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

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

An ASP-Based Approach to Scheduling Pre-operative Assessment Clinic

The problem of scheduling Pre-Operative Assessment Clinic (PAC) consists of assigning patients to a day for the exams needed before a surgical procedure, taking into account patients with different priority levels, due dates, and operators availability. Realizing a satisfying schedule is of upmost importance for a clinic, since delay in PAC can cause delay in the subsequent phases, causing a decrease in patients’ satisfaction. In this paper, we divide the problem in two sub-problems: In the first sub-problem patients are assigned to a day taking into account a default list of exams; then, in the second sub-problem, having the actual list of exams needed by each patient, we use the results of the first sub-problem to assign a starting time to each exam. We first present a mathematical formulation for both problems. Then, we present solutions based on Answer Set Programming (ASP): The first solution is a genuine ASP encoding of the sub-problems, while the second introduces domain-specific optimizations. Experiments show that both solutions provide satisfying results in short time, while the second is able to prove optimality faster

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

Implementation of Branch-Point-Tolerant Wavefront Reconstructor for Strong Turbulence Compensation

Branch points arise in optical transmissions due to strong atmospheric turbulence, long propagation paths, or a combination of both. Unfortunately, these conditions are very often present in desired operational scenarios for laser weapon systems, optical communication, and covert imaging, which suffer greatly when traditional adaptive optics systems either cannot sense branch points or implement non-optimal methods for sensing and correcting branch points. Previous research by Pellizzari presented a thorough analysis of various novel branch point tolerant reconstructors in the absence of noise. In this research a realistic model of the Air Force Institute of Technology\u27s adaptive optics system is developed and used for the first realistic tests of these branch point tolerant reconstructors. Utilizing a self-referencing interferometer as the high-order wavefront sensor -- this type of sensor being theoretically immune to scintillation -- this effort has extended previous theoretical work by adding realistic noise effects to the SRI\u27s measurements before reconstructing the wavefronts and applying the control law. Not only is the adaptive optics correction shown to outperform the traditional techniques by as much as 126%, but several new theories and refinements to existing theories were discovered along the way. This study provides a foundation to guide hardware implementation in the future, where noise effects will be present

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications