Given a setup with external cameras and a mobile manipulator with an eye-in-hand camera, we address theproblem of computing a sequence of base poses and grasp choices that allows for clearing objects from atable while minimizing the overall execution time. The first step in our approach is to construct a worldmodel, which is generated by an anchoring process, using information from the external cameras. Next, wedeveloped a planning module which – based on the contents of the world model - is able to create a plausibleplan for reaching base positions and suitable grasp choices keeping execution time minimal. Comparing ourapproach to two baseline methods shows that the average execution cost of plans computed by our approach is40% lower than the naive baseline and 33% lower than the heuristic-based baseline. Furthermore, we integrateour approach in a demonstrator, undertaking the full complexity of the problem.

This paper presents a method for contact-based pose estimation of workpieces using a collaborative robot. The proposed pose estimation exploits positions and surface normal vectors along an arbitrary path on an object with known geometry, where surface normal vectors are estimated based on contact forces measured by the robot. When data is only available along a single path, it is difficult to find initial correspondences between source data (recorded points and normal vectors) and target data (CAD of an object); hence, a novel weighted incremental spatial search approach for generating correspondences based on point pair features is proposed. Subsequently, robust pose estimation is employed to reduce the effect of erroneous correspondences. The proposed pose estimation is verified in simulation on three paths on two objects and with different levels of noise on the source data to quantify the robustness of the algorithm. Finally, the method is experimentally validated to provide an average pose rotation and translation accuracy of$\mathbf{0.55}^{\circ}$and 0.51 mm, respectively, when using the robust estimation cost function Geman-McClure.

Drone grasping on power lines for recharging is challenging since it requires the gripper to be lightweight, carried by a drone, and efficient for a firm grasp. A deep understanding of the power line nature and its magnetic characteristic helps ease such challenges and bring new knowledge to gripper design. In this work, a novel adaptive, lightweight, and fail-safe magnetic gripper with a recharging feature is presented. The gripper exploits the radiated magnetic field of the lines for charging and holding the drone and can easily detach from the line. The gripper design has been validated in the lab and on a quadcopter with a real power line.

The ability to track the 6D pose distribution of an object when a mobile manipulator robot is still approaching the object can enable the robot to pre-plan grasps that combine base and arm motion. However, tracking a 6D object pose distribution from a distance can be challenging due to the limited view of the robot camera. In this work, we present a framework that fuses observations from external stationary cameras with a moving robot camera and sequentially tracks it in time to enable 6D object pose distribution tracking from a distance. We model the object pose posterior as a multi-modal distribution which results in a better performance against uncertainties introduced by large camera-object distance, occlusions and object geometry. We evaluate the proposed framework on a simulated multi-view dataset using objects from the YCB data set. Results show that our framework enables accurate tracking even when the robot camera has poor visibility of the object.

Fast, robust, and flexible part feeding is essential for enabling automation of low volume, high variance assembly tasks. An actuated vision-based solution on a traditional vibratory feeder, referred to here as a vision trap, should in principle be able to meet these demands for a wide range of parts. However, in practice, the flexibility of such a trap is limited as an expert is needed to both identify manageable tasks and to configure the vision system. We propose a novel approach to vision trap design in which the identification of manageable tasks is automatic and the configuration of these tasks can be delegated to an automated feeder design system. We show that the trap's capabilities can be formalized in such a way that it integrates seamlessly into the ecosystem of automated feeder design. Our results on six canonical parts show great promise for autonomous configuration of feeder systems.

This paper presents a framework for transferring surface finishing skills to new surface geometries while preserving the surface finish quality. The main idea is to estimate the contact area between the workpiece and the tool by using 3D point cloud approach and replicate a given material removal rate and the accumulated material removal, as these quantities are the main parameters for quality. The grinding motion trajectory is generated by solving a constrained optimization problem that minimizes the maximal point-wise deviation between actual and desired material removal and simultaneously minimizes the average deviation between actual and desired material removal rate. The proposed approach is verified in simulation to show the difference between direct replication of force/motion and the proposed replication of material removal. Finally, experimental results confirm that the quality of a surface finishing task can be transferred to new surface geometries with the proposed method.

Unmanned Aerial Vehicles (UAVs) have introduced benefits in many areas of the energy sector. Today, power line sensor deployment is manually executed on passive power lines using helicopters, introducing great risks, costs and difficulties for the power distribution companies and the human operators.In this paper, we present a novel modular mechanical system utilizing pneumatic as the actuation source to deploy a sensor unit to a power line using a UAV, with aid of an autonomous alignment algorithm. The results show that the UAV can facilitate the sensor unit, and is capable of deploying it to the power line in an autonomous manner. The works leave opportunity for expanding to further applications in the future.

Vision systems are often used jointly with robotic manipulators to perform automated tasks in industrial applications. Still, the correct set up of such workcells is difficult and requires significant resources. One of the main challenges, when implementing such systems in industrial use cases, is the pose uncertainties presented by the vision system which have to be handled by grasping. In this paper, we present a framework for the design and analysis of optimal gripper finger designs and vision parameters. The proposed framework consists of two parallel methods which rely on vision and grasping simulation to provide an initial estimation of the uncertainty compensation capabilities of the designs. In case the compensation is not feasible with the initial design, an optimization process is introduced, to select the optimal pose estimation parameters and finger designs for the presented task. The proposed framework was evaluated in dynamic simulation and implemented in a real industrial use case.

In this paper we present an initial approach towards reversible robot movement primitives. Our approach is a modification of Dynamic Movement Primitives (DMPs), a widely used framework for robot learning from demonstration. DMPs are based on dynamical systems to guarantee properties such as convergence to a goal state, robustness to perturbation, and the ability to generalize to other goal states. Yet a main limitation of their original formulation is that they do not allow for movements to be reversed. Thus, to execute the same task forwards and backwards would mean to learn two separate primitives. We propose to replace the transformation system in DMPs with the Logistic Differential Equation (LDE), a known time-reversible non-linear system. Similarly to the original DMP formulation, our system's temporal evolution is controlled by a phase system, which in our case is derived from the LDE to guarantee reversibility. We evaluate our approach experimentally with demonstration data from a real robot assembly task, and show comparable properties to those of the original DMP system.

Encoding desired motions into dynamic movement primitives (DMPs) is a common way for generating compact task representations that are able to handle sensor-based goal adaptations. At the same time, a robot should not only express adaptive motion capabilities at planning level, but use also contact wrench feedback in the adaptation and learning process of the DMP. Despite first approaches exist in this direction, no fully integrated approach has been proposed so far. In this paper, we introduce a new class of admittance-coupled DMPs that addresses environmental changes by including contact wrench feedback dynamics into the DMP formalism. Moreover, a novel iterative learning approach is devised that is based on monitoring the overall system passivity analysis in terms of reference power tracking. Simulations and experimental results with the Kuka LWR robot maintaining a non-rigid contact with the environment (wiping a surface) are shown for supporting the validity of our approach.

We present a three-level cognitive system in a learning by demonstration context. The system allows for learning and transfer on the sensorimotor level as well as the planning level. The fundamentally different data structures associated with these two levels are connected by an efficient mid-level representation based on so-called “semantic event chains.” We describe details of the representations and quantify the effect of the associated learning procedures for each level under different amounts of noise. Moreover, we demonstrate the performance of the overall system by three demonstrations that have been performed at a project review. The described system has a technical readiness level (TRL) of 4, which in an ongoing follow-up project will be raised to TRL 6.