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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.

Multi-arm mobile manipulators can be represented as a combination of multiple robotic agents from the perspective of task-assignment and motion planning. Depending upon the task, agents might collaborate or work independently. Integrating motion planning with task-agent assignment is a computationally slow process as infeasible assignments can only be detected through expensive motion planning queries. We present three speed-up techniques for addressing this problem-(1) spatial constraint checking using conservative surrogates for motion planners, (2) instantiating symbolic conditions for pruning infeasible assignments, and (3) efficiently caching and reusing previously generated motion plans. We show that the developed method is useful for real-world operations that require complex interaction and coordination among high-DOF robotic agents.

We present a context-dependent bi-directional tree-search framework for point-to-point path planning for manipulators. Conceptually, our framework is composed of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting synergistic interaction between these strategies and selecting appropriate strategies based the contextual cues from the search state, we show an instance of our framework that computes high-quality solutions in a variety of complex scenarios with a low failure rate. We also show that some popular path planning methods in the literature can be easily represented in our framework. We compare our approach with these popular methods in a diverse set of test scenarios. We report a 15-fold reduction in failure rate coupled with at least a 26% drop in solution suboptimality when compared to the best of the alternative methods.

We present a two-layered architecture for task-agent assignment and motion planning of Bi-manual Mobile manipulators for executing complex tasks. We use search trees in temporal windows to determine feasible task assignments of agents using task and spatial constraint-based heuristics. We also introduce a caching scheme for moving between different trees so as to avoid re-planning of those portions of the robot motion that were successful. This greatly reduces the number of calls to the motion planner as compared to direct motion planning after task-agent assignment. We have shown our approach works on a set of complex tasks with significantly lower computation times.

Successfully completing a complex manufacturing task requires finding a feasible placement of the workpiece in the robot workspace. The workpiece placement should be such that the task surfaces on the workpiece are reachable by the robot, the robot can apply the required forces, and the end-effector/tool can move with the desired velocity. This paper formulates the problem of identifying a feasible placement as a non-linear optimization problem over the constraint violation functions. This is a computationally challenging problem. We show that this problem can be solved by successively searching for the solution by incrementally applying different constraints. We demonstrate the feasibility of our approach using several complex workpieces.

This paper presents a new trajectory planning algorithm based on context-dependent search strategy switching for manipulators working in confined workspaces. This problem has an inherently complex and large search space. Finding good solutions to this problem requires developing an efficient search strategy using appropriately-designed motion primitives and heuristic functions to guide the search. The algorithm presented in this paper monitors the search progress and uses different search strategies in different parts of the search space. This allows it to solve manipulator trajectory planning problems for highly-confined workspaces. It is also able to seek input from the human user when search heuristics are unable to provide guidance to the search process. We present results on challenging example parts and compare them with the previously reported approaches.

Warehouses and factories are beginning to deploy mobile manipulators for transporting parts between machines and work stations. Minimizing the number of these robots on the shop floor requires that each robot should complete the assigned task in a time-optimal manner and therefore maximize the robot's capacity. In this paper we present a search-based algorithm for generating time-optimal trajectories for picking and transporting parts using a mobile manipulator. The approach is based on a hierarchical search that uses discrete search for planning of the mobile base. The time cost due to the motion of the manipulator for picking up the part is evaluated and integrated with the search state space at the appropriate times. The trajectories generated result in picking up of the part with the manipulator while the mobile base is in motion. The performance of the algorithm is studied for different factory sizes and obstacle densities. This approach enables the mobile manipulator to perform pick and transport operation in a smaller time duration compared to the operation where it stops to pick up the object.

This paper introduces an approach for decomposing exploration tasks among multiple unmanned surface vehicles (USVs) in congested regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environmental constraints on the USVs. The performance of a USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths due to tides and weather. The team of USVs needs to explore a certain region of the harbor and we need to develop an algorithm to decompose the region of interest into multiple subregions. The algorithm overlays a two-dimensional grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, the rate at which each USV is able to travel varies with the applicable speed limits at the location. The objective is to minimize the time taken for the last USV to finish exploring the assigned area. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments where speed restrictions are not known in advance.