The application of DNNs for CV in industrial environments holds significant promise for enhancing production efficiency. However, SMEs often face adoption barriers due to limited technical resources, cost constraints, and reliability concerns—particularly when dealing with disturbances in image acquisition or transmission. This paper discusses ongoing work within the DeepFrame project, which aims to improve the robustness of deep learning systems for industrial sensor data. Rather than presenting a finalized framework, we outline a research agenda and conceptual design to address challenges posed by real-world data disturbances. Building on existing robustness research and insights gathered through expert interviews we propose a conceptual system with two key components: (1) dataset engineering using automated synthetic data generation and robustness evaluation; and (2) multi-modal neural representation to fuse sensor data and reconstruct disrupted inputs.

Insertion tasks are fundamental yet challenging for robots, particularly in autonomous operations, due to their continuous interaction with the environment. AI-based approaches appear to be up to the challenge, but in production they must not only achieve high success rates. They must also ensure insertion quality and reliability. To address this, we introduce QBIT, a quality-aware benchmarking framework that incorporates additional metrics such as force energy, force smoothness and completion time to provide a comprehensive assessment. To ensure statistical significance and minimize the sim-to-real gap, we randomize contact parameters in the MuJoCo simulator, account for perceptual uncertainty, and conduct large-scale experiments on a Kubernetes-based infrastructure. Our microservice-oriented architecture ensures extensibility, broad applicability, and improved reproducibility. To facilitate seamless transitions to physical robotic testing, we use ROS2 with containerization to reduce integration barriers. We evaluate QBIT using three insertion approaches: geometric-based, force-based, and learning-based, in both simulated and real-world environments. In simulation, we compare the accuracy of contact simulation using different mesh decomposition techniques. Our results demonstrate the effectiveness of QBIT in comparing different insertion approaches and accelerating the transition from laboratory to real-world applications. Code is available on GitHub3.

This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of the entire system, specifically in tasks where only the relative pose of the robots is constrained, such as dual-arm scanning of unknown objects. Unlike traditional IK methods using surrogate metrics, our approach directly optimizes execution time while implicitly considering collisions. A neural network based execution time approximator is employed to predict time-efficient joint configurations while accounting for potential collisions. Through experimental evaluation on a system composed of a UR5 and a KUKA iiwa robot, we demonstrate significant reductions in execution time. The proposed method outperforms conventional approaches, showing improved motion efficiency without sacrificing positioning accuracy.

Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that leverages a generative model to decompose a complex planning problem into small manageable ones by incrementally generating subgoals given the current planning context. Then, we take into account the temporal information and use learned time estimators based on different statistic distributions to examine and select the generated subgoal candidates. Experiments show that planning from the current robot state to the selected subgoal can satisfy the given time-dependent constraints while being goal-oriented.

We address the problem of robotic grasping of known and unknown objects using implicit behavior cloning. We train a grasp evaluation model from a small number of demonstrations that outputs higher values for grasp candidates that are more likely to succeed in grasping. This evaluation model serves as an objective function, that we maximize to identify successful grasps. Key to our approach is the utilization of learned implicit representations of visual and geometric features derived from a pre-trained NeRF. Though trained exclusively in a simulated environment with simplified objects and 4-DoF topdown grasps, our evaluation model and optimization procedure demonstrate generalization to 6-DoF grasps and novel objects both in simulation and in real-world settings, without the need for additional data. Supplementary material is available at: https://gergely-soti.github.io/grasp

Offloading software modules to the edge/cloud can enhance a robot’s capabilities by leveraging massive computing power. However, determining which software module should be offloaded and scheduled to which robot/edge/cloud node is a challenging task, particularly for robot fleets with diverse tasks. In this paper, we tackle the software scheduling problem and introduce a taxonomy to categorize software modules and classify their applicability and requirements for offloading. Additionally, by using prior measurements, we model the compute cluster and formalize software scheduling as a multi-objective optimization problem which we tackle with a genetic algorithm. To evaluate our approach with a challenging setup, we build a mobile manipulation task using open-source frameworks and libraries in the Robot Operating System (ROS2) community in simulation as well as a mildly simplified real-world variant. Our evaluation shows significant improvements compared to the built-in scheduler of Kubernetes (K8s) regarding robotic specific metrics such as the rate of missed cycle time in both simulated and real-world experiments.

Precise pick-and-place is essential in robotic applications. To this end, we define an exact training method and an iterative inference method that improve pick-and-place precision with Transporter Networks [1]. We conduct a large scale experiment on 8 simulated tasks. A systematic analysis shows, that the proposed modifications have a significant positive effect on model performance. Considering picking and placing independently, our methods achieve up to 60% lower rotation and translation errors than baselines. For the whole pick-and-place process we observe 50% lower rotation errors for most tasks with slight improvements in terms of translation errors. Furthermore, we propose architectural changes that retain model performance and reduce computational costs and time. We validate our methods with an interactive teaching procedure on real hardware. Supplementary material is available at: https://gergely-soti.github.io/p3

As advanced algorithms enable robots to handle more challenging tasks and operate more autonomously, the on-board computer cannot meet the increased demands regarding computing power and memory storage in an efficient way. Leveraging the massive computing power of the cloud and low-latency connectivity to the edge can compensate for this lack of computing resources. However, this introduces a new challenge related to the deployment of complex robotic software across multiple devices, especially in a large-scale system. This paper presents KubeROS, a unified and fully managed platform for automated deployment of robotic applications developed on top of Robot Operating System 2 (ROS2), in a hybrid computing infrastructure with robots, edge and cloud. KubeROS uses Kubernetes from Cloud Native Computing as its underlying software orchestration framework. It aims to help researchers and developers with no prior cloud computing knowledge deploy their ROS2-based robotic applications at any scale. KubeROS eliminates the need for system configuration and network setup. We demonstrate the applicability of KubeROS by deploying a fleet of simulated mobile manipulators in a clas-sical pick-and-place application. The experiments demonstrate the effects of different deployment strategies for vision-based motion planning under different fleet sizes and workloads. In addition, KubeROS improves task performance by using high-performance computing at the edge and in the cloud, and achieves high resource efficiency when using the shared deployment strategy.

This paper proposes a reachability-aware model predictive control with a discrete control barrier function for backward obstacle avoidance for a tractor-trailer system. The framework incorporates the state-variant reachable set obtained through sampling-based reachability analysis and symbolic regression into the objective function of model predictive control. By optimizing the intersection of the reachable set and iterative non-safe region generated by the control barrier function, the system demonstrates better performance in terms of safety with a constant decay rate, while enhancing the feasibility of the optimization problem. The proposed algorithm improves real-time performance due to a shorter horizon and outperforms the state-of-the-art algorithms in the simulation environment and on a real robot.

The promising automation of flexible surgical instruments and robots is impeded by the lack of sensory means, which allow for sensing of an instrument's position to the surrounding tissue. This work presents a novel sensory method utilizing capacitive proximity sensing to derive a relative localization of a flexible instrument inside a hollow organ. The method is evaluated by exemplary integration of a sensor in a commercial gastroendoscope and accuracy analysis using a high precision robot. The results show an accuracy of distance sensing from a medical phantom's center of 2%. The method is also evaluated for the irregularly shaped surrounding of ex-vivo tissue in a dynamic scenario. This promising approach holds potential for transfer to clinical scenarios and for further development towards pose estimation of flexible surgical robots and shape sensing of a minimally invasive environment.

A robot must comply with very restrictive safety standards in close human-robot collaboration applications. These standards limit the robot's performance because of speed reductions to avoid potentially large forces exerted on humans during collisions. On-robot capacitive proximity sensors (CPS) can serve as a solution to allow higher speeds and thus better productivity. They allow early reactive measures before contacts occur to reduce the forces during collisions. An open question on designing the systems is the selection of an adequate activation distance to trigger safety measures for a specific robot while considering latency and detection robustness. Furthermore, the systems' actual effectiveness of impact attenuation and performance gain has not been evaluated before. In this work, we define and conduct a unified test procedure based on collision experiments to determine these parameters and investigate the performance gain. Two capacitive proximity sensor systems are evaluated on this test strategy on two robots. A significant performance increase can be achieved, since a small detection distance doubles robot operation speed while maintaining the same contact force as without Capacitive Proximity Sensor (CPS). This work can serve as a reference guide for designing, configuring and implementing future on-robot CPS.

With the goal of efficiently computing collisionfree robot motion trajectories in dynamically changing environments, we present results of a novel method for Heuristics Informed Robot Online Path Planning (HIRO). Dividing robot environments into static and dynamic elements, we use the static part for initializing a deterministic roadmap, which provides a lower bound of the final path cost as informed heuristics for fast path-finding. These heuristics guide a search tree to explore the roadmap during runtime. The search tree examines the edges using a fuzzy collision checking concerning the dynamic environment. Finally, the heuristics tree exploits knowledge fed back from the fuzzy collision checking module and updates the lower bound for the path cost. As we demonstrate in real-world experiments, the closed-loop formed by these three components significantly accelerates the planning procedure. An additional backtracking step ensures the feasibility of the resulting paths. Experiments in simulation and the real world show that HIRO can find collisionfree paths considerably faster than baseline methods with and without prior knowledge of the environment.

Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey article, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. In this domain, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Therefore, based on this observation, in the beginning of this article, we propose a categorization to organize the use cases of proximity sensors in human-centered robotics. Then, we devote effort to present the sensing technologies and different measuring principles that have been developed over the years, also providing a summary in form of a table. Following, we review the literature regarding the applications that have been proposed. Finally, we give an overview of the most important trends that will shape the future of this domain.

During the co-presence of human workers and robots, measures are required to avoid injuries from undesired contacts. Capacitive Proximity Sensors (CPSs) offer a cost-effective solution to cover the entire robot manipulator with fast close-range perception for HRC tasks, closing the perception gap between tactile detection and mid-range perception. CPSs do not suffer from occlusion and compared to pure tactile or force sensing, they react earlier and allow increasing the operating speed of Collaborative Robots (Cobots) while still maintaining safety. However, since capacitive coupling to obstacles varies with their distance, shape and material properties, the projection from capacitance to actual distances is a general problem. In this work, we propose an universal benchmark test procedure for fellow researchers to evaluate their CPSs. Considering ISO/TS 15066 for Power and Force Limiting (PFL) as a reference, we derive the requirements for the specified body regions and propose a method for determining the operation speed to comply with PFL based on a pre-defined detection threshold. Finally, the benchmark test procedure is evaluated on three different concepts of CPSs from the contributed researchers, demonstrating the general applicability.

As it happens, despite yet unmatched by robots perception and motor skills humans drop objects during handover because of false grasp detection and early release. Accordingly, the fluent robot-human handover is still an open challenge. This paper presents an approach to a natural robot to human handover using Capacitive Proximity Sensor (CPS) for robust grasp detection and release trigger. We propose an experimental setup for the evaluation using a collaborative robot, an eye-in-hand depth camera, and CPS integrated into the gripper. Three grasp detection methods were implemented and an object release was triggered based on torque-sensing, capacitive sensing, and the combination of both. Finally, a user study was designed and conducted, indicating that the capacitive method is the most preferred type with the shortest human idle time and the highest fluency ratings.

This article proposes the AutomationML query template (AQT) for accessing engineering data stored in the XML-based data format AutomationML (IEC 62714). The motivation of AQT is to facilitate query construction for domain experts who are familiar with AutomationML but unskilled in programming. The contribution of the article is threefold. First, AQT has an AutomationML-based syntax, which allows constructing queries using conventional AutomationML tools, e.g., the AutomationML Editor. Second, the formal semantics of AQT is defined based on the notion of tree pattern queries, which are a fundamental concept for querying tree-structured data, including XML. Finally, algorithms are presented for the automated translation from AQTs to XPath and XQuery programs, which can be executed on any standard-conform XQuery processors. We show that AQT covers the essential query requirements for AutomationML and provide a prototype implementation in Java.

Classical methods of modelling and mapping robot work cells are time consuming, expensive and involve expert knowledge. We present a novel approach to mapping and cell setup using modern Head Mounted Displays (HMDs) that possess self-localisation and mapping capabilities. We leveraged these capabilities to create a point cloud of the environment and build an OctoMap - a voxel occupancy grid representation of the robot's workspace for path planning. Through the use of Augmented Reality (AR) interactions, the user can edit the created Octomap and add safety zones. We perform comprehensive tests of the HoloLens' depth sensing capabilities and the quality of the resultant point cloud. A high-end laser scanner is used to provide the ground truth for the evaluation of the point cloud quality. The amount of false-positive and false-negative voxels in the OctoMap are also tested.

The World Wide Web Consortium (W3C) has published several recommendations for building and storing ontologies, including the most recent OWL 2 Web Ontology Language (OWL). These initiatives have been followed by practical implementations that popularize OWL in various domains. For example, OWL has been used for conceptual modeling in industrial engineering, and its reasoning facilities are used to provide a wealth of services, e.g. model diagnosis, automated code generation, and semantic integration. More specifically, recent studies have shown that OWL is well suited for harmonizing information of engineering systems stored as AutomationML (AML) files. However, OWL and its tools can be cumbersome for direct use by domain experts such that an ontology expert is often required in practice. Although much attention has been paid in the literature to overcome this issue by transforming OWL ontologies from/to AML models automatically, dealing with OWL complex classes remains an open research question. In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models. We show that this approach provides an efficient and intuitive interface for domain experts to visualize, modify, and create OWL complex classes in typical ontology engineering tasks.

In the era of digitization, industrial engineering process generates a vast amount of data. To organize, store, and exchange such data, dedicated international standards are developed, including the XML-based data format AutomationML (AML). AML is standardized as IEC 62714 and is recommended for managing data flow in continuous engineering. Nevertheless, engineering data is inherently heterogeneous, and the harmonization of various data sources presents a bottleneck in the vision of an integrated engineering toolchain. In this paper, we present AMLLearner - a semi-automated system for learning engineering concepts from AML data. The results of learning are formal ontological definitions of engineering artifacts that naturally serve as knowledge for information integration. Based on the previous works on learning in AML, this paper emphasizes the involvement of end users, i.e. domain experts, for providing suggestions and feedback to the learner. To show the interactivity of AMLLearner, we discuss its characteristics regarding the recent study on interactive machine learning systems.

Hand guidance of robots has proven to be a useful tool both for programming trajectories and in kinesthetic teaching. However hand guidance is usually relegated to robots possessing joint-torque sensors (JTS). Here we propose to extend hand guidance to robots lacking those sensors through the use of an Augmented Reality (AR) device, namely Microsoft's Hololens. Augmented reality devices have been envisioned as a helpful addition to ease both robot programming and increase situational awareness of humans working in close proximity to robots. We reference the robot by using a registration algorithm to match a robot model to the spatial mesh. The in-built hand tracking capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations we achieve a completely sensorless hand guidance without any need to build a dynamic model of the robot itself. We did the first tests our approach on a commonly used industrial manipulator, the KUKA KR-5.

Hand guidance emerged from the safety requirements for collaborative robots, namely possessing joint-torque sensors. Since then it has proven to be a powerful tool for easy trajectory programming, allowing lay-users to reprogram robots intuitively. Going beyond, a robot can learn tasks by user demonstrations through kinesthetic teaching, enabling robots to generalise tasks and further reducing the need for reprogramming. However, hand guidance is still mostly relegated to collaborative robots. Here we propose a method that does not require any sensors on the robot or in the robot cell, by using a Microsoft HoloLens augmented reality head mounted display. We reference the robot using a registration algorithm to match the robot model to the spatial mesh. The in-built hand tracking and localisation capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations and propagating it down through the kinematic chain, we achieve a generalised hand guidance without the need to build a dynamic model of the robot itself. We tested our approach on a commonly used industrial manipulator, the KUKA KR-5.

In this paper we present an approach for material recognition using capacitive tactile and proximity sensors. By variating the spatial resolution and the exciter frequency during the measurement in mutual capacitive mode, information about the dielectrical properties of different objects was captured and provided as data frames. For material recognition an artificial neural network was set up and fed with various data sets of different electrode combinations and exciter frequencies. The influence of the electrode combinations and shapes on the recognition accuracy was investigated. It is shown that seven objects of conductive and non-conductive dielectric materials have been ranged with an overall accuracy of about 71%-94%.

Over the next few years, more and more autonomous mobile robot systems will find their way into modern shop floors. However, it will be necessary to provide human-machine interfaces for interventions in unexpected situations like system-deadlocks, algorithm failures or inabilities. Using virtual or mixed reality-technologies, multi-modal teleoperation offers potential for being a suitable human-machine interface. Essential challenges in this field are, among others, a real-time remote control, a time-efficient and holistic environment detection using multiple sensors, a noise-reduced visualization of sensor-data, and capabilities of object recognition. This paper summarizes research results regarding an architecture capable of a near realtime, interoperable, and operator-supporting teleoperation. The focus of this paper is on a method to efficiently process and visualize point-clouds to meet high frame rate demands of virtual reality applications. To provide near real-time feedback of the robot and its environment over large distances, the presented method is capable to segment known objects from unknown objects to reduce bandwidth requirements. The results of this paper were evaluated using a industrial articulated robotic arm for teleoperation via a long distance UDP/IP communication.

A concept consisting of a new configurable vacuum gripper system and a corresponding method for determining optimal grasp configurations solely based on 3D vision is introduced. The robot system consists of a dynamically configurable vacuum gripper, a visual sensor, and a robot arm that are used in combination with a new grasp planner to robustly grasp unknown objects in arbitrary positions. For this purpose, formalized aspects of selecting contact surfaces for arbitrary suction cups are described; the concept involves visual detection of the objects, segmentation, iterative grasp planning, and action execution. The approach allows for a fast and efficient, yet precise execution of grasps. The core idea is a two-step 3D data acquisition approach and grasp point computation that takes advantage of the fact that the suction cups of the gripper can all be aligned axis-parallel. Therefore, an adequate sensor-based surface acquisition is done from a single viewpoint with respect to the gripper. Results of realworld experiments show that the proposed concept is suitable for a wide range of different and unknown objects in our setup.

Many manipulation applications in industrial settings using robots depend on reliable detection and grasping capabilities of stationary or moving objects. The latter demand an extended use of intrinsic and extrinsic sensors being in a closed control-loop with a robotic system. In this paper we present an approach for tracking, reconstruction and grasping of unknown objects from a moving conveyor belt using an external depth sensor and an industrial robot. The approach consists of an algorithm for the estimation of the position, velocity and form of an object, an algorithm for estimation of the conveyor's path and an algorithm for calculating an interception trajectory for the robot. The presented scheme is designed for tracking and reconstruction of unknown rotationally symmetrical objects. Nevertheless, the reconstruction algorithm is able to detect asymmetrical parts of an object and recognize if an object is hollow. This is further used for calculation of a feasible grasp. From the reconstruction data a mesh of the object is created. Finally, robots interception and grasping trajectory are calculated on-line for any arbitrary position on the conveyor. The objects are sucessfully grasped from moving conveyor up to the speed of more than 10 [cm/s].

One of the major investment for applying industrial robots in production resides in the software development, which is an interdisciplinary and heterogeneous engineering process. This paper presents a novel model-driven approach that uses AutomationML as modeling framework and ontological reasoning as inference framework for constructing robotic application using Robot Operating System (ROS). We show how different robotic components can be classified and modeled with AutomationML, how these components can be composed together to a production system, and how the AutomationML models can be processed semantically by utilizing Semantic Web technologies and ontological reasoning. By applying model-to-text transformation techniques, executable ROS code can be generated from the models that foster fast prototyping and the reuse of robotic software.

In this paper we've equipped a cylindrical end-effector with an array of capacitive sensors in order to implement 3D contour following. During the task, using proximity servoing, the sensors are aligned parallel to the surface and kept at a target distance. In addition, due to the spatial resolution, it's possible to estimate the surface's curvature in two dimensions along the rows and columns of the array. We show how a compound movement can be derived from both curvatures that pre-aligns the end-effector in each step, yielding a predictive component for the control scheme. We evaluate our approach with different geometries and show that the curvature information produces smooth contour following paths. We show that the system can handle speeds up to 150mms-1.

In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.

Shortening product lifecycles and small lot sizes require manufacturing systems to adapt increasingly fast. Many existing machine tools, handling and logistics systems are already generic and not bound to a specific product a-priori. Yet this flexibility and reconfigurability on the asset level is lost in automated systems that are limited to executing a small set of predefined actions in a fixed sequence. The SkillPro1project aims to develop a holistic service-oriented framework for modelling and orchestration of modern adaptable manufacturing systems. The core concept is a unified abstraction for manufacturing tasks: skills provided by the available assets and the requirements of the different production steps. The skill-based system model enables the transition from generic high-level descriptions to low-level formats that can be directly executed. Self-describing assets can be added, changed and removed at runtime, taking into account technical and economic conditions to best achieve the manufacturing goals.

In this paper we present a novel approach for intuitive telemanipulation in Cartesian space and discuss the results of a user study evaluating different aspects of our approach. The proposed method inhibits certain degrees of freedom based on the current viewpoint. Together with automatic mapping of the input device to corresponding motion axes, our approach provides a very intuitive method for controlling the telemanipulation system while reducing the mental workload of the operator and therefore the amount of erroneous commands. Similar principles apply for controlling the viewpoint of real or virtual cameras to facilitate manipulation or navigation tasks.

In this paper we present applications for a robot system whose gripper is equipped with distributed capacitive tactile proximity sensors (CTPS). Firstly, we introduce and evaluate a closed loop control scheme by which it is possible to align the gripper to objects or features of the environment using proximity values alone. We call this control method proximity servoing. It is implemented by equilibrating the sensor signals, resulting in a robust preshape in all 6DOF of the object pose. Objects can then be grasped with virtually no displacement. Secondly, also based on proximity servoing, we demonstrate and evaluate novel ideas for combined haptic and proximity-based exploration. Without any cues from an external camera the system is capable of detecting and exploring features such as curvatures, edges or corners of objects. It is also shown that tactile and proximity exploration steps can be used complementarily to increase efficiency in exploration while delivering accurate object measurements.

In this paper we base upon capacitive tactile proximity sensor modules developed in a previous work to demonstrate applications for safe human-robot-interaction. Arranged as a matrix, the modules can be used to model events in the near proximity of the robot surface, closing the near field perception gap in robotics. The central application investigated here is object tracking. Several results are shown: the tracking of two human hands as well as the handling of occlusions and the prediction of collision for object trajectories. These results are important for novel pretouch- and touch-based humanrobot interaction strategies and for assessing and implementing safety capabilities with these sensor systems.

In order to enable robots to execute highly dynamic tasks in dangerous or remote environments, a semiautomatic teleoperation concept has been developed and will be presented in this paper. It relies on a modular software architecture, which allows intuitive control over the robot and compensates latency-based risks by using Augmented Reality techniques together with path prediction and collision avoidance to provide the remote user with visual feedback about the tasks and skills that will be executed. Based on this architecture different skills with high dynamics are integrated in the robot control, so that they can be executed autonomously without the delayed feedback of the user. The skill-based grasping by adherence of smooth or fragile objects during a remote controlled picking and placing task will be exemplary presented.

This paper focuses on the simplification of the on-line programming process of industrial robots. It presents in detail the input part of a modular on-line programming environment presented as overview in [1]. Main concept of this programming environment is an intuitive way of moving and teaching robots, while supporting the user with assisting algorithms like collision avoidance and automatic path planning. Goal is the combination of different approaches from tele-operation, programming by demonstration, Virtual Reality and off-line programming, and to reuse them in a new fashion on-line on the shop-floor. This paper presents some ideas and concepts, how input devices and strategies for robot programming could look like and how to use them to set up an intuitive manual motion control of the robot.

This paper focuses on the simplification of the on-line programming process of industrial robots. It presents a modular on-line programming environment (software and hardware), which supports an intuitive way of moving and teaching robots, while supporting the user with assisting algorithms like collision avoidance and automatic path planning. Main idea is the combination of different approaches from tele-operation, programming by demonstration and off-line programming, and reuse them in a new fashion on-line on the shopfloor. The proposed programming environment is designed to evaluate the usability of different combination of assisting techniques.

An open-loop method on the basis of acceleration compensation to reduce liquid surface oscillation generated during a high-speed transfer will be presented in this paper. In order to suppress the undesirable liquid vibration effects, so called `sloshing', a new simple and effective methodology consisting of adapting the gripper orientation is proposed. The assumption of slosh-free movement will be valid so long as there is no relative motion between the container and the liquid. To accomplish this objective, the maximum acceleration in every time-instant has to be considered in the computation. This represents that our method operates basically in maintaining the normal of the liquid surface opposite to the entire systems acceleration until the completion of the transportation. Experimental results using a manipulator KUKA-KR16 will be demonstrated to validate the effectiveness of our approach.

During the rapid transfer of freely suspended loads, hazardous swing oscillations may be produced. In order to deal with this problem, we propose a new methodology, which derives immediately from the acceleration compensation principle[1]. It consists basically of modifying the original reference-trajectory comprised by a set of suspension points, with the purpose to compensate undesirable residual swing effects at the end of the transfer motion. This open-loop method is computationally simple, time-efficient and feasible. In contrast to the popular methods utilized to control the overhead cranes, which traveling motions are restricted only in the horizontal plane, our compensation technique involves as well the motion in vertical plane. The rope length is assumed invariant. A set of simulations and experimental verifications using an industrial robot manipulator KUKA KR 16 have been carried out to demonstrate the feasibility of the proposed solution.