A formal approach to human robot collaborative assembly planning under uncertainty

For assembly planning, robots necessitate certain cognitive skills: high-level planning of actuation actions is needed to decide for their order, while geometric reasoning is needed to check their feasibility. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans behaviors, but also to ensure safer collaborations. We introduce a novel formal framework for collaborative assembly planning under uncertainty that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. We show the applicability of our approach over a furniture assembly domain, where a bi-manual Baxter robot collaborates with a human to assemble a table, with dynamic simulations and physical implementations. We also evaluate our approach experimentally in this domain with respect to quantitative and qualitative performance measures

Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach

For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans' behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture. This manuscript is under consideration for acceptance in TPLP.Comment: 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 15 page

Conservative Bridge Preparation By Using Natural Tooth As A Pontic With Ribbond Fiber: A Case Report At IIDH Islamabad

Summary: Traumatic damage to anterior teeth is a common form of dental injury, particularly in younger people. The abutment teeth only need to be slightly prepared for the conservative bridge preparation, as the name suggests. The final prosthesis can be fixed to the adjacent natural teeth in no time. This case reports a chair-side conservative and esthetic restoration in a 19-year-old girl who came with grade 3 mobility in her upper left central incisor by using her natural tooth as a pontic with ribbond fibre. Keywords: Conservative, Dental trauma, Ribbond fiber

Foliar applied proline and acetic acid improves growth and yield of wheat under salinity stress by improving photosynthetic pigments, physiological traits, antioxidant activities and nutrient uptake

Salinity stress (SS) is serious abiotic stress and a major limiting factor for crop productivity and global food security. In this context, the application of osmolytes is considered as an environmental friend approach to improve plant growth under SS. Thus, the present study was conducted to determine the impact of foliar applied proline (Pro) and acetic acid (AA) on growth, yield, physiological traits, photosynthetic pigments, ionic homeostasis and antioxidant activities of wheat under SS. The study contained SS levels 0, 6 and 12 dS m-1 and foliar spray of Pro and AA; water spray, Pro (75 mM), AA (15 mM) and AA (30 mM). The study was conducted in a completely randomized design with the factorial arrangement. Salinity stress significantly reduced wheat growth and yield, by decreasing relative water contents (-49.07%), photosynthetic pigments, free amino acids (FAA: -44.79%), total soluble proteins (TSP: -15.94%) and increasing the electrolyte leakage (EL: +27.28%), hydrogen peroxide (H2O2: +51.86%), and malondialdehyde (MDA: +36.91%) accumulation. The foliar spray of Pro and AA markedly improved the wheat growth and productivity through enhanced photosynthetic pigments, RWC, FAA, TSP, antioxidant activities (catalase: CAT, ascorbate peroxide: APX: peroxidase: POD), K+ and Ca2+ uptake and decreasing EL, MDA and H2O2 accumulation and restricted entry of toxic ions (Na+ and Cl-1).  Therefore, foliar application of AA and Pro effectively improves the growth and yield of wheat under SS by strengthening the antioxidant defense system, and maintaining ionic homeostasis and physiological performance

Human robot collaborative assembly planning: an answer set programming approach

For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans' behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture

ROSSMARie: A Domain-Specific Language To Express Dynamic Safety Rules and Recovery Strategies for Autonomous Robots

Ensuring functional safety is a critical challenge for autonomous robots, as they must operate reliably and predictably despite uncertainty. However, existing safety measures can over-constrain the system, limiting the robot’s availability to perform its assigned task. To address this problem, we propose a more flexible strategy that equips robots with theability to adapt to system failures and recover from those situations without human intervention. We extend a domain-specific language, Declarative Robot Safety (DeROS), whose runtime stops a robot whenever it violates a safety rule (e.g., proximity to a human). Our extended language, ROSSMARie, adds the capability to monitor whether a rule is no longer violated and to recover and resume robot operation. We validate ROSSMARie on the ROS-based industrial platform SkiROS2 and verify its effectiveness in achieving safety and availability. Our experiments demonstrate that our DSL extension ensuresfunctional safety while enabling robots to complete their tasks

Human-robot collaborative assembly planning using hybrid conditional planning

For assembly planning, robots necessitate certain cognitive skills: high-level planning of actuation actions is needed to decide for the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans’ behaviors but also to ensure safe collaborations. We introduce a novel formal framework for collaborative assembly planning that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. We evaluate this method by a set of experiments in a furniture assembly domain

EzSkiROS: A Case Study on Embedded Robotics DSLs to Catch Bugs Early

When we develop general-purpose robot software components, we rarely know the full context that they will execute in. This limits our ability to make predictions, including our ability to detect program bugs early. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. In this paper, we propose an approach to help developers find bugs early with minimal additional effort by using embedded Domain-Specific Languages (DSLs) that enforce early checks. We describe DSL design patterns suitable for the robotics domain and demonstrate our approach for DSL embedding in Python, using a case study on an industrial tool SkiROS2, designed for the composition of robot skills. We demonstrate our patterns on the embedded DSL EzSkiROS and show that our approach is effective in performing safety checks while deploying code on the robot, much earlier than at runtime. An initial study with SkiROS2 developers show that our DSL-based approach is useful for early bug detection and improving the maintainability of robot code