Franziska Zacharias's research while affiliated with German Aerospace Center (DLR) and other places

More and more systems are developed that include several robot arms, like humanoid robots or industrial robot systems. These systems are designed for complex tasks to be solved in cooperation by the robot arms. However, the capabilities of the individual robot arms to perform given tasks or the suitability of a multi-robot system for cooperative tasks cannot be intuitively comprehended. For planning complex tasks or designing robot systems, a representation of a robot arm's workspace is needed that allows to determine from which directions objects in the workspace can be reached. In this paper, the capability map is presented. It is a representation of a robot arm's kinematic capabilities in its workspace. The capability map is used to compare existing robot arms, to support the design phase of an anthropomorphic robot arm and to enable robot workcell planning.

Robot performance indices evaluate how well a robot can apply forces or move during a specific task or throughout the whole workspace. Hence, they potentially contribute to a general description of the versatile workspace that is the focus of this book. In this chapter, criteria used in robot arm design are compared and evaluated with respect to their objectives. It is identified whether these criteria provide measures to evaluate a robotic arm’s kinematic capabilities with respect to reachability and manipulation of objects throughout the workspace. It is determined whether they can be used to represent the robot arm workspace. Furthermore state of the art approaches to model the workspace are analyzed and evaluated.

This chapter summarizes the achievements presented in this book, provides some concluding remarks as well as an outlook on potential future applications and future research directions.

This chapter demonstrates the use of the capability map in planning tasks. Using two examples, it is demonstrated how the capability map can be used to restrict the search space. The algorithms thus address the gap between task planning and path planning that is indicated in Figure 6.1 by the red rectangle. In the first application covered in this chapter a robot is placed to perform a given trajectory. Its suitability for the task is evaluated. In the second application the capability map is used to obtain good parameters for a path planner and bias the path planning process.

In general, every robot arm is designed differently, and therefore has different kinematic capabilities. These capabilities can result in directional structures specific to workspace regions. The robot’s ability to manipulate objects depends on the relative position of the objects. Two-handed manipulation is limited to a region where the workspaces of both arms overlap. The best performance is achieved in an even smaller subspace. In the previous chapter, requirements were identified that a representation of the reachability throughout the workspace has to fulfill. The reachability sphere map is a representation that meets these requirements. The choice of this name becomes clear later. It was first introduced in [117]. As a first step, the construction of the reachability sphere map is described. A visualization scheme is introduced for the representation. It allows the detection of structure in the workspace and enables its visualization. In a second step, a compact abstraction is proposed for the data of the reachability sphere map. The approach is illustrated using a DLR light weight arm (LWR ).

In this chapter, the technical terms relevant for this work are introduced. Furthermore, on different levels of abstraction, components are described that are necessary for a service robot to solve manipulation tasks. The state of the art in high-level logical planning, also called task planning, is analyzed. Here the focus is on robotic manipulation problems. In the subsequent sections, it is analyzed how low-level planners like path planners, grasp planners and robot placement planners are used to solve the subtasks involved in manipulation, e.g. moving to an object, grasping it, and transporting it to a different position. It is examined whether knowledge representations are used to speed up or facilitate planning processes. Furthermore, it is outlined how the high-level and low-level planning can benefit from the use of knowledge representations.

Current humanoid robots are mostly deployed in laboratory environments. However they are envisioned as helpful assistants in future households that e.g. enable elderly citizens to live independently in their homes by supporting them in their daily life. Possible service tasks are clearing the dish washer or setting the table. In this book a model of a robot arm’s workspace is developed. It is used to analyze the scene and a robot’s capabilities. In planning processes it serves as a source of information that supports the decision process.

Assistive robotic systems in household or industrial production environments get more and more capable of performing also complex tasks which previously only humans were able to do. As robots are often equipped with two arms and hands, similar manipulations can be executed. The robust programming of such devices with a very large number of degrees of freedom (DOFs) compared with single industrial robot arms however is laborious if done joint-wise. Two major directions to overcome this problem have been previously proposed. The programming by demonstration (PbD) approach, where human arm and recently also hand motions are tracked, segmented and re-executed in an adaptive way on the robotic system and the high-level planning approach which tries to generate a task sequence on a logical level and attributes geometric information as necessary to generate artificial trajectories to solve the task. Here we propose to combine the best of both worlds. For the very complex motion generation for a robotic hand, a rather direct approach to assign manipulation actions from human demonstration to a human hand is taken. For the combination of different basic manipulation actions the task constraints are segmented from the demonstration action and used to generate a task oriented plan. This plan is validated against the robot kinematic and geometric constraints and then a geometric motion planner can generate the necessary robot motions to fulfill the task execution on the system.

Assistive robotic systems in household or industrial production environments get more and more capable of performing also complex tasks which previously only humans were able to do. As robots are often equipped with two arms and hands, similar manipulations can be executed. The robust programming of such devices with a very large number of degrees of freedom (DOFs) compared with single industrial robot arms however is laborious if done joint-wise. Two major directions to overcome this problem have been previously proposed. The programming by demonstration (PbD) approach, where human arm and recently also hand motions are tracked, segmented and re-executed in an adaptive way on the robotic system and the high-level planning approach which tries to generate a task sequence on a logical level and attributes geometric information as necessary to generate artificial trajectories to solve the task. Here we propose to combine the best of both worlds. For the very complex motion generation for a robotic hand, a rather direct approach to assign manipulation actions from human demonstration to a human hand is taken. For the combination of different basic manipulation actions the task constraints are segmented from the demonstration action and used to generate a task oriented plan. This plan is validated against the robot kinematic and geometric constraints and then a geometric motion planner can generate the necessary robot motions to fulfill the task execution on the system.

This paper presents the graspability map, a novel approach to represent for a particular object the positions and orientations that a given mechanical hand can adopt to achieve a force closure precision grasp. The algorithm is based on the intersection between the fingertip workspaces and the object, plus the verification of a necessary condition for force closure grasps. The maps are computed offline and can be used for comparing the grasp capabilities of different mechanical hands with respect to some benchmark objects. The maps have also potential applications in online grasp and manipulation planning.