NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Sensing and Infrastructure Design for Robots: A Plan-Based Perspective

Currently there do not exist general-purpose robots, and the procedures by which robots are designed are often ad hoc. Additionally, designers must deal with considerations including budget, energy requirements, and the availability of parts, all of which complicate the problem. Abstract formal theories have, among other benefits, the potential to assist designers in developing and understanding the capabilities of novel robotic systems. Of particular interest is the concept of action-based sensors, which focus on the idea that a robot need only know enough to know what action to perform next. As a mathematical abstraction, action-based sensors prescribe actions to the agent; details of the sensor itself are irrelevant. From this information-oriented perspective, this concept also links planning directly to the design problem: the definition of what action “should” be taken depends upon the plan a robot is executing, serving to specify its desired behavior. While the theoretical abstractions of sensors are technology-neutral, we present ways to connect action-based sensors to the considerations and constraints faced by real robot designers. Action-based sensors have been formalized in terms of specific plans (informally those that take the fewest actions to achieve a goal), but there exist cases in which it is useful to consider other plans. In extending this formalization to include all plans, we find that certain plans have obstructions that prevent their expression as action-based sensors. We have developed an algorithm to remove these obstructions, which result from the interactions between a robot and its environment. After this, we move from the question of what a robot must sense about the environment to the question of how an environment should provide information. The use of infrastructure for spaces shared by multiple agents is another way in which designers can simplify tasks for agents. The complexity of this design problem arises from infrastructure’s ability to modify both what an agent observes and the outcome of actions. We present a method for modeling the impact of infrastructure to determine its utility to a given agent, and also consider how the utility of the infrastructure can vary depending on the differing needs of agents and how they make use of the environment. The present work, in addition to extending Erdmann’s original theory, focuses on the way in which information that must be retained by the agent can be contained within a plan’s structure. Use of a graph-based framework allows for us to identify if that structure is necessary for successful execution of the plan. This dissertation then shifts to a complementary design problem, examining the ability of infrastructure to externalize information and actuation requirements. It also presents a model for predicting the impact of introducing new infrastructure. Finally, it will explore the ways in which information can be used to estimate sensor failures in robots and bound the space of possible configurations. Transitioning from the design of robots and their environments to their operation, this dissertation also presents a method for estimating sensor failures. Through knowledge of the world structure and expected observations, inconsistencies can be tracked to form hypotheses on potential sensor failures. We introduce a lattice-based method of expressing these failures, as well as an algorithm for tracking inconsistencies. The algorithm allows for an often concise representation of a potentially exponential set of hypotheses, enabling use during a robot’s execution. This basis also allows for the robot to determine if a failure interferes with its ability to complete a task. We also present a method through which the sensors that are required for task completion can be determined at any point. The primary means to validate the theoretical results in this dissertation are a range of case studies. For action-based sensors, we consider several varieties of design problems including sensor selection and navigation problems. Moving beyond the sets of action-based sensors considered in these design problems, we also examine concise combinatorial representations for sets of sensors more generally, and apply these to settings involving robot self-diagnosis. For infrastructure, we provide a taxonomy as a guide by which to examine several different cases in which infrastructure is introduced to an environment. These case studies focus both on changes in agent behavior after being introduced, as well as ways in which the value of the introduced infrastructure can be deter-mined. For the identification of sensor failures, an example is also presented that demonstrates the concise nature of the model, particularly when compared to naïve methods

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

SpiNNaker: Fault tolerance in a power- and area- constrained large-scale neuromimetic architecture

AbstractSpiNNaker is a biologically-inspired massively-parallel computer designed to model up to a billion spiking neurons in real-time. A full-fledged implementation of a SpiNNaker system will comprise more than 105 integrated circuits (half of which are SDRAMs and half multi-core systems-on-chip). Given this scale, it is unavoidable that some components fail and, in consequence, fault-tolerance is a foundation of the system design. Although the target application can tolerate a certain, low level of failures, important efforts have been devoted to incorporate different techniques for fault tolerance. This paper is devoted to discussing how hardware and software mechanisms collaborate to make SpiNNaker operate properly even in the very likely scenario of component failures and how it can tolerate system-degradation levels well above those expected

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Coordination of Multirobot Systems Under Temporal Constraints

Multirobot systems have great potential to change our lives by increasing efficiency or decreasing costs in many applications, ranging from warehouse logistics to construction. They can also replace humans in dangerous scenarios, for example in a nuclear disaster cleanup mission. However, teleoperating robots in these scenarios would severely limit their capabilities due to communication and reaction delays. Furthermore, ensuring that the overall behavior of the system is safe and correct for a large number of robots is challenging without a principled solution approach. Ideally, multirobot systems should be able to plan and execute autonomously. Moreover, these systems should be robust to certain external factors, such as failing robots and synchronization errors and be able to scale to large numbers, as the effectiveness of particular tasks might depend directly on these criteria. This thesis introduces methods to achieve safe and correct autonomous behavior for multirobot systems. Firstly, we introduce a novel logic family, called counting logics, to describe the high-level behavior of multirobot systems. Counting logics capture constraints that arise naturally in many applications where the identity of the robot is not important for the task to be completed. We further introduce a notion of robust satisfaction to analyze the effects of synchronization errors on the overall behavior and provide complexity analysis for a fragment of this logic. Secondly, we propose an optimization-based algorithm to generate a collection of robot paths to satisfy the specifications given in counting logics. We assume that the robots are perfectly synchronized and use a mixed-integer linear programming formulation to take advantage of the recent advances in this field. We show that this approach is complete under the perfect synchronization assumption. Furthermore, we propose alternative encodings that render more efficient solutions under certain conditions. We also provide numerical results that showcase the scalability of our approach, showing that it scales to hundreds of robots. Thirdly, we relax the perfect synchronization assumption and show how to generate paths that are robust to bounded synchronization errors, without requiring run-time communication. However, the complexity of such an approach is shown to depend on the error bound, which might be limiting. To overcome this issue, we propose a hierarchical method whose complexity does not depend on this bound. We show that, under mild conditions, solutions generated by the hierarchical method can be executed safely, even if such a bound is not known. Finally, we propose a distributed algorithm to execute multirobot paths while avoiding collisions and deadlocks that might occur due to synchronization errors. We recast this problem as a conflict resolution problem and characterize conditions under which existing solutions to the well-known drinking philosophers problem can be used to design control policies that prevents collisions and deadlocks. We further provide improvements to this naive approach to increase the amount of concurrency in the system. We demonstrate the effectiveness of our approach by comparing it to the naive approach and to the state-of-the-art.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162921/1/ysahin_1.pd

LIPIcs, Volume 248, ISAAC 2022, Complete Volume

LIPIcs, Volume 248, ISAAC 2022, Complete Volum

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research