Search Results (11)

To address the challenges of low automation in tunnel wet-spraying jumbos and the heavy reliance on manual expertise for ensuring the spraying quality, this study proposes a novel task planning method for tunnel spraying operations. First, the tunnel surface to be sprayed is aligned with the designed contour using a vehicle navigation method, enabling the estimation of the overbreak and underbreak volumes. These volumes are then utilized to hierarchically plan the spraying tasks (e.g., patching, filling, and surface smoothing). A concrete coating thickness prediction method is developed, incorporating static and dynamic coating accumulation models with key process parameters—spraying flow rate Q, air pressure P, and spraying distance H—as independent variables. Based on the required thickness for each task layer, operational parameters such as the spraying duration t and nozzle movement speed v are optimized. By analyzing the spray gun action combinations and integrating hierarchical task planning with parameter optimization, a Planning Domain Definition Language (PDDL) domain file and problem file are designed to generate the spray gun action sequences and paths via a planner. The experimental results demonstrate that the overbreak volume on the sprayed tunnel surface is reduced to approximately 3 cm after applying the planned sequences. The proposed method autonomously generates the task hierarchies and the corresponding spray gun actions based on the 3D morphology of the tunnel surface, effectively ensuring the spraying quality while significantly reducing the dependence on manual intervention. This approach provides a practical solution for enhancing automation and precision in tunnel spraying operations. Full article

Automated Planning and Scheduling (APS) is an area of artificial intelligence dedicated to generating efficient plans to achieve goals by optimizing objectives. This case study is based on a middle-mile segment of the dairy supply chain. This article focuses on applying and analyzing APS compared to the following classical optimization methods: mathematical modeling based on Mixed-Integer Programming (MILP) and the Genetic Algorithm (GA). The language supported for APS modeling is Planning Domain Definition Language (PDDL), and the temporal solver used is the OPTIC planner. Optimization methods are guided by a mathematical model developed specifically for the research scope, considering production, inventory, and transportation conditions and constraints. Dairy products are highly perishable; therefore, the main optimization objective is to minimize Tmax, i.e., the total time to meet demand, ensuring that the products are available at the distribution center with a viable shelf life for commercialization. The APS application showed limitations compared to the other optimization approaches, with the Exact Method proving the most efficient. Finally, all algorithms, models, and results are available on GitHub, aiming to foster further research and enhance operational efficiency in the dairy sector through optimization. Full article

This paper introduces, as a proof of concept, a tool chain for automated control and simulation of a robot team in the domain of production of carbon-fiber-reinforced polymers. The starting point is a CAD construction of a simple aviation component from which single cut pieces of carbon fiber, together withtheir properties, are extracted. Using this information and the layout of a given robot cell, various possibilities of assignments of cut pieces to grippers and robots or robot teams are determined. Subsequently, two approaches using an PDDL solver are introduced, with the goal of finding a scheduling for the lay-up process. Finally, the resulting process is simulated using a physics and rendering engine. The main purpose of this paper is to show the feasibility of such an approach; we do not concentrate on the optimization of single process steps and other details. Due to the modular structure of our approach, extensions and optimizations of the single blocks are easy to integrate. At the moment, digitization and automated control are little explored areas in the domain of production technology using pick and place processes in the aerospace industry. We think that our work will lead to further research in this direction. Full article

This article addresses the problem of automating a multi-arm pick-and-place robotic system. The objective is to optimize the execution time of a task simultaneously performed by multiple robots, sharing the same workspace, and determining the order of operations to be performed. Due to its ability to address decision-making problems of all kinds, the system is modeled under the mathematical framework of the Markov Decision Process (MDP). In this particular work, the model is adjusted to a deterministic, single-agent, and fully observable system, which allows for its comparison with other resolution methods such as graph search algorithms and Planning Domain Definition Language (PDDL). The proposed approach provides three advantages: it plans the trajectory to perform the task in minimum time; it considers how to avoid collisions between robots; and it automatically generates the robot code for any robot manufacturer and any initial objects’ positions in the workspace. The result meets the objectives and is a fast and robust system that can be safely employed in a production line. Full article

The recognition of human activities from video sequences and their transformation into a machine-readable form is a challenging task, which is the subject of many studies. The goal of this project is to develop an automated method for analyzing, identifying and processing motion capture data into a planning language. This is performed in a cooking scenario by recording the pose of the acting hand. First, predefined side actions are detected in the dataset using classification. The remaining frames are then clustered into main actions. Using this information, the known initial positions and virtual object tracking, a machine-readable planning domain definition language (PDDL) is generated. Full article

In the industrial domain, maintenance is essential to guarantee the correct operations, availability, and efficiency of machinery and systems. With the advent of Industry 4.0, solutions based on machine learning can be used for the prediction of future failures by exploiting historical failure data. Most of the time, these historical data have been collected by companies without a specific structure, schema, or even best practices, resulting in a potential loss of knowledge. In this paper, we analyze the historical data on maintenance alerts of the components of a revamping topping plant (referred to as RT2) belonging to the SARAS group. This analysis is done in collaboration with the ITALTELECO company, a partner of SARAS, that provided the necessary data. The pre-processing methodology to clean and fill these data and extract features useful for a prediction task will be shown. More in detail, we show the process to fill missing fields of these data to provide (i) a category for each fault by using simple natural language processing techniques and performing a clustering, and (ii) a data structure that can enable machine learning models and statistical approaches to perform reliable failure predictions. The data domain in which this methodology is applied is oil and gas, but it may be generalized and reformulated in various industrial and/or academic fields. The ultimate goal of our work is to obtain a procedure that is simple and can be applied to provide strategic support for the definition of an adequate maintenance plan. Full article

Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against various situations. In this paper, we propose a semantic navigation framework based on a Triplet Ontological Semantic Model (TOSM) to manage various conditions affecting the execution of tasks. The framework allows robots with different kinematics to perform tasks in indoor and outdoor environments. We define the TOSM-based semantic knowledge and generate a semantic map for the domains. The robots execute tasks according to their characteristics by converting inferred knowledge to Planning Domain Definition Language (PDDL). Additionally, to make the framework sustainable, we determine a policy of maintaining the map and re-planning when in unexpected situations. The various experiments on four different kinds of robots and four scenarios validate the scalability and reliability of the proposed framework. Full article

In a multi-agent system, multi-job assignment is an optimization problem that seeks to minimize total cost. This can be generalized as a complex problem in which several variations of vehicle routing problems are combined, and as an NP-hard problem. The parameters considered include the number of agents and jobs, the loading capacity, the speed of the agents, and the sequence of consecutive positions of jobs. In this study, a deep neural network (DNN) model was developed to solve the job assignment problem in a constant time regardless of the state of the parameters. To generate a large training dataset for the DNN, the planning domain definition language (PDDL) was used to describe the problem, and the optimal solution that was obtained using the PDDL solver was preprocessed into a sample of the dataset. A DNN was constructed by concatenating the fully-connected layers. The assignment solution obtained via DNN inference increased the average traveling time by up to 13% compared with the ground cost. As compared with the ground cost, which required hundreds of seconds, the DNN execution time was constant at approximately 20 ms regardless of the number of agents and jobs. Full article

As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment. Full article

Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard Planning Domain Definition Language (PDDL) format, are still very hard to formalize for non expert engineers when the use case to model is complex. Human Robot Interaction (HRI) is one of those complex environments. This manuscript describes the rationale followed to design a planning model able to control social autonomous robots interacting with humans. It is the result of the authors’ experience in modeling use cases for Social Assistive Robotics (SAR) in two areas related to healthcare: Comprehensive Geriatric Assessment (CGA) and non-contact rehabilitation therapies for patients with physical impairments. In this work a general definition of these two use cases in a unique planning domain is proposed, which favors the management and integration with the software robotic architecture, as well as the addition of new use cases. Results show that the model is able to capture all the relevant aspects of the Human-Robot interaction in those scenarios, allowing the robot to autonomously perform the tasks by using a standard planning-execution architecture. Full article

Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles. Full article