Multimodal Grasp Planner for Hybrid Grippers in Cluttered Scenes

Grasping a variety of objects is still an open problem in robotics, especially for cluttered scenarios. Multimodal grasping has been recognized as a promising strategy to improve the manipulation capabilities of a robotic system. This work presents a novel grasp planning algorithm for hybrid grippers that allows for multiple grasping modalities. In particular, the planner manages two-finger grasps, single or double suction grasps, and magnetic grasps. Grasps for different modalities are geometrically computed based on the cuboid and the material properties of the objects in the clutter. The presented framework is modular and can leverage any 6D pose estimation or material segmentation network as far as they satisfy the required interface. Furthermore, the planner can be applied to any (hybrid) gripper, provided the gripper clearance, finger width, and suction diameter. The approach is fast and has a low computational burden, as it uses geometric computations for grasp synthesis and selection. The performance of the system has been assessed with an experimental campaign in three manipulation scenarios of increasing difficulty using the objects of the YCB dataset and the DLR hybrid-compliant gripper

A general deterministic sequence for sampling d-dimensional configuration spaces

Previous works have already demonstrated that deterministic sampling can be competitive with respect to probabilistic sampling in sampling-based path planners. Nevertheless, the definition of a general sampling sequence for any d-dimensional Configuration Space satisfying the requirements needed for path planning is not a trivial issue. This paper makes a proposal of a simple and yet efficient deterministic sampling sequence based on the recursive use, over a multi-grid cell decomposition, of the ordering of the 2d descendant cells of any parent cell. This ordering is generated by the digital construction method using a d × d matrix Td. A general expression of this matrix (i.e. for any d) is introduced and its performance analyzed in terms of the mutual distance. The paper ends with a performance evaluation of the use of the proposed deterministic sampling sequence in different well known path planners.Trabajos anteriores ya han demostrado que el muestreo determinista puede ser competitivo con respecto al muestreo probabilístico en los planificadores de rutas basados en el muestreo. Sin embargo, la definición de una secuencia de muestreo general para cualquier espacio de configuración d-dimensional que satisfaga los requisitos necesarios para la planificación de rutas no es una cuestión trivial. En este trabajo se propone una secuencia de muestreo determinista, sencilla y eficiente, basada en el uso recursivo, sobre una descomposición de celdas multirejilla, de la ordenación de las 2 celdas descendientes de cualquier celda padre. Esta ordenación se genera mediante el método de construcción digital utilizando una matriz d × d Td. Se introduce una expresión general de esta matriz (es decir, para cualquier d) y se analiza su rendimiento en términos de distancia mutua. El artículo termina con una evaluación del rendimiento del uso de la secuencia de muestreo determinista propuesta en distintos planificadores de rutas conocidos

Optimización Bayesiana para un agarre robótico robusto.

Among the most complex tasks performed by humans is the manipulation of objects. In robotics, automating these tasks has applications in a variety of environments, such as the development of industrial processes or providing assistance to people with physical or motor disabilities. Using bio-inspired robotic hands is helping the emergence of increasingly robust and dexterous grasping strategies. However, the difficulty lies in adapting these strategies to the variety of tasks and objects, which can often be unknown also involving the computational overhead of identifying them and reconfiguring the grasp. The brute-force solution is to learn new grasps by trial and error. This method however is inefficient and ineffective, as it is based on pure randomness. In contrast, Bayesian optimization allows us to turn this process into active learning, where each attempt adds information to the approximation of an optimal grasp, in a manner analogous to a child’s learning. The present work aims to test Bayesian optimization in this context, providing some techniques to enhance its performance, and experimenting not only in simulation but also on real robots, as well as studying different grasp metrics that allow the grasp evaluation during the optimization process and how they behave when computing from a real system. For this, along this work, we implemented a realistic simulation environment using PyBullet, which emulates the real experimental environment. This work provides experimental results using the Light Weight robotic arm, designed at the German Aerospace Center (DLR), and two tridactyl robotic hands, the CLASH (DLR) and ReFlex TakkTile (Right Hand Robotic), demonstrating the usefulness of the method for performing unknown object grasping even in the presence of noise and uncertainty inherent in a real-world environment. Consequently, this work contributes with practical knowledge to the studied field and serves as a proof-of-concept for future grasp planning and robotic manipulation technology<br /

Grasp Quality Evaluation in Underactuated Robotic Hands

Underactuated and synergy-driven hands are gaining attention in the grasping community mainly due to their simple kinematics, intrinsic compliance and versatility for grasping objects even in non structured scenarios. The evaluation of the grasping capabilities of such hands is a challenging task. This paper revisits some traditional quality measures developed for multi-fingered, fully actuated hands, and applies them to the case of underactuated hands. The extension of quality metrics for synergy-driven hands for the case of underactuated grasping is also presented. The performance of both types of measures is evaluated with simulated examples, concluding with a comparative discussion of their main features

Grasp Quality Evaluation in Underactuated Robotic Hands

Underactuated and synergy-driven hands are gaining attention in the grasping community mainly due to their simple kinematics, intrinsic compliance and versatility for grasping objects even in non structured scenarios. The evaluation of the grasping capabilities of such hands is a challenging task. This paper revisits some traditional quality measures developed for multi-fingered, fully actuated hands, and applies them to the case of underactuated hands. The extension of quality metrics for synergy-driven hands for the case of underactuated grasping is also presented. The performance of both types of measures is evaluated with simulated examples, concluding with a comparative discussion of their main features

Video1_Task-specific robot base pose optimization for robot-assisted surgeries.MP4

Preoperative planning and intra-operative system setup are crucial steps to successfully integrate robotically assisted surgical systems (RASS) into the operating room. Efficiency in terms of setup planning directly affects the overall procedural costs and increases acceptance of RASS by surgeons and clinical personnel. Due to the kinematic limitations of RASS, selecting an optimal robot base location and surgery access point for the patient is essential to avoid potentially critical complications due to reachability issues. To this end, this work proposes a novel versatile method for RASS setup and planning based on robot capability maps (CMAPs). CMAPs are a common tool to perform workspace analysis in robotics, as they are in general applicable to any robot kinematics. However, CMAPs have not been completely exploited so far for RASS setup and planning. By adapting global CMAPs to surgical procedure-specific tasks and constraints, a novel RASS capability map (RASSCMAP) is generated. Furthermore, RASSCMAPs can be derived to also comply with kinematic access constraints such as access points in laparoscopy. RASSCMAPs are versatile and applicable to any kind of surgical procedure; they can be used on the one hand for aiding in intra-operative experience-based system setup by visualizing online the robot’s capability to perform a task. On the other hand, they can be used to find the optimal setup by applying a multi-objective optimization based on a genetic algorithm preoperatively, which is then transfered to the operating room during system setup. To illustrate these applications, the method is evaluated in two different use cases, namely, pedicle screw placement in vertebral fixation procedures and general laparoscopy. The proposed RASSCMAPs help in increasing the overall clinical value of RASS by reducing system setup time and guaranteeing proper robot reachability to successfully perform the intended surgeries.</p