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3D reconstruction technology is used in a wide variety of applications. Currently, automatically creating accurate pointclouds for large parts requires expensive hardware. We are interested in using low-cost depth cameras mounted on commonly available industrial robots to create accurate pointclouds for large parts automatically. Manufacturing applications require fast cycle times. Therefore, we are interested in speeding up the 3D reconstruction process. We present algorithmic advances in 3D reconstruction that achieve a sub-millimeter accuracy using a low-cost depth camera. Our system can be used to determine a pointcloud model of large and complex parts. Advances in camera calibration, cycle time reduction for pointcloud capturing, and uncertainty estimation are made in this work. We continuously capture point-clouds at an optimal camera location with respect to part distance during robot motion execution. The redundancy in pointclouds achieved by the moving camera significantly reduces errors in measurements without increasing cycle time. Our system produces sub-millimeter accuracy.

Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques are useful in solving a specific class of problems. However, these techniques do not handle noise, variations in lighting conditions, and backgrounds with complex textures. In recent times, deep learning has been widely explored for use in automation of defect detection. This survey article presents three different ways of classifying various efforts in literature for surface defect detection using deep learning techniques. These three ways are based on defect detection context, learning techniques, and defect localization and classification method respectively. This article also identifies future research directions based on the trends in the deep learning area.

Tool-Path planning is the foundation for automating many manufacturing processes. Robotic manipulators are increasingly being considered to automate tasks that require complex tool motions. Robotic manipulators provide extra degrees of freedom and are more flexible than traditional automation technologies. However, a tool-path needs to be planned and given to the manipulator trajectory generator as input. The traditional tool-path planning considers the tool to have one contact point, known as the Tool Center Point (TCP). This underutilizes the available flexibility of the manipulator. To make use of the manipulator’s flexibility, multiple contact points or multiple TCPs can be considered. These tool contact considerations make the tool path planning problem complex and computationally challenging. In this paper, we present a novel tool path planning algorithm. Our algorithm incorporates the multiple tool contact points consideration during tool-path planning in an efficient manner to generate a high-quality tool-path in a reasonable amount of run time. We evaluated our algorithm on four different test cases. The test cases include the parts with complex geometry and tools which can have surface area for establishing contacts with the part. In these test cases, a traditional single contact tool-path will not work due to geometric constraints, and multiple tool contacts make the problem challenging. Our algorithm was able to generate tool-paths for these four test cases successfully.

High-performance composites are widely used in industry because of specific mechanical properties and lightweighting opportunities. Current automation solutions to manufacturing components from prepreg (pre-impregnated precursor material) sheets are limited. Our previous work has demonstrated the technical feasibility of a robotic cell to automate the sheet layup process. Many decisions are required for the cell to function correctly, and the time necessary to make these decisions must be reduced to utilize the cell effectively. Robot placement with respect to the mold is a significant and complex decision problem. Ensuring that robots can collaborate effectively requires addressing multiple constraints related to the robot workspace, singularity, and velocities. Solving this problem requires developing computationally efficient algorithms to find feasible robot placements in the cell. We describe an approach based on successive solution refinement strategy to identify a cell design that satisfies all constraints related to robot placement.

Using fixtures for assembly operations is a common practice in manufacturing processes with high production volume. For automated assembly cells using robotic arms, trajectories are programmed manually and robots follow the same path repeatedly. It is not economically feasible to build fixed fixtures for small volume productions as they require high accuracy and are part specific. Moreover, hand coding robot trajectories is a time consuming task. The uncertainties in part localization and inaccuracy in robot motions make it challenging to automate the task of assembling two parts with tight tolerances. Researchers in past have developed methods for automating the assembly task using contact-based search schemes and impedance control-based trajectory execution. Both of these approaches may lead to undesired collision with critical features on the parts. Our method guarantees safety for parts with delicate features during the assembly process. Our approach enables us to select optimum impedance control parameters and utilizes a learning-based search strategy to complete assembly tasks under uncertainties in bounded time. Our approach was tested on an assembly of two rectangular workpieces using KUKA IIWA 7 manipulator. The method we propose was able to successfully select the optimal control parameters. The learning-based search strategy successfully estimated the uncertainty in pose of parts and converged in few iterations.

Surface finishing is an important manufacturing process. Many parts with complex geometries require finishing of internal regions before they can be used. In small and medium volume productions most of the finishing tasks are non-repetitive in nature, and have to be performed manually. These finishing operations for parts with complex geometries can be quite labor intensive, and may pose risk to humans. We have developed a collaborative finishing system where human operators work on high level decision making, and the robot assistants carry out the labor intensive low level finishing tasks. The human operator guides the robotic system by transferring operator knowledge through a user interface. Our system generates instructions for the robots based on the user inputs and task requirements. We have also developed a planning algorithm that automatically computes the paths for the robots by using the CAD model of the part. This significantly reduces the robot programming time and improves the efficiency of the finishing system. If needed, the system seeks help from the human operator by generating notifications.

The performance of four automatically-generated path planning behaviors is evaluated through field-testing. Experiments were conducted using a model-referenced trajectory planner, which was implemented on unmanned surface vehicles (USVs) of different size, thrust, and maneuverability characteristics. The planner combines a local search based on the Velocity Obstacles (VO) concept with a global, lattice-based search for a dynamically feasible trajectory (determined using models of the nonlinear dynamics, nonholonomic constraints, and low-level control of each system). Multiple low-level USV heading and speed controllers, varying in complexity from proportional control to nonlinear backstepping control, were tested with the planner. High planning performance is achieved by searching within a resolution-adaptive space of the USV׳s candidate motion goals. Sampling is constrained to ensure that the International Regulations for the Prevention of Collisions at Sea (COLREGs) are followed. Prior to on-water implementation, the system was fine-tuned in simulation. Four automatically-generated USV behaviors were demonstrated in on-water tests within a complex, static obstacle field: 1) Approach (USV approaches a fixed target); 2) Follow (USV approaches a moving target); 3) Single COLREGs-Compliant Crossing (USV approaches a fixed target while avoiding another vessel); and 4) Multiple COLREGs-Compliant Crossings (USV approaches a fixed target while avoiding an interference vessel multiple times).

The capability of following a moving target in an environment with obstacles is required as a basic and necessary function for realizing an autonomous unmanned surface vehicle (USV). Many target following scenarios involve a follower and target vehicles that may have different maneuvering capabilities. Moreover, the follower vehicle may not have prior information about the intended motion of the target boat. This paper presents a trajectory planning and tracking approach for following a differentially constrained target vehicle operating in an obstacle field. The developed approach includes a novel algorithm for computing a desired pose and surge speed in the vicinity of the target boat, jointly defined as a motion goal, and tightly integrates it with trajectory planning and tracking components of the entire system. The trajectory planner generates a dynamically feasible, collision-free trajectory to allow the USV to safely reach the computed motion goal. Trajectory planning needs to be sufficiently fast and yet produce dynamically feasible and short trajectories due to the moving target. This required speeding up the planning by searching for trajectories through a hybrid, pose-position state space using a multi-resolution control action set. The search in the velocity space is decoupled from the search for a trajectory in the pose space. Therefore, the underlying trajectory tracking controller computes desired surge speed for each segment of the trajectory and ensures that the USV maintains it. We have carried out simulation as well as experimental studies to demonstrate the effectiveness of the developed approach.

We present a model-predictive trajectory planning algorithm for following a target boat by an autonomous unmanned surface vehicle (USV) in an environment with static obstacle regions and civilian boats. The planner developed in this work is capable of making a balanced trade-off among the following, possibly conflicting criteria: the risk of losing the target boat, trajectory length, risk of collision with obstacles, violation of the Coast Guard Collision Regulations (COLREGs), also known as “rules of the road”, and execution of avoidance maneuvers against vessels that do not follow the rules. The planner addresses these criteria by combining a search for a dynamically feasible trajectory to a suitable pose behind the target boat in 4D state space, forming a time-extended lattice, and reactive planning that tracks this trajectory using control actions that respect the USV dynamics and are compliant with COLREGs. The reactive part of the planner represents a generalization of the velocity obstacles paradigm by computing obstacles in the control space using a system-identified, dynamic model of the USV as well as worst-case and probabilistic predictive motion models of other vessels. We present simulation and experimental results using an autonomous unmanned surface vehicle platform and a human-driven vessel to demonstrate that the planner is capable of fulfilling the above mentioned criteria.

This article presents an experimental assessment of an Unmanned Surface Vehicle (USV) executing an approach behavior to several stationary targets in an obstacle field. A lattice-based trajectory planner is implemented with a priori knowledge of the vehicle characteristics. In parallel, a low-level controller is developed for the vehicle using a proportional control law. These systems are integrated on the USV control system using the Lightweight Communications and Marshalling (LCM) message passing system. Filtered vehicle-state information from onboard sensors is passed to the planner, which returns a least-cost, dynamically feasible trajectory for achieving the ascertained goal. The system was tested in a 750 m by 150 m area of the US Intracoastal Waterway in South Florida in the presence of wind and wave disturbances to characterize its effectiveness in a real-world scenario. The vehicle was able to replicate behavior predicted in simulations when navigating around obstacles. The approach distance to each target was favorably lower than the user-defined limit. Owing to the fact that the USV uses differential thrust for steering, the vehicle tracked the planned trajectories better at lower speeds.Copyright © 2013 by ASME