Towards Error Handling in a DSL for Robot Assembly Tasks

This work-in-progress paper presents our work with a domain specific language (DSL) for tackling the issue of programming robots for small-sized batch production. We observe that as the complexity of assembly increases so does the likelihood of errors, and these errors need to be addressed. Nevertheless, it is essential that programming and setting up the assembly remains fast, allows quick changeovers, easy adjustments and reconfigurations. In this paper we present an initial design and implementation of extending an existing DSL for assembly operations with error specification, error handling and advanced move commands incorporating error tolerance. The DSL is used as part of a framework that aims at tackling uncertainties through a probabilistic approach.Comment: Presented at DSLRob 2014 (arXiv:cs/1411.7148

Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment

Autonomous Mechanical Assembly on the Space Shuttle: An Overview

The space shuttle will be equipped with a pair of 50 ft. manipulators used to handle payloads and to perform mechanical assembly operations. Although current plans call for these manipulators to be operated by a human teleoperator. The possibility of using results from robotics and machine intelligence to automate this shuttle assembly system was investigated. The major components of an autonomous mechanical assembly system are examined, along with the technology base upon which they depend. The state of the art in advanced automation is also assessed

The navigation system of the JPL robot

The control structure of the JPL research robot and the operations of the navigation subsystem are discussed. The robot functions as a network of interacting concurrent processes distributed among several computers and coordinated by a central executive. The results of scene analysis are used to create a segmented terrain model in which surface regions are classified by traversibility. The model is used by a path planning algorithm, PATH, which uses tree search methods to find the optimal path to a goal. In PATH, the search space is defined dynamically as a consequence of node testing. Maze-solving and the use of an associative data base for context dependent node generation are also discussed. Execution of a planned path is accomplished by a feedback guidance process with automatic error recovery

Reversible Execution for Robustness in Embodied AI and Industrial Robots

International audienceReversible computation is a computing paradigm where execution can progress backwards as well as in the usual, forward direction. It has found applications in many areas of computer science, such as circuit design, programming languages, simulation, modelling of chemical reactions, debugging and robotics. In this article, we give an overview of reversible computation focusing on its use in robotics. We present an example of programming industrial robots for assembly operations where we combine classical AI planning with reversibility and embodied AI to increase robustness and versatility of industrial robots

Reversible Computation: Extending Horizons of Computing

This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first

Robotic workcell analysis and object level programming

For many years robots have been programmed at manipulator or joint level without any real thought to the implementation of sensing until errors occur during program execution. For the control of complex, or multiple robot workcells, programming must be carried out at a higher level, taking into account the possibility of error occurrence. This requires the integration of decision information based on sensory data.Aspects of robotic workcell control are explored during this work with the object of integrating the results of sensor outputs to facilitate error recovery for the purposes of achieving completely autonomous operation.Network theory is used for the development of analysis techniques based on stochastic data. Object level programming is implemented using Markov chain theory to provide fully sensor integrated robot workcell control

Robot graphic simulation testbed

The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts