Designing cable harnesses can be time-consuming and complex due to many design and manufacturing aspects and rules. Automating the design process can help to fulfil these rules, speed up the process, and optimize the design. To accommodate this, we formulate a harness routing optimization problem to minimize cable lengths, maximize bundling by rewarding shared paths, and optimize the cables’ spatial location with respect to case-specific information of the routing environment, e.g., zones to avoid. A deterministic and computationally effective cable harness routing algorithm has been developed to solve the routing problem and is used to generate a set of cable harness topology candidates and approximate the Pareto front. Our approach was tested against a stochastic and an exact solver and our routing algorithm generated objective function values better than the stochastic approach and close to the exact solver. Our algorithm was able to find solutions, some of them being proven to be near-optimal, for three industrial-sized 3D cases within reasonable time (in magnitude of seconds to minutes) and the computation times were comparable to those of the stochastic approach.

Cycle time minimization in multi-robot manufacturing stations is computationally challenging. This is due to the many aspects that need to be accounted for, including assigning process tasks to robots, specifying robot configurations at tasks, sequencing, planning motions, and coordinating the robots to avoid collisions. Hence, to find good solutions, often some assumptions are made and/or the problem is divided into subproblems—often limiting the set of solutions with the risk of excluding the best ones. In this study, we generalize the completely disjoint solution method that challenges the so-calledshortest pathassumption, i.e., to let each robot use its shortest collision-free motion between any two configurations, regardless of the other robots. We devise a generalized method calledspatial–temporal load balancing and coordination, which prevents robot–robot collisions by a sequence of disjoint solutions, guiding task assignments, sequences, and robot motions (path and velocity). We study both artificial and industrial instances. For some of them, our suggested method is superior to methods based on the shortest path assumption, with as much as a 28% reduction in cycle time. Moreover, for problem instances with no special structure, we establish that the shortest path assumption is often reasonable.Note to Practitioners—This work is motivated by a particular industrial problem instance of a spot-welding station with two robots and where welds are placed along the edge of a workpiece. Due to the special geometry of the instance one robot can only perform welds in the middle of the edge and the other only at the ends. As a result, if the robots use their shortest motions between welds, then waiting times are required to prevent collisions. Moreover, the tasks are too close to each other to allow for a completely disjoint solution. Hence, we suggest a method based on sequence of disjoint solutions.

In the manufacturing industry, spray painting is often an important part of the manufacturing process. Especially in the automotive industry, the perceived quality of the final product is closely linked to the exactness and smoothness of the painting process. For complex products or low batch size production, manual spray painting is often used. But in large scale production with a high degree of automation, the painting is usually performed by industrial robots. There is a need to improve and simplify the generation of robot trajectories used in industrial paint booths. A novel method for spray paint optimization is presented, which can be used to smooth out a generated initial trajectory and minimize paint thickness deviations from a target thickness. The smoothed out trajectory is found by solving, using an interior point solver, a continuous non-linear optimization problem. A two-dimensional reference function of the applied paint thickness is selected by fitting a spline function to experimental data. This applicator footprint profile is then projected to the geometry and used as a paint deposition model. After generating an initial trajectory, the position and duration of each trajectory segment are used as optimization variables. The primary goal of the optimization is to obtain a paint applicator trajectory, which would closely match a target paint thickness when executed. The algorithm has been shown to produce satisfactory results on both a simple 2-dimensional test example, and a non-trivial industrial case of painting a tractor fender. The resulting trajectory is also proven feasible to be executed by an industrial robot.Note to Practitioners—The work is motivated by the need to generate well performing robot trajectories in robotized spray-painting booths. The described method applies to cases where robotic spray painting is to be used for painting a surface with an even layer of paint at a specified thickness. The method generates and optimizes robot trajectories and is shown to be able to generate a satisfactory paint cover for simple test cases as well as more realistic industrial cases. For a user it could be implemented as is, or be obtained as a standalone service, but there are some prerequisites that need to be fulfilled to make use of the optimization method. It is assumed that the surface to be painted is available as a CAD-model and that physical testing has been performed to determine the characteristics of the paint and nozzle. These physical tests amount to spraying paint on a flat piece of material at a few different distances from the surface and measuring the cross section of the paint thickness. The resulting trajectory can be executed on any industrial painting robot that can handle linear motion commands.

We consider the problem of creating tighter-fitting bounding volumes (more specifically rectangular swept spheres) when constructing bounding volume hierarchies (BVHs) for complex 3D geometries given in the form of unstructured triangle meshes/soups with the aim of speeding up our IPS Path Planner for rigid bodies, where the triangles often have very different sizes. Currently, the underlying collision and distance computation module (IPS CDC) does not take into account the sizes of the triangles when it constructs BVHs using a top-down strategy. To split triangles in a BVH node into two BVH nodes, IPS CDC has to compute both the split axis and the split position. In this work, we use the principal axes of the tensor of inertia as the potential split axes and the center of mass as the split position, where the computations of both the tensor of inertia and the center of mass require knowledge of the areas of the triangles. We show that our method improves performance (up to 20 % faster) of our IPS Path Planner when it is used to plan collision-free disassembly paths for three different test cases taken from manufacturing industries.

Path planning of an object which is allowed to be in contact with other objects during assembly process is a significant challenge due to the variety of permitted or forbidden collisions between the distinct parts of the objects to be assembled. In order to put objects together in real-life scenarios, parts of assembled objects may be required to flex, whereas other parts may have to fit exactly. Consequently, existing collision checking and distance computation algorithms have to be modified to enable path planning of objects that can be in contact during the assembly process. In this paper, we analyze an improved broad phase proximity query algorithm to enable such contact-based assembly tasks we call CHAT (Contact-based Hierarchy for Assembly Tasks). We demonstrate that, compared to existing approaches, our proposed method is more than an order of magnitude faster for collision queries and up to three times faster for distance queries when the two objects contain a large number of parts (with some parts containing thousands or tens of thousands of triangles). Due to the nature of the algorithm, we expect the performance improvements to increase as the number of parts in an object becomes larger.

Assembly cells, where multiple robots perform sets of tasks, often face the challenge to minimize cycle time and avoid collisions. Collisions are avoided by introducing synchronization schemes among the robots, preventing shared volumes of the workspaces to be simultaneously entered. Synchronization often increases the cycle time and makes the robot programming more difficult to generate, adjust, and maintain. In this paper, we present a novel method to maximize throughput while eliminating all synchronizations among robots. We devise algorithms to minimize cycle time generating no intersection among robots at any time during their paths. First, a surrogate model for minimizing cycle time is provided and solved to optimality: each task is assigned to a robot in a way that no collision occurs. Afterward, the entire workspace is partitioned such that each robot's workspace is separated from the others. Finally, robot paths are generated automatically in order to avoid collisions with the environment and remaining in their precomputed partitions. In the rare cases where some of these steps fail, a feedback procedure redistributing the tasks or modifying the partitions is adopted. Furthermore, since the surrogate model approximates cycle time, several solutions are generated and evaluated based on better approximations of the model. The results are convincing: computational experience on different cases from the automotive industry shows that it is possible to generate programs where the robots never intersect with each other and achieve cycle times comparable to the ones generated allowing synchronization.

This work is inspired by the problem of planning sequences of operations, as welding, in car manufacturing stations where multiple industrial robots cooperate. The goal is to minimize the station cycle time, i.e., the time it takes for the last robot to finish its cycle. This is done by dispatching the tasks among the robots, and by routing and scheduling the robots in a collision-free way, such that they perform all predefined tasks. We propose an iterative and decoupled approach in order to cope with the high complexity of the problem. First, collisions among robots are neglected, leading to a min-max Multiple Generalized Traveling Salesman Problem (MGTSP). Then, when the sets of robot loads have been obtained and fixed, we sequence and schedule their tasks, with the aim to avoid conflicts. The first problem (min-max MGTSP) is solved by an exact branch and bound (B&B) method, where different lower bounds are presented by combining the solutions of a min-max set partitioning problem and of a Generalized Traveling Salesman Problem (GTSP). The second problem is approached by assuming that robots move synchronously: a novel transformation of this synchronous problem into a GTSP is presented. Eventually, in order to provide complete robot solutions, we include path planning functionalities, allowing the robots to avoid collisions with the static environment and among themselves. These steps are iterated until a satisfying solution is obtained. Experimental results are shown for both problems and for their combination. We even show the results of the iterative method, applied to an industrial test case adapted from a stud welding station in a car manufacturing line. This paper is motivated by the problem of planning robot operations in welding applications in the automotive industry. Here, a number of welding tasks have been introduced along the car body: the goal is to let the robots perform such tasks while minimizing the cycle time (or makespan). The main difficulties, from the manufacturing engineer perspective, lie in assigning the tasks to the robots, deciding the order and the timing of the operations, avoiding collisions between the robots and the environment, and among the robots themselves. We present in this work an iterative approach, consisting of two steps: first, sequences for the robot operations are computed in order to minimize the cycle time, while neglecting collisions among robots; then, given the assignment of tasks to robots, the operations are reordered and scheduled while avoiding conflicts among robots. Robot motions are also automatically computed to avoid collisions with the static environment. We show an optimal algorithm, for the first part, based on implicit enumeration (B&B) and introduce a novel suboptimal algorithm, for the second part, to synchronize the robots. These algorithms are iterated while fetching information about the problem that are hard to compute, thus following a lazy approach. Tests on problems adapted from the literature and from the automotive industry, show clear improvements over more sequential approaches and good running times. The algorithms are especially suited for cases with up to 40 tasks and 4 robots, as for typical geometry stations. In future works, we will further investigate efficient heuristic optimization approaches in order to handle larger problems, consisting of more than 100 tasks, as for typical assembly stations.