Rico Belder

Dynamic Motion Planning for Robots in Partially Unknown Environments

Abstract In both domestic and also industrial settings robotic Co-Workers are expected to become a commodity. Even though the particular application areas may vastly change, a robot always needs to act in a dynamic and partially unknown environment. It shall reactively generate motions and prevent upcoming collisions. If contact is desired or inevitable, it has to handle it robustly and safely. For preventing collisions in a real-time fashion the Circular Fields method is a powerful scheme, which we developed further and evaluated extensively. After an initial analysis in rather complex 2D simulations, we extend the evaluation to 3D as well as 6D, where we introduce a hybrid strategy based on Circular and Potential Fields. Finally, the 6D implementation of a hybrid Circular & Potential Fields approach is used to perform the experimental analysis for static multi-object parcours and to avoid a dynamically moving human in a 6D task motion. Based on the algorithms for collision avoidance we also develop and experimentally verify an algorithm for tactile exploration of complex planar 3D wire elements, whose structure is a-priori unknown.

Holistic design and analysis for the human-friendly robotic co-worker

In this overview paper we present current work on safety analysis for physical Human-Robot Interaction (pHRI) and motion control methods for robotic co-workers. In particular, we introduce the analysis tools for investigating the potential injury a human would suffer during robot-human impacts. Furthermore, we outline our concept for establishing a procedure towards standardized crash testing in robotics with automobile crash-test dummies. Since it is only possible to investigate blunt impacts with these devices, we developed a drop testing setup for analyzing soft-tissue injury in robotics from a biomechanics perspective. In the second part of the paper, some of our methods for task preserving and task relaxing motion schemes are described, which enable collision avoidance in real-time. The algorithms are well suited to work in an integrated fashion with the soft robotics control developed for the DLR Lightweight Robot III (LWR-III). In addition, it is shown how the torque sensing capabilities of the robot can be used to support reactive motion schemes. Finally, an overview of our human-friendly control architecture for the LWR-III is given, which unifies the rich bundle of developed methods for this manipulator

Robotic agents capable of natural and safe physical interaction with human co-workers

Many future application scenarios of robotics envision robotic agents to be in close physical interaction with humans: On the factory floor, robotic agents shall support their human co-workers with the dull and health threatening parts of their jobs. In their homes, robotic agents shall enable people to stay independent, even if they have disabilities that require physical help in their daily life - a pressing need for our aging societies. A key requirement for such robotic agents is that they are safety-aware, that is, that they know when actions may hurt or threaten humans and actively refrain from performing them. Safe robot control systems are a current research focus in control theory. The control system designs, however, are a bit paranoid: programmers build “software fences” around people, effectively preventing physical interactions. To physically interact in a competent manner robotic agents have to reason about the task context, the human, and her intentions. In this paper, we propose to extend cognition-enabled robot control by introducing humans, physical interaction events, and safe movements as first class objects into the plan language. We show the power of the safety-aware control approach in a real-world scenario with a leading-edge autonomous manipulation platform. Finally, we share our experimental recordings through an online knowledge processing system, and invite the reader to explore the data with queries based on the concepts discussed in this paper.

Collision Avoidance with Potential Fields Based on Parallel Processing of 3D-Point Cloud Data on the GPU

In this paper we present an experimental study on real-time collision avoidance with potential fields that are based on 3D point cloud data and processed on the Graphics Processing Unit (GPU). The virtual forces from the potential fields serve two purposes. First, they are used for changing the reference trajectory. Second they are projected to and applied on torque control level for generating according nullspace behavior together with a Cartesian impedance main control loop. The GPU algorithm creates a map representation that is quickly accessible. In addition, outliers and the robot structure are efficiently removed from the data, and the resolution of the representation can be easily adjusted. Based on the 3D robot representation and the remaining 3D environment data, the virtual forces that are fed to the trajectory planning and torque controller are calculated. The algorithm is experimentally verified with a 7-Degree of Freedom (DoF) torque controlled KUKA/DLR Lightweight Robot for static and dynamic environmental conditions. To the authors knowledge, this is the first time that collision avoidance is demonstrated in real-time on a real robot using parallel GPU processing.

RAFCON: A graphical tool for engineering complex, robotic tasks

Robotic tasks are becoming increasingly complex, and with this also the robotic systems. This requires new tools to manage this complexity and to orchestrate the systems to fulfill demanding autonomous tasks. For this purpose, we developed a new graphical tool targeting at the creation and execution of robotic tasks, called RAFCON. These tasks are described in hierarchical state machines supporting concurrency. A formal notation of this concept is given. The tool provides many debugging mechanisms and a GUI with a graphical editor, allowing for intuitive visual programming and fast prototyping. The application of RAFCON for an autonomous mobile robot in the SpaceBotCamp competition has already proved to be successful.

Safe Acting and Manipulation in Human Environments: A Key Concept for Robots in our Society

In this paper we review our work on safe acting and manipulation in human environments. In order for a robot to be able to safely interact with its environment it is primary to be able to react to unforeseen events in real-time on basically all levels of abstraction. Having this goal in mind, our contributions reach from fundamental understanding of human injury due to robot-human collisions as the underlying metric for “safe” behavior, various interaction control schemes that ground on the basic components impedance control and collision behavior, to real-time motion planning and behavior based control as an interface level for task planning. A significant amount of this work has found found its way into international standardization committees, products, and was applied in numerous real-world applications.

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

This paper treats the systematic injury analysis of lower arm robot–human impacts. For this purpose, a passive mechanical lower arm (PMLA) was developed that mimics the human impact response and is suitable for systematic impact testing and prediction of mild contusions and lacerations. A mathematical model of the passive human lower arm is adopted to the control of the PMLA. Its biofidelity is verified by a number of comparative impact experiments with the PMLA and a human volunteer. The respective dynamic impact responses show very good consistency and support the fact that the developed device may serve as a human substitute in safety analysis for the described conditions. The collision tests were performed with two different robots: the DLR Lightweight Robot III (LWR-III) and the EPSON PS3L industrial robot. The data acquired in the PMLA impact experiments were used to encapsulate the results in a robot independent safety curve, taking into account robots reflected inertia, velocity and impact geometry. Safety curves define the velocity boundaries on robot motions based on the instantaneous manipulator dynamics and possible human injury due to unforeseen impacts.

RAFCON: a Graphical Tool for Task Programming and Mission Control

There are many application fields for robotic systems including service robotics, search and rescue missions, industry and space robotics. As the scenarios in these areas grow more and more complex, there is a high demand for powerful tools to efficiently program heterogeneous robotic systems. Therefore, we created RAFCON, a graphical tool to develop robotic tasks and to be used for mission control by remotely monitoring the execution of the tasks. To define the tasks, we use state machines which support hierarchies and concurrency. Together with a library concept, even complex scenarios can be handled gracefully. RAFCON supports sophisticated debugging functionality and tightly integrates error handling and recovery mechanisms. A GUI with a powerful state machine editor makes intuitive, visual programming and fast prototyping possible. We demonstrated the capabilities of our tool in the SpaceBotCamp national robotic competition, in which our mobile robot solved all exploration and assembly challenges fully autonomously. It is therefore also a promising tool for various RoboCup leagues.