usage, and prepared for potential off-nominal scenarios. Overall Orion in-space trajectory performance is compared tomission designs to demonstrate the success of the design and operations work-flows.
Simulated annealing is used to solve a minimum fuel trajectory problem in the space station environment. The environment is unique because the space station will define the first true multivehicle environment in space. The optimization yields surfaces which are potentially complex, with multiple local minima. Because of the likelihood of these local minima, descent techniques are unable to offer robust solutions. Other deterministic optimization techniques were explored without success. The simulated annealing optimization is capable of identifying a minimum-fuel, two-burn trajectory subject to four constraints. Furthermore, the computational efforts involved in the optimization are such that missions could be planned on board the space station. Potential applications could include the on-site planning of rendezvous with a target craft of the emergency rescue of an astronaut. Future research will include multiwaypoint maneuvers, using a knowledge base to guide the optimization.
We propose an explorer that uses a flexible problem-solver with a significant capacity to adapt its behavior.
A trajectory design process is presented for a proposed National Launch System (NLS) vehicle configuration. A software trajectory planner was developed to generate near optimal pitch plane trajectories for variable mission plans, variable launch site selections, and last minute wind conditions at the time of launch. In addition, the trajectory planner performs in-flight near optimal trajectory redesign in the event of an engine-out or unexpected wind dispersions. The trajectory planner iteratively simulates trial trajectories defined by variables corresponding to a parameterized trajectory shape. The trajectory planner optimizes these trajectory parameters according to the fuel used to realize the trajectories. For prelaunch scenarios, the trajectory planner generated near optimal trajectories of consistent fuel performance irrespective of the winds encountered at the launch site. In-flight trajectory redesigns were also shown to be effective in fulfilling the mission plan and preserving the structural integrity of the vehicle. The in-flight redesign results indicate that closed-loop guidance in the early stages of ascent is highly effective in accommodating engine failures or wind dispersions. All trajectory designs were verified using a 6DOF simulation with flight controllers.
The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.
In November 2007, the International Space Station (ISS) will have supported seven years of continuous presence in space, with 15 Expeditions completed. These years have been characterized by the numerous technical challenges of assembly as well as operational and logistical challenges related to the availability of transportation by the Space Shuttle. During this period, an active set of early research objectives have also been accomplished alongside the assembly. This paper will review the research accomplishments on ISS to date, with the objective of drawing insights on the potential of future research following completion of ISS assembly. By the end of Expedition 15, an expected 121 U.S.-managed investigations will have been conducted on ISS, with 91 of these completed. Many of these investigations include multiple scientific objectives, with an estimated total of 334 scientists served. Through February 2007, 101 scientific publications have been identified. Another 184 investigations have been sponsored by ISS international partners, which independently track their scientists served and results publication. Through this survey of U.S. research completed on ISS, three different themes will be addressed: (1) How have constraints on transportation of mass to orbit affected the types of research successfully completed on the ISS to date? What lessons can be learned for increasing the success of ISS as a research platform during the period following the retirement of the Space Shuttle? (2) How have constraints on crew time for research during assembly and the active participation of crewmembers as scientists affected the types of research successfully completed on the ISS to date? What lessons can be learned for optimizing research return following the increase in capacity from 3 to 6 crewmembers (planned for 2009)? What lessons can be learned for optimizing research return after assembly is complete? (3) What do early research results indicate about the various scientific disciplines represented in investigations on ISS? Are there lessons specific to human research, technology development, life sciences, and physical sciences that can be used to increase future research accomplishments? Research has been conducted and completed on ISS under a set of challenging constraints during the past 7 years. The history of research accomplished on ISS during this time serves as an indicator of the value and potential of ISS when full utilization begins. By learning from our early experience in completing research on ISS, NASA and our partners can be positioned to optimize research returns as a full crew complement comes onboard, assembly is completed, and research begins in full.
A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.
A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.
A survey of applications of mathematical programming methods is used to improve the design of helicopters and their components. Applications of multivariable search techniques in the finite dimensional space are considered. Five categories of helicopter design problems are considered: (1) conceptual and preliminary design, (2) rotor-system design, (3) airframe structures design, (4) control system design, and (5) flight trajectory planning. Key technical progress in numerical optimization methods relevant to rotorcraft applications are summarized.
This paper presents a sampling-based motion planning algorithm for real-time and propellant-optimized autonomous spacecraft trajectory generation in near-circular orbits. Specifically, this paper leverages recent algorithmic advances in the field of robot motion planning to the problem of impulsively actuated, propellant- optimized rendezvous and proximity operations under the Clohessy-Wiltshire-Hill dynamics model. The approach calls upon a modified version of the FMT* algorithm to grow a set of feasible trajectories over a deterministic, low-dispersion set of sample points covering the free state space. To enforce safety, the tree is only grown over the subset of actively safe samples, from which there exists a feasible one-burn collision-avoidance maneuver that can safely circularize the spacecraft orbit along its coasting arc under a given set of potential thruster failures. Key features of the proposed algorithm include 1) theoretical guarantees in terms of trajectory safety and performance, 2) amenability to real-time implementation, and 3) generality, in the sense that a large class of constraints can be handled directly. As a result, the proposed algorithm offers the potential for widespread application, ranging from on-orbit satellite servicing to orbital debris removal and autonomous inspection missions.
A systematic approach for monitoring U.S. air traffic was developed in the context of system-wide planning and control. Towards this end, a network optimization model with nonlinear objectives was chosen as the central element in the planning/control system. The network representation was selected because: (1) it provides a comprehensive structure for depicting essential aspects of the air traffic system, (2) it can be solved efficiently for large scale problems, and (3) the design can be easily communicated to non-technical users through computer graphics. Briefly, the network planning models consider the flow of traffic through a graph as the basic structure. Nodes depict locations and time periods for either individual planes or for aggregated groups of airplanes. Arcs define variables as actual airplanes flying through space or as delays across time periods. As such, a special case of the network can be used to model the so called flow control problem. Due to the large number of interacting variables and the difficulty in subdividing the problem into relatively independent subproblems, an integrated model was designed which will depict the entire high level (above 29000 feet) jet route system for the 48 contiguous states in the U.S. As a first step in demonstrating the concept's feasibility a nonlinear risk/cost model was developed for the Indianapolis Airspace. The nonlinear network program --NLPNETG-- was employed in solving the resulting test cases. This optimization program uses the Truncated-Newton method (quadratic approximation) for determining the search direction at each iteration in the nonlinear algorithm. It was shown that aircraft could be re-routed in an optimal fashion whenever traffic congestion increased beyond an acceptable level, as measured by the nonlinear risk function.
In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.
Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project.