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Mobile manipulators are being deployed for transporting parts between machines and work stations in warehouses and shop floors. To increase the efficiency of operations, these mobile manipulators are required to complete the tasks as fast as possible. Picking up parts with the manipulator while the mobile base is moving decreases the time required to complete the transportation task and increases the efficiency of operations. However, motions of the manipulator on a moving platform can be risky, and hence, it is desired that the manipulator starts and ends its motions as close as possible to the part being picked up. In this article, we present a bidirectional sampling-based scheme for generating such manipulator trajectories for a given mobile base trajectory for pickup and transportation. Our approach implicitly determines the location of the mobile base where the manipulator motion starts and ends as well as where grasping happens. It also determines which grasping pose to use for picking up the part. Furthermore, we have presented the techniques to reduce the manipulator motion time (span time) and the computation time. Our approach enables us to reduce span time on average by 35% with a 16x reduction in the computation time compared to the RRT-based baseline methods.

Many applications require structures composed of layers of heterogeneous materials and prefabricated components embedded between the layers. The existing additive manufacturing process based on layered object manufacturing is not able to handle multiple layer materials and cannot embed prefabricated components. Moreover, the existing process imposes restrictions on the material options. This significantly limits the type of heterogeneous structures that can be manufactured using traditional additive manufacturing. This paper presents an extension of sheet lamination object manufacturing process by using a robotic cell to perform the sheet manipulation and handling. It makes the following three advances: (1) enabling the use of multi-material layers and inclusion of prefabricated components between the layers, (2) developing an algorithmic foundation to facilitate automated generation of robot instructions, and (3) identifying the relevant process constraints related to speed, accuracy, and strength. We demonstrate the system capabilities by using three case studies.

In many manufacturing applications, robotic manipulators need to operate in cluttered environments. Quickly finding high-quality paths is very important in applications that require high part fix and frequent setup changes. This paper presents a point-to-point path planning framework for manipulators operating in cluttered environments. It uses a bi-directional tree-search to find path and facilitates finding a balance between path quality and planning time. The framework dynamically switches between search strategies based on the search progress to produce high-quality paths quickly. This paper three main contributions. First, we extend a previously developed sampling-based modular tree-search. Specifically, we present a strategy that can sample effectively in challenging regions of the search-space by using local approximations of the configuration space. Second, we add new strategies and scheduling logic that decreases the failure rate as well as the planning time compared to the prior work. We also present an inter-tree connection strategy that adapts to collision information gathered over time. We introduce a scheduling rule that regulates the exploitation of focusing hints derived from the workspace obstacles. Third, we present theoretical reasoning behind strategy switching and how it can help decrease planning times and increase path quality. Together, these new features the reduce average failure rate by a factor of 4 and improve the average planning time by 22% over the previous work.

Determining a feasible path for nonholonomic mobile manipulators operating in congested environments is challenging. Sampling-based methods, especially bi-directional tree search-based approaches, are amongst the most promising candidates for quickly finding feasible paths. However, sampling uniformly when using these methods may result in high computation time. This paper introduces two techniques to accelerate the motion planning of such robots. The first one is coordinated focusing of samples for the manipulator and the mobile base based on the information from robot surroundings. The second one is a heuristic for making connections between the two search trees, which is challenging owing to the nonholonomic constraints on the mobile base. Incorporating these two techniques into the bi-directional RRT framework results in about 5x faster and 10x more successful computation of paths as compared to the baseline method.

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.

Deburring and chamfering are two important manufacturing processes and are mostly performed manually by human operators, especially for parts with complex geometries. This paper presents a robotic deburring and chamfering system that can be used in high-mix/low-volume manufacturing applications. The developed system includes a robotic manipulator, force/torque sensor, pneumatic tools and tool changer, and pneumatic control components. Two Human Machine Interfaces (HMIs) have been designed for selecting target features on the CAD model and planning collision-free trajectories for deburring and chamfering. The first HMI is designed to work with the CAD models (CAD-HMI) for feature selection. Human operators can select any features on the CAD model and export selected features as tool paths for trajectory generation. Once the human operator mounts the gear into the work cell, the second HMI (main HMI) can be used to register the gear and plan collision-free trajectories for the desired operation. The control and operation of the robot and tools are also integrated into the main HMI. We demonstrated that the developed system is useful for complex industrial parts.

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.

In this letter, an approach is presented to estimate the stiffness characteristics of elastically deformable objects, while being processed in a robotic cleaning system. Building on our previous work [J. D. Langsfeld, A. M. Kabir, K. N. Kaipa, and S. K. Gupta, “Robotic bimanual cleaning of deformable objects with online learning of part and tool models,” in Proc. IEEE Int. Conf. Autom. Sci. Eng. , 2016, pp. 626–632.], significant extensions are made in this letter by presenting new methods that lead to an overall improvement in the results. The robot system plans the actions of both the grasping and cleaning arms using its current model of the part deformation behavior, and attempts to minimize the overall cleaning time by finding plans that use a small number of grasping positions on the part. A finite-element method approach is used to model the part deformation and a general update algorithm is presented that can modify the stiffness parameters of the model to match observed deformation data collected during cleaning. This allows the system to very quickly discover the correct locations to grasp the part so as to minimize deformation. The methods described in this lettter are shown to achieve better results than the ones from our previous work: The results are much smoother, along with a twofold reduction in parameter-estimation error, and notable faster convergence.

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.

Low production volume applications cannot use custom fixtures to reduce uncertainties in part locations. Traditional tool-path planners for area coverage applications may not perform satisfactorily when there is significant uncertainty in part locations because these planners do not account for the tool-part contact and impedance controller parameters during path execution. This may lead to tool collision with the part, incorrect force at the Tool Center Point (TCP), and loss of contact while performing area coverage operations. Moreover, the poorly designed tool-path and incorrectly estimated impedance control parameters also lead to poor task performance. To avoid such failures, we concurrently design a collision-free tool-paths, generate executable robot trajectory, and select the impedance compensation parameters so that it can take care of part uncertainties and provide the desired tool-part contact. We apply the designed algorithm for area coverage manufacturing applications. Finally, we evaluate the algorithm performance by conducting physical experiments.

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.

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.

To minimize the operation time, mobile manipulators need to pick-up parts while the mobile base and the gripper are moving. The gripper speed needs to be selected to ensure that the pick-up operation does not fail due to uncertainties in part pose estimation. This, in turn, affects the mobile base trajectory. This paper presents an active learning based approach to construct a meta-model to estimate the probability of successful part pick-up for a given level of uncertainty in the part pose estimate. Using this model, we present an optimization-based framework to generate time-optimal trajectories that satisfy the given level of success probability threshold for picking-up the part.

Extrusion-based additive manufacturing (AM), also known as fused deposition modeling (FDM) extrudes filaments through a heated nozzle and builds a part layer-by-layer. Using a smaller diameter nozzle can achieve better surface finish. However, there is a trade-off between surface finish and build times as using a small diameter nozzle leads to smaller layer thickness and long build times. Traditional FDM printers create a part with planar layers, and this restricts control over fiber orientations. This paper presents a robotic cell for multi-resolution AM. The cell consists of two 6 degrees of freedom (DOF) robot manipulators capable of printing non-planar and/or planar layers. We describe algorithms for decomposing parts into multi-resolution layers and generating collision-free trajectories for the robot manipulators. We validate our approach by printing five parts with multi-resolution.

We pose the problem of path-constrained trajectory generation for the synchronous motion of multi-robot systems as a non-linear optimization problem. Our method determines appropriate parametric representation for the configuration variables, generates an approximate solution as a starting point for the optimization method, and uses successive refinement techniques to solve the problem in a computationally efficient manner. We have demonstrated the effectiveness of the proposed method on challenging simulation and physical experiments with high degrees of freedom robotic systems.

Hand layup is a commonly used process for making composite structures from several plies of carbon-fiber prepreg. The process involves multiple human operators manipulating and conforming layers of prepreg to a mold. The manual layup process is ergonomically challenging, tedious, and limits throughput. Moreover, different operators may perform the process differently, and hence introduce inconsistency. We have developed a multi-robot cell to automate the layup process. A human expert provides a sequence to conform to the ply and types of end-effectors to be used as input to the system. The system automatically generates trajectories for the robots that can achieve the specified layup. Using the cell requires the automated generation of different types of plans. This paper addresses two main planning problems: (a) generating plans to grasp and manipulate the ply and (b) generating feasible robot trajectories. We use a hybrid-physics based simulator coupled with a state space search to find grasp plans. The system employs a strategy that applies constraints successively in a non-linear optimization formulation to identify suitable placements of the robots around the mold so that feasible trajectories can be generated. Our system can generate plans in a computationally efficient manner, and it can handle a wide variety of complex parts. We demonstrate the automated layup by conducting physical experiments on an industry-inspired mold using the generated plans.

The developed y4 copter is an Unmanned Aerial Vehicle with autonomous subsystems which reports the fully automated y4 copter for the first time. Automation is implemented through maintaining the desired roll, pitch and yaw therefore the flight stability as well as in case of controlling elevation. An improved and efficient algorithm that uses Quaternion is implemented to determine Euler angles avoiding Gimbal lock in case of developing an inertial measurement unit. It has a self-stabilization system powered by Proportional Integral Derivative control system which is computationally affordable by an ordinary 8 bit microcontroller capable of performing only integer operations. A remote is designed to communicate with the y4 copter from the base station using XBee transceiver modules which come with secured communication with long range. The developed system demonstrates simple design offering lower cost and secured means of control.

We propose a hybrid human-robot cell for reducing labor in multi-layer prepreg composite sheet layup process. The human expert performs initial experiments to assess the process sequence, and the direction of the layup. The expert knowledge is then transferred to the system in the form of high level commands which are converted into low-level collision free trajectories for the robots in the cell. This paper demonstrates the feasibility of the composite sheet layup using the robotic manipulators along with the design of end-effector tools that are used during the layup process. We give a comparison between manual and hybrid cell for execution times in different steps involved. Finally, we give an overview of process consistency benefits of the hybrid cell.

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.

Use of robots is rising in process applications where robots need to interact with parts using tools. Representative examples can be cleaning, polishing, grinding, etc. These tasks can be non-repetitive in nature and the physics-based models of the task performances are unknown for new materials and tools. In order to reduce operation cost and time, the robot needs to identify and optimize the trajectory parameters. The trajectory parameters that influence the performance can be speed, force, torque, stiffness, etc. Building physics-based models may not be feasible for every new task, material, and tool profile as it will require conducting a large number of experiments. We have developed a method that identifies the right set of parameters to optimize the task objective and meet performance constraints. The algorithm makes decisions based on uncertainty in the surrogate model of the task performance. It intelligently samples the parameter space and selects a point for experimentation from the sampled set by determining its probability to be optimum among the set. The iterative process leads to rapid convergence to the optimal point with a small number of experiments. We benchmarked our method against other optimization methods on synthetic problems. The method has been validated by conducting physical experiments on a robotic cleaning problem. The algorithm is general enough to be applied to any optimization problem involving black box constraints.