Recent advancements in manufacturing have a growing demand for fast, automatic prototyping (i.e. assembly and disassembly) capabilities to meet users' needs. This paper studies automatic rapid LEGO prototyping, which is devoted to constructing target LEGO objects that satisfy individual customization needs and allow users to freely construct their novel designs. A construction plan is needed in order to automatically construct the user-specified LEGO design. However, a freely designed LEGO object might not have an existing construction plan, and generating such a LEGO construction plan requires a non-trivial effort since it requires accounting for numerous constraints (e.g. object shape, colors, stability, etc.). In addition, programming the prototyping skill for the robot requires the users to have expert programming skills, which makes the task beyond the reach of the general public. To address the challenges, this paper presents a simulation-aided learning from demonstration (SaLfD) framework for easily deploying LEGO prototyping capability to robots. In particular, the user demonstrates constructing the customized novel LEGO object. The robot extracts the task information by observing the human operation and generates the construction plan. A simulation is developed to verify the correctness of the learned construction plan and the resulting LEGO prototype. The proposed system is deployed to a FANUC LR-mate 200id/7L robot. Experiments demonstrate that the proposed SaLfD framework can effectively correct and learn the prototyping (i.e. assembly and disassembly) tasks from human demonstrations. And the learned prototyping tasks are realized by the FANUC robot.

This paper introduces a wall construction planner for Unmanned Aerial Vehicles (UAVs), which uses a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic to generate near-time-optimal building plans for even large walls within seconds. This approach addresses one of the most time-consuming and labor-intensive tasks, while also minimizing workers' safety risks. To achieve this, the wall-building problem is modeled as a variant of the Team Orienteering Problem and is formulated as Mixed-Integer Linear Programming (MILP), with added precedence and concurrence constraints that ensure bricks are built in the correct order and without collision between cooperating agents. The GRASP planner is validated in a realistic simulation and demonstrated to find solutions with similar quality as the optimal MILP, but much faster. Moreover, it outperforms all other state-of-the-art planning approaches in the majority of test cases. This paper presents a significant advancement in the field of automated wall construction, demonstrating the potential of UAVs and optimization algorithms in improving the efficiency and safety of construction projects.

Data preprocessing is the first (and arguably most important) step toward building a working machine learning model. If your data hasn't been cleaned and preprocessed, your model does not work. Data preprocessing is generally thought of as the boring part. But it's the difference between being prepared and being completely unprepared. You might not like the preparation part, but tightening down the details in advance can save you from one nightmare of a trip.

Pune, India -- (SBWIRE) -- 06/19/2018 -- The Artificial Intelligence is used in the creation of construction plans. Autonomous equipment is considered as AI as it is aware of its surroundings and is capable of navigation without human input. In the planning stages, AI machinery can survey a proposed construction site and gather enough information to create 3D maps, blueprints and construction plans. Before this advancement, these processes would take weeks – now they can be done in one day. This helps to save firms both time and money in the form of labor.

Artificial intelligence, also referred to as AI, has come a long way in terms of becoming an efficient and effective tool for a variety of workplaces. Armed with complex algorithms and features that allow machines to display their own type of understanding and problem solving, artificial intelligence has also been incorporated into the construction process. AI has applications for data-collecting and planning, and can help with the creation of construction plans. It can aid with the management of a project, offering solutions based on a historical database of knowledge. It can even be utilised to help with physical tasks.