Multi-robot systems in cognitive factories: representation, reasoning, execution and monitoring

We propose the use of causality-based formal representation and automated reasoning methods from artificial intelligence to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as optimal planning and diagnostic reasoning. We present a framework that features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning. We embed this planning framework inside an execution and monitoring framework and show its applicability on multi-robot systems. In particular, we focus on two domains that are relevant to cognitive factories: i) a manipulation domain with multiple robots working concurrently / co-operatively to achieve a common goal and ii) a factory domain with multiple teams of robots utilizing shared resources. In the manipulation domain two pantograph robots perform a complex task that requires true concurrency. The monitoring framework checks plan execution for two sorts of failures: collisions with unknown obstacles and change of the world due to human interventions. Depending on the cause of the failures, recovery is done by calling the motion planner (to find a different trajectory) or the causal reasoner (to find a new task plan). Therefore, recovery relies on not only motion planning but also causal reasoning. We extend our planning and monitoring framework for the factory domain with multiple teams of robots by introducing algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy (e.g., robots may get broken or tasks may get reassigned to teams). We show the applicability of these algorithms on an intelligent factory scenario through dynamic simulations and physical experiments

Optimal global planning for cognitive factories with multiple teams of heterogeneous robots

We consider a cognitive factory domain with multiple teams of heterogeneous robots where the goal is for all teams to complete their tasks as soon as possible to achieve overall shortest delivery time for a given manufacturing order. Should the need arise, teams help each other by lending robots. This domain is challenging in the following ways: different capabilities of heterogeneous robots need to be considered in the model; discrete symbolic representation and reasoning need to be integrated with continuous external computations to find feasible plans (e.g., to avoid collisions); a coordination of the teams should be found for an optimal feasible global plan (with minimum makespan); in case of an encountered discrepancy/failure during plan execution, if the discrepancy/failure prevents the execution of the rest of the plan, then finding a diagnosis for the discrepancy/failure and recovering from the plan failure is required to achieve the goals. We introduce a formal planning, execution and monitoring framework to address these challenges, by utilizing logic-based formalisms that allow us to embed external computations in continuous spaces, and the relevant state-of-the-art automated reasoners. To find a global plan with minimum makespan, we propose a semi-distributed approach that utilizes a mediator subject to the condition that the teams and the mediator do not know about each other’s workspaces or tasks. According to this approach, 1) the mediator gathers sufficient information from the teams about when they can/need lend/borrow how many and what kind of robots, 2) based on this information, the mediator computes an optimal coordination of the teams and informs each team about this coordination, 3) each team computes its own optimal local plan to achieve its own tasks taking into account the information conveyed by the mediator as well as external computations to avoid collisions, 4) these optimal local plans are merged into an optimal global plan. For the first and the third stages, we utilize methods and tools of hybrid reasoning. For the second stage, we formulate the problem of finding an optimal coordination of teams that can help each other, prove its intractability, and describe how to solve this problem using existing automated reasoners. For the last stage, we prove the optimality of the global plan. For execution and monitoring of an optimal global plan, we introduce a formal framework that provides methods to diagnose failures due to broken robots, and to handle changes in manufacturing orders and in workspaces. We illustrate the applicability of our approaches on various scenarios of cognitive factories with dynamic simulations and physical implementation

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Qualitative Futures

Contains fulltext : 112461.pdf (publisher's version ) (Closed access

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

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

Manipulation of Articulated Objects using Dual-arm Robots via Answer Set Programming

The manipulation of articulated objects is of primary importance in Robotics, and can be considered as one of the most complex manipulation tasks. Traditionally, this problem has been tackled by developing ad-hoc approaches, which lack flexibility and portability. In this paper we present a framework based on Answer Set Programming (ASP) for the automated manipulation of articulated objects in a robot control architecture. In particular, ASP is employed for representing the configuration of the articulated object, for checking the consistency of such representation in the knowledge base, and for generating the sequence of manipulation actions. The framework is exemplified and validated on the Baxter dual-arm manipulator in a first, simple scenario. Then, we extend such scenario to improve the overall setup accuracy, and to introduce a few constraints in robot actions execution to enforce their feasibility. The extended scenario entails a high number of possible actions that can be fruitfully combined together. Therefore, we exploit macro actions from automated planning in order to provide more effective plans. We validate the overall framework in the extended scenario, thereby confirming the applicability of ASP also in more realistic Robotics settings, and showing the usefulness of macro actions for the robot-based manipulation of articulated objects. Under consideration in Theory and Practice of Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.Comment: 72 pages, 14 figure

Automation and robotics for the National Space Program

The emphasis on automation and robotics in the augmentation of the human centered systems as it concerns the space station is discussed. How automation and robotics can amplify the capabilities of humans is detailed. A detailed developmental program for the space station is outlined