Understanding the reasoning behind the behavior of an automated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the invalid activity to determine the missing constraint. If a scheduler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to alter constraints to achieve the desired schedule. In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for operational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the constraints provided; and (3) provides guidance on potential constraint relaxations to enable the activities to schedule in future scheduler runs.
Olis Robotics, a leader in next-generation AI-driven software for remote robotics in dynamic environments in subsea, terrestrial, and space applications, today announced that it has been selected by Maxar Technologies to provide robotic operator planning software for Maxar's Sample Acquisition, Morphology Filtering, and Probing of Lunar Regolith (SAMPLR) robotic arm. The arm will be mounted to a yet-to-be-named lander as one of 12 payloads that NASA selected as part of its Artemis program to send the first woman and the next man to the Moon by 2024 in preparation for a human mission to Mars. Olis Robotics' operator planning software will solve for the extreme latency experienced while operating robotics on the lunar surface by enabling operators to simulate and plan movements from the ground. Olis' software will provide a 3D visualization of the lunar environment and intuitive controls for operators on Earth, providing enhanced control during exploration missions. "The moon provides an excellent proving ground for our robotic operator planning software, allowing operators on Earth to successfully complete more complex missions faster and safer than ever before," explained Olis Robotics CEO Don Pickering.
Over the past decade, the NASA Autonomous Systems and Operations (ASO) project has developed and demonstrated numerous autonomy enabling technologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission operations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission operations functions. Vehicle System Management uses AI techniques to turn the astronaut's spacecraft into a robot, allowing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. We first describe human spaceflight mission operations capabilities. We then describe the ASO project, and the development and demonstration performed by ASO since 2011. We will describe the AI techniques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learning based approaches. Finally, we conclude with an assessment of future development needs for AI to enable NASA's future Exploration missions.
To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.
The flight plan for the ill-fated 1970 Apollo 13 mission which had to be altered following the an emergency on board has been unearthed. The 352-page document bears the annotations made by all three crew members recording in detail the actions they had to take after an explosion ripped off part of their space ship. Apollo 13 was to be the third mission to land on the moon, but just under 56 hours into flight, an oxygen tank explosion forced the crew to cancel the lunar landing and move into the Aquarius lunar module to return back to Earth. The drama that unfolded during the Apollo 13 mission was re-told in the Hollywood film starring Tom Hanks, Kevin Bacon as Swigert and the late Bill Paxton as Haise. The mission is famous for the line ''Houston, we have had a problem here', which is often misquoted as'Houston, we have a problem'.
The International Conference on Automated Planning and Scheduling (ICAPS) is the premier forum for researchers and practitioners in planning and scheduling - two technologies that are critical to manufacturing, space systems, software engineering, robotics, education, and entertainment. The ICAPS conference resulted from merging two bi-annual conferences, namely the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). The primary objectives of ICAPS are to further the field of automated planning and scheduling through the organization of technical meetings, including the annual ICAPS conference, through the organization of summer schools, tutorials and training activities at various events, through the organization of planning and scheduling competitions, benchmarking and other means of advancing and assessing the state of the art in the field, by promoting the involvement of young scientists in the field through scholarships and other means, and by promoting and disseminating publications, planning and scheduling systems, domains, simulators, software tools and technical material. The ICAPS 2016 conference was held from June 12-17, 2016 in London, UK. Conference chairs are Andrew Coles and Daniele Magazzeni.
But it was one giant leap for computer-kind, with a state of the art artificial intelligence system being given primary command of a spacecraft. Known as Remote Agent, the software operated NASA's Deep Space 1 spacecraft and its futuristic ion engine during two experiments that started on Monday, May 17, 1999. For two days Remote Agent ran on the on-board computer of Deep Space 1, more than 60,000,000 miles (96,500,000 kilometers) from Earth. The tests were a step toward robotic explorers of the 21st century that are less costly, more capable and more independent from ground control. A second remote agent experiment was conducted on Friday, May 21, starting at 7:15 a.m.
As NASA explores destinations beyond the Moon, the distance between Earth and spacecraft will increase communication delays between astronauts and Mission Control. Today, astronauts coordinate with Mission Control to request assistance and await approval to perform tasks. Many of these coordination tasks require multiple exchanges of information, (for example, taking turns). In the presence of long communication delays, the length of time between turns may lead to inefficiency, or increased mission risk. Future astronauts will need software-based decision aids to enable them to work autonomously from Mission Control. These tools require the right combination of mission operations functions, for example, automated planning and fault management, troubleshooting recommendations, easy to access information, and just-in-time training. Ensuring these elements are properly designed and integrated requires an integrated human factors approach. This article describes a recent demonstration of autonomous mission operations using a novel software-based decision aid onboard the International Space Station. We describe how this new technology changes the way astronauts coordinate with mission control, and how the lessons learned from these early demonstrations will enable the operational autonomy needed to ensure astronauts can safely journey to Mars, and beyond.
This article describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). I present the approach taken to reduce the complexity of the activity-planning task in order to perform it effectively under the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft’s orbit, given their dynamic nature due to the continually updated orbit determination solution.
Planning an interplanetary trajectory is a very complex task, traditionally accomplished by domain experts using computer-aided design tools. Recent advances in trajectory optimization allow automation of part of the trajectory design but have yet to provide an efficient way to select promising planetary encounter sequences. In this work, we present a heuristic-free approach to automated trajectory planning (including the encounter sequence planning) based on Monte Carlo Tree Search (MCTS). We discuss a number of modifications to traditional MCTS unique to the domain of interplanetary trajectory planning and provide results on the Rosetta and Cassini-Huygens interplanetary mission design problems. The resulting heuristic-free method is found to be orders of magnitude more efficient with respect to a standard tree search with heuristic-based pruning which is the current state-of-the art in this domain.