Playing table tennis with a human player is a challenging robotic task due to its dynamic nature. Despite a number of researches being devoted to developing robotic table tennis systems, most of the works have demanding hardware requirements and ignore safety measures when generating the swing stoke. To address these issues, we propose a safe motion planning framework that fully pushes the robotic hardware performance limits to play table tennis. In particular, we propose a pipeline to generate manipulator joint trajectories with environmental safety constraints and scale the trajectories to satisfy joint movement limitations. We use three different agents to validate the planning algorithm with our handmade robot platform in both simulation and real-world environments.

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution.Note to Practitioners—The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.

In minimally invasive robotic surgery, the surgical instrument is usually inserted inside the patient’s body through a small incision, which acts as a remote center of motion (RCM). Serial-link manipulators can be used as macro robots on which microsurgical robotic instruments are mounted to increase the number of degrees of freedom of the system and ensure safe task and RCM motion execution. However, the surgical instrument needs to be placed in an appropriate configuration when completing the motion tasks. The contribution of this article is to present a novel framework that preoperatively identifies the best base configuration, in terms of Roll, Pitch, and Yaw angles, of the microsurgical instrument with respect to the macro serial-link manipulator’s end effector in order to achieve the maximum accuracy and dexterity in performing specified tasks. The framework relies on hierarchical quadratic programming for the control, genetic algorithm for the optimization, and on a resilience to error strategy to make sure deviations from the optimum do not affect the system’s performance. Simulation results show that the mounting configuration of the surgical instrument significantly impacts the performance of the whole macro–micro manipulator in executing the desired motion tasks, and both the simulation and experimental results demonstrate that the proposed optimization method improves the overall performance.

Robot modelling is an essential part to properly understand how a robotic system moves and how to control it. The kinematic model of a robot is usually obtained by using Denavit-Hartenberg convention, which relies on a set of parameters to describe the end-effector pose in a Cartesian space. These parameters are assigned based on geometrical considerations of the robotic structure, however, the assigned values may be inaccurate. The purpose of robot kinematic calibration is therefore to find optimal parameters which improve the accuracy of the robot model. In this work we present Kalibrot, an open source Matlab package for robot kinematic calibration. Kalibrot has been designed to simplify robot calibration and easily assess the calibration results. Beside computing the optimal parameters, Kalibrot provides a visualization layer showing the values of the calibrated parameters, what parameters can be identified, and the calibrated robotic structure. The capabilities of the package are here shown through simulated and real world experiments.

In robotically assisted surgical procedures the surgical tool is usually inserted in the patient’s body through a small incision, which acts as a constraint for the motion of the robot, known as remote center of Motion (RCM). The location of the insertion point on the patient’s body has huge effects on the performances of the surgical robot. In this work we present an offline pre-operative framework to identify the optimal insertion point location in order to guarantee accurate and safe surgical task execution. The approach is validated using a serial-link manipulator in conjunction with a surgical robotic tool to perform a tumor resection task, while avoiding nearby organs. Results show that the framework is capable of identifying the best insertion point ensuring high dexterity, high tracking accuracy, and safety in avoiding nearby organs.

Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end-effector rotating, and good adaptivity for object manipulation. Meanwhile, safety and efficiency are primary concerns for robotic dual-arm coordinated manipulation. Thus, the normally adopted fully automated task execution approaches based on environmental perception and motion planning techniques are still inadequate and problematic for the arduous twisting tasks. To this end, this paper presents a novel strategy of the dual-arm coordinated control for twisting manipulation based on the combination of optimized motion planning for one arm and real-time telecontrol with human intelligence for the other. The analysis and simulation results showed it can achieve collision and singularity free for dual arms with enhanced dexterity, safety, and efficiency.

In fields such as minimally invasive surgery, effective control strategies are needed to guarantee safety and accuracy of the surgical task. Mechanical designs and actuation schemes have inevitable limitations such as backlash and joint limits. Moreover, surgical robots need to operate in narrow pathways, which may give rise to additional environmental constraints. Therefore, the control strategies must be capable of satisfying the desired motion trajectories and the imposed constraints. Model Predictive Control (MPC) has proven effective for this purpose, allowing to solve an optimal problem by taking into consideration the evolution of the system states, cost function, and constraints over time. The high nonlinearities in tendon-driven systems, adopted in many surgical robots, are difficult to be modelled analytically. In this work, we use a model learning approach for the dynamics of tendon-driven robots. The dynamic model is then employed to impose constraints on the torques of the robot under consideration and solve an optimal constrained control problem for trajectory tracking by using MPC. To assess the capabilities of the proposed framework, both simulated and real world experiments have been conducted.

In the past few decades machine learning and data analysis have been having a huge growth and they have been applied in many different problems in the field of robotics. Data are usually the result of sensor measurements and, as such, they might be subjected to noise and outliers. The presence of outliers has a huge impact on modelling the acquired data, resulting in inappropriate models. In this work a novel approach for outlier detection and rejection for input/output mapping in regression problems is presented. The robustness of the method is shown both through simulated data for linear and nonlinear regression, and real sensory data. Despite being validated by using artificial neural networks, the method can be generalized to any other regression method.