Planning robot actions under position and shape uncertainty

Geometric uncertainty may cause various failures during the execution of a robot control program. Avoiding such failures makes it necessary to reason about the effects of uncertainty in order to implement robust strategies. Researchers first point out that a manipulation program has to be faced with two types of uncertainty: those that might be locally processed using appropriate sensor based motions, and those that require a more global processing leading to insert new sensing operations. Then, they briefly describe how they solved the two related problems in the SHARP system: how to automatically synthesize a fine motion strategy allowing the robot to progressively achieve a given assembly relation despite position uncertainty, and how to represent uncertainty and to determine the points where a given manipulation program might fail

Conditional Task and Motion Planning through an Effort-based Approach

This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation

Monitoring robot actions for error detection and recovery

Reliability is a serious problem in computer controlled robot systems. Although robots serve successfully in relatively simple applications such as painting and spot welding, their potential in areas such as automated assembly is hampered by programming problems. A program for assembling parts may be logically correct, execute correctly on a simulator, and even execute correctly on a robot most of the time, yet still fail unexpectedly in the face of real world uncertainties. Recovery from such errors is far more complicated than recovery from simple controller errors, since even expected errors can often manifest themselves in unexpected ways. Here, a novel approach is presented for improving robot reliability. Instead of anticipating errors, researchers use knowledge-based programming techniques so that the robot can autonomously exploit knowledge about its task and environment to detect and recover from failures. They describe preliminary experiment of a system that they designed and constructed

Flexible human-robot cooperation models for assisted shop-floor tasks

The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process. In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person but also a precious capability for efficiently dealing with unexpected events. In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier

Contact detection and contact motion for error recovery in the presence of uncertainties

Due to various kinds of uncertainties, a robot motion may fail and result in some unintended contact between the object held by the robot and the environment, which greatly hampers robotics applications on tasks with high-precision requirements, such as assembly tasks. Aiming at automatically recovering a robotic task from such a failure, this paper discusses, in the presence of uncertainties, contact detection based on contact motion for recovery. It presents a framework for on-line recognizing contacts using multiple sensor modalities in the presence of sensing uncertainties and means for ensuring successful compliant motions in the presence of sensing and control uncertainties

Achieving reliability using behavioural modules in a robotic assembly system

The research in this thesis looks at improving the reliability of robotic as¬ sembly while still retaining the flexibility to change the system to cope with dif¬ ferent assemblies. The lack of a truly flexible robotic assembly system presents a problem which current systems have yet to overcome. An experimental sys¬ tem has been designed and implemented to demonstrate the ideas presented in this work. Runs of this system have also been performed to test and assess the scheme which has been developed.The Behaviour-based SOMASS system looks at decomposing the task into modular units, called Behavioural Modules, which reliably perform the as¬ sembly task by using variation reducing strategies. The thesis work looks at expanding this framework to produce a system which relaxes the constraints of complete reliability within a Behavioural Module by embedding these in a re¬ liable system architecture. This means that Behavioural Modules do not have to guarantee to successfully perform their given task but instead can perform it adequately, with occasional failures dealt with by the appropriate introduction of alternative actionsTo do this, the concepts of Exit States, the Ideal Execution Path, and Alter¬ native Execution Paths have been described. The Exit State of a Behavioural Module gives an indication of the control path which has actually been taken during its execution. This information, along with appropriate information available to the execution system (such as sensor and planner data), allows the Ideal Execution Path and Alternative Execution Paths to be defined. These show, respectively, the best control path through the system (as determined by the system designer) and alternative control routes which can be taken when necessary

A Stack-of-Tasks Approach Combined with Behavior Trees: a New Framework for Robot Control

Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this approach solves a sequence of Quadratic Programs (QP) at each time-step, without taking into account any temporal state evolution, it is suitable for dealing with local disturbances. However, its limitation lies in the handling of situations that require non-quadratic objectives to achieve a specific goal, as well as situations where countering the control disturbance would require a locally suboptimal action. Recent works address this shortcoming by exploiting Finite State Machines (FSMs) to compose the tasks in such a way that the robot does not get stuck in local minima. Nevertheless, the intrinsic trade-off between reactivity and modularity that characterizes FSMs makes them impractical for defining reactive behaviors in dynamic environments. In this letter, we combine the SoT control strategy with Behavior Trees (BTs), a task switching structure that addresses some of the limitations of the FSMs in terms of reactivity, modularity and re-usability. Experimental results on a Franka Emika Panda 7-DOF manipulator show the robustness of our framework, that allows the robot to benefit from the reactivity of both SoT and BTs

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