An ontology for failure interpretation in automated planning and execution

This is a post-peer-review, pre-copyedit version of an article published in ROBOT - Iberian Robotics Conference. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-35990-4_31”.Autonomous indoor robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions, where task and motion planning levels are coupled. In both planning levels and execution phase, several source of failures can occur. In this paper, an interpretation ontology covering several sources of failures in automated planning and also during the execution phases is introduced with the purpose of working the planning more informed and the execution prepared for recovery. The proposed failure interpretation ontological module covers: (1) geometric failures, that may appear when e.g. the robot can not reach to grasp/place an object, there is no free-collision path or there is no feasible Inverse Kinematic (IK) solution. (2) hardware related failures that may appear when e.g. the robot in a real environment requires to be re-calibrated (gripper or arm), or it is sent to a non-reachable configuration. (3) software agent related failures, that may appear when e.g. the robot has software components that fail like when an algorithm is not able to extract the proper features. The paper describes the concepts and the implementation of failure interpretation ontology in several foundations like DUL and SUMO, and presents an example showing different situations in planning demonstrating the range of information the framework can provide for autonomous robotsPeer ReviewedPostprint (author's final draft

Assembly planning in cluttered environments through heterogeneous reasoning

Assembly recipes can elegantly be represented in description logic theories. With such a recipe, the robot can figure out the next assembly step through logical inference. However, before performing an action, the robot needs to ensure various spatial constraints are met, such as that the parts to be put together are reachable, non occluded, etc. Such inferences are very complicated to support in logic theories, but specialized algorithms exist that efficiently compute qualitative spatial relations such as whether an object is reachable. In this work, we combine a logic-based planner for assembly tasks with geometric reasoning capabilities to enable robots to perform their tasks under spatial constraints. The geometric reasoner is integrated into the logic-based reasoning through decision procedures attached to symbols in the ontology.Peer ReviewedPostprint (author's final draft

Intention recognition in manufacturing applications

In this article, we present a novel approach to intention recognition, based on the recognition and representation of state information in a cooperative human-robot environment. States are represented by a combination of spatial relations along with cardinal direction information. The output of the Intention Recognition Algorithms will allow a robot to help a human perform a perceived operation or, minimally, not cause an unsafe situation to occur. We compare the results of the Intention Recognition Algorithms to those of an experiment involving human subjects attempting to recognize the same intentions in a manufacturing kitting domain. In almost every case, results show that the Intention Recognition Algorithms performed as well, if not better, than a human performing the same activity.Scopu

Runtime Verification of the {ARIAC} Competition: Can a Robot be Agile and Safe at the same time?

none6noneAngelo Ferrando ; Zeid Kootbally ; Pavel Piliptchak ; Rafael C. Cardoso ; Craig Schlenoff ; Michael FisherFerrando, Angelo; Kootbally, Zeid; Piliptchak, Pavel; Cardoso, Rafael C.; Schlenoff, Craig; Fisher, Michae

Enabling codesharing in Rescue Simulation with USARSim/ROS

The Robot Operating System (ROS) has been steadily gaining popularity among robotics researchers as an open source framework for robot control. The Unified System for Automation and Robot Simulation (USARSim) has been used for many years by robotics researchers and developers as a validated framework for simulation. This paper presents a new ROS node that is designed to seamlessly interface between ROS and USARSim. It provides for automatic configuration of ROS transforms and topics to allow for full utilization of the simulated hardware. The design of the new node as well as examples of its use for mobile robot inside the RoboCup Rescue Simulation League are presented

Modular Robot Software Framework for the Intelligent and Flexible Composition of Its Skills

Part 3: Sustainability and Reconfigurability of Manufacturing SystemsInternational audienceCurrent trends such as mass customization necessitate an agile and transformable production. In this context, robotic technologies are seen as a key enabler. But, to date, industrial robots lack the flexibility to easily adapt to changing needs. Therefore, a modular skill-based software framework aiming for free configurability is presented here. A generic task control allows varying incoming tasks to be processed, based on the actual skills of the robot. In this way, the flexible composition of a robot’s skills can be achieved, according to the actual situation