In this paper we present a framework to learn skills from human demonstrations in the form of geometric nullspaces, which can be executed using a robot. We collect data of human demonstrations, fit geometric nullspaces to them, and also infer their corresponding geometric constraint models. These geometric constraints provide a powerful mathematical model as well as an intuitive representation of the skill in terms of the involved objects. To execute the skill using a robot, we combine this geometric skill description with the robot's kinematics and other environmental constraints, from which poses can be sampled for the robot's execution. The result of our framework is a system that takes the human demonstrations as input, learns the underlying skill model, and executes the learnt skill with different robots in different dynamic environments. We evaluate our approach on a simulated industrial robot, and execute the final task on the iCub humanoid robot.

Many industry manufacturing processes require a lot of manpower to accomplish tasks manually, for example, manual polishing and masking. Industrial robot can be used to replace most of the tedious and repeated tasks. However, using robot program to generate the tool path for the manufacturing process might need programming skills and expertise. Besides, Computer Aided Design (CAD) files might not be available or accurate for the engineer to design the robot tool path. Hence, we propose an automatic way to generate the adaptive robot tool path for manufacturing process by using scan point cloud data of the target coupon. The core algorithm is based on point cloud projection on plane, tool path pattern design and reverse transform matrix to project the 2d tool path back to 3d point cloud. The algorithm is based on Point Cloud Library (PCL) and OpenCV libraries. After the toolpath is generated in the point cloud, Robot Operating System (ROS) is used to plan trajectory and check for collision. The automated tool path generation algorithm can be applied to multiple manufacturing process, such as masking, polishing and painting.

In this paper, we show that visual servoing tasks can be integrated into a prioritized constraint-based torque control framework. This framework can achieve real time control of robots performing strict prioritized motion and forces tasks. A null-space projector is used to combine the tasks with different priorities. The visual servoing is considered just another task formulated with constraints in the whole body control framework. Several relevant constraints (i.e., motion constraints, joint limits) are tested to evaluate the control framework. Further, we evaluate the proposed approach in typical industrial robotics applications: grasping of cylindrical objects, and two position/force control applications (Erasing and Peg-in-Hole).

One of the primary challenges for a successful Highly Assisted and/or Autonomous Vehicle is its localization. To improve the precision of location of the vehicle, not only the internal sensors are being used, but also using data from external sensors is attracting increasing attention from the research community. One such proposed sensor is an infrastructure RADAR which can be used to improve the localization of the ego-vehicle. Although a RADAR indeed is a supplementary source of information, it suffers a unique type of clutter which have trajectories like real objects and can therefore result in “ghost measurements”, i.e., measurements which do not correspond to any real vehicles. This deteriorates the quality of the fused state estimates. This paper proposes a robust method to fuse the RADAR readings in presence of such outliers. This methodology builds upon a previously proposed solution where the problem was formulated as a factor graph. The RADAR measurements were added as a novel constraint of sum of inter-vehicle distance, called Topology Factor. Our previous work assumed clutter free environment. This paper proposes a novel robust Topology Factor which is also resilient against above mentioned outliers. Simulations (based on real data) show promising results in the direction of lowering the degradation of fused state estimates in presence of such clutter.

This paper presents an approach to generate the time-optimal trajectory for a robot manipulator under certain kinematic constraints such as joint position, velocity, acceleration, and jerk limits. This problem of generating a trajectory that takes the minimum time to pass through specified waypoints is formulated as a nonlinear constraint optimization problem. Unlike prior approaches that model the motion of consecutive waypoints as a Cubic Spline, we model this motion with a seven-segment acceleration profile, as this trajectory results in a shorter overall motion time while staying within the bounds of the robot manipulator's constraints. The optimization bottleneck lies in the complexity that increases exponentially with the number of waypoints. To make the optimization scale well with the number of waypoints, we propose an approach that has linear complexity. This approach first divides all waypoints to consecutive batches, each with an overlap of two waypoints. The overlapping waypoints then act as a bridge to concatenate the optimization results of two consecutive batches. The whole trajectory is effectively optimized by successively optimizing every batch. We conduct experiments on practical scenarios and trajectories generated by motion planners to evaluate the effectiveness of our proposed approach over existing state-of-the-art approaches.

In this paper, we introduce a novel robot programming paradigm. It focuses on reducing the required expertise in robotics to a level that allows shop floor workers to use robots in their application domain without the need of extensive training. Our approach is user-centric and can interpret underspecified robot tasks, enabling communication on an abstract level. Such high-level task descriptions make the system amenable for users that are experts in a particular domain, but have limited knowledge about robotics and are thus not able to specify low-level details and instructions. Semantic models for all involved entities, i.e., processes, workpieces, and workcells, enable automatic reasoning about underspecified tasks and missing pieces of information. We showcase and evaluate this methodology on two industrial use cases from the domains of assembly and woodworking, comparing it to state-of-the-art solutions provided by robot manufacturers.

In this paper, we propose a framework for prioritized constraint-based specification of robot tasks. This framework is integrated with a cognitive robotic system based on semantic models of processes, objects, and workcells. The target is to enable intuitive (re-)programming of robot tasks, in a way that is suitable for non-expert users typically found in SMEs. Using CAD semantics, robot tasks are specified as geometric inter-relational constraints. During execution, these are combined with constraints from the environment and the workcell, and solved in real-time. Our constraint model and solving approach supports a variety of constraint functions that can be non-linear and also include bounds in the form of inequalities, e.g., geometric inter-relations, distance, collision avoidance and posture constraints. It is a hierarchical approach where priority levels can be specified for the constraints, and the nullspace of higher priority constraints is exploited to optimize the lower priority constraints. The presented approach has been applied to several typical industrial robotic use-cases to highlight its advantages compared to other state-of-the-art approaches.

We present an approach to control a 6-degree-of-freedom (DOF) manipulator using an uncalibrated visual servoing (VS) approach that addresses the challenges of choosing proper image features for target objects and designing a VS controller to enhance the tracking performance. The main contribution of this paper is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6-D poses from Cartesian space to this virtual visual space. Each component of the 6-D pose vector defined in this virtual visual space is linearly independent, leading to a full-rank 6 × 6 image Jacobian matrix, which allows avoiding classical problems, such as image space singularities and local minima. Furthermore, the control for rotational and translational motion of robot is decoupled due to the diagonal image Jacobian. Finally, simulation results with an eye-to-hand robotic system confirm the improvement in controller stability and motion performance with respect to conventional VS approaches. Experimental results on a 6-DOF industrial robot are provided to illustrate the effectiveness of the proposed method and the feasibility of using this method in practical scenarios.

We present an approach for monitoring and interpreting human activities based on a novel multimodal vision-based interface, aiming at improving the efficiency of human-robot interaction (HRI) in industrial environments. Multi-modality is an important concept in this design, where we combine inputs from several state-of-the-art sensors to provide a variety of information, e.g. skeleton and fingertip poses. Based on typical industrial workflows, we derived multiple levels of human activity labels, including large-scale activities (e.g. assembly) and simpler sub-activities (e.g. hand gestures), creating a duration- and complexity-based hierarchy. We train supervised generative classifiers for each activity level and combine the output of this stage with a trained Hierarchical Hidden Markov Model (HHMM), which models not only the temporal aspects between the activities on the same level, but also the hierarchical relationships between the levels.

In this paper, we introduce an approach for leveraging CAD description to a semantic level, in order to link additional knowledge to CAD models and to exploit resulting synergy effects. This has been achieved by designing a description language, based on the Web Ontology Language (OWL), that is used to define boundary representations (BREP) of objects. This involves representing geometric entities in a semantic meaningful way, e.g., a circle is defined by a coordinate frame and a radius instead of a set of polygons. Furthermore, the scope of this semantic description language also covers geometric constraints between multiple objects. Constraints can be specified not only on the object level, but down to single edges or faces of an object. This semantic representation is used to improve a variety of applications, ranging from shape-based object recognition to constraint-based robot task descriptions. Results from a quantitative evaluation are presented to assess the practicability of this approach.

Intuitive programming of industrial robots is especially important for small and medium-sized enterprises. We evaluated four different input modalities (touch, gesture, speech, 3D tracking device) regarding their preference, usability, and intuitiveness for robot programming.

In this paper, we propose a framework for intuitive task-based programming of robots using geometric inter-relational constraints. The intended applications of this framework are robot programming interfaces that use semantically rich task descriptions, allow intuitive (re-)programming, and are suitable for non-expert users typically found in SMEs. A key concept in this work is the use of CAD semantics to represent geometric entities in the robotic workcell. The robot tasks are then represented as a set of geometrical inter-relational constraints, which are solved in real-time to be executed on the robot. Since these constraints often specify the target pose only partially, the robot can be controlled to move in the constraints' null space in order to handle external disturbances or further optimize the robot's pose during runtime. Geometrical inter-relational constraints are easy to understand and can be intuitively specified using CAD software. A number of applications common in industrial robotic scenarios have been chosen to highlight the advantages of the presented approach vis-à-vis the state-of-the-art approaches.

In this paper, we present an uncalibrated position-based fixed-camera Visual Servoing for robot manipulators, where the goal is to track the 3D position and orientation of the target. The stereo system with 2 USB cameras is uncalibrated with respect to the robot base frame and the transformation between them is estimated on-line while performing the task. Dynamic impedance control is designed to generate a dynamic trajectory for the robot manipulator considering the dynamic environment constraints, such as: robot singularities avoidance and (self-/obstacle-) collision avoidance. Experiments have been carried out to verify performance of the proposed system on a real industrial robot, where the calibration estimation process and handling of all uncertainties in the environment are demonstrated. Moreover the uncalibrated stereo camera system can be manually moved while performing the task in order to obtain a clearer view and the re-calibration is performed automatically and on-line.

This paper introduces 6 new image features to provide a solution to the open problem of uncalibrated 6D image-based visual servoing for robot manipulators, where the goal is to control the 3D position and orientation of the robot end-effector using visual feedback. One of the main contributions of this article is a novel stereo camera model which employs virtual orthogonal cameras to map 6D Cartesian poses defined in the Task space to 6D visual poses defined in a Virtual Visual space (Image space). This new model is used to compute a full-rank square Image Jacobian matrix (Jimg), which solves several common problems exhibited by the classical image Jacobians, e.g., Image space singularities and local minima. This Jacobian is a fundamental key for the image-based controller design, where a chattering-free adaptive second order sliding mode is employed to track 6D visual motions for a robot manipulator. Exponential convergence of errors in both spaces without local minima are demonstrated. The complete control system is experimentally evaluated on a real industrial robot. The robustness of the control scheme is evaluated for cases where the extrinsic parameters of the uncalibrated stereo camera system are changed on-line and unknown when the stereo system is manually moved to obtain a clearer view of the task.

This paper introduces a new comprehensive solution for the open problem of uncalibrated 3D image-based stereo visual servoing for robot manipulators. One of the main contributions of this article is a novel 3D stereo camera model to map positions in the task space to positions in a new 3D Visual Cartesian Space (a visual feature space where 3D positions are measured in pixel). This model is used to compute a full-rank Image Jacobian Matrix (Jimg), which solves several common problems presented on the classical image Jacobians, e.g., image space singularities and local minima. This Jacobian is a fundamental key for the image-based control design, where uncalibrated stereo camera systems can be used to drive a robot manipulator. Furthermore, an adaptive second order sliding mode visual servo control is designed to track 3D visual motions using the 3D trajectory errors defined in the Visual Cartesian Space. The stability of the control in closed loop with a dynamic robot system is formally analyzed and proved, where exponential convergence of errors in the Visual Cartesian Space and task space without local minima are demonstrated. The complete control system is evaluated both in simulation and on a real industrial robot. The robustness of the control scheme is evaluated for cases where the extrinsic parameters of the stereo camera system change on-line and the kinematic/dynamic robot parameters are considered as unknown. This approach offers a proper solution for the common problem of visual occlusion, since the stereo system can be moved to obtain a clear view of the task at any time.