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

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology

Survey of Command Execution Systems for NASA Spacecraft and Robots

NASA spacecraft and robots operate at long distances from Earth Command sequences generated manually, or by automated planners on Earth, must eventually be executed autonomously onboard the spacecraft or robot. Software systems that execute commands onboard are known variously as execution systems, virtual machines, or sequence engines. Every robotic system requires some sort of execution system, but the level of autonomy and type of control they are designed for varies greatly. This paper presents a survey of execution systems with a focus on systems relevant to NASA missions

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems

Petri Net Plans A framework for collaboration and coordination in multi-robot systems

Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling multi-robot systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration

Integrating Planning, Execution, and Learning to Improve Plan Execution

Algorithms for planning under uncertainty require accurate action models that explicitly capture the uncertainty of the environment. Unfortunately, obtaining these models is usually complex. In environments with uncertainty, actions may produce countless outcomes and hence, specifying them and their probability is a hard task. As a consequence, when implementing agents with planning capabilities, practitioners frequently opt for architectures that interleave classical planning and execution monitoring following a replanning when failure paradigm. Though this approach is more practical, it may produce fragile plans that need continuous replanning episodes or even worse, that result in execution dead-ends. In this paper, we propose a new architecture to relieve these shortcomings. The architecture is based on the integration of a relational learning component and the traditional planning and execution monitoring components. The new component allows the architecture to learn probabilistic rules of the success of actions from the execution of plans and to automatically upgrade the planning model with these rules. The upgraded models can be used by any classical planner that handles metric functions or, alternatively, by any probabilistic planner. This architecture proposal is designed to integrate off-the-shelf interchangeable planning and learning components so it can profit from the last advances in both fields without modifying the architecture.Publicad

Proceedings of the Third Symposium on Programming Languages and Software Tools : Kääriku, Estonia, August 23-24 1993

http://www.ester.ee/record=b1064507*es

Investigating the readability of formal specification languages

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.Includes bibliographical references (leaves 90-92).by Marc Kenton Zimmerman.S.M