A Spherical Active Joint for Humanoids and Humans

Both humanoid robotics and prosthetics rely on the possibility of implementing spherical active joints to build dexterous robots and useful prostheses. There are three possible kinematic implementations of spherical joints: serial, parallel, and hybrid, each one with its own advantages and disadvantages. In this letter, we propose a hybrid active spherical joint, that combines the advantages of parallel and serial kinematics, to try and replicate some of the features of biological articulations: large workspace, compact size, dynamical behavior, and an overall spherical shape. We compare the workspace of the proposed joint to that of human joints, showing the possibility of an almost-complete coverage by the device workspace, which is limited only by kinematic singularities. A first prototype is developed and preliminarly tested as part of a robotic shoulder joint

Haptic-Guided Shared Control Grasping for Collision-Free Manipulation

We propose a haptic-guided shared control system that provides an operator with force cues during reach-to-grasp phase of tele-manipulation. The force cues inform the operator of grasping configuration which allows collision-free autonomous post-grasp movements. Previous studies showed haptic guided shared control significantly reduces the complexities of the teleoperation. We propose two architectures of shared control in which the operator is informed about (1) the local gradient of the collision cost, and (2) the grasping configuration suitable for collision-free movements of an aimed pick-and-place task. We demonstrate the efficiency of our proposed shared control systems by a series of experiments with Franka Emika robot. Our experimental results illustrate our shared control systems successfully inform the operator of predicted collisions between the robot and an obstacle in the robot's workspace. We learned that informing the operator of the global information about the grasping configuration associated with minimum collision cost of post-grasp movements results in a reach-to-grasp time much shorter than the case in which the operator is informed about the local-gradient information of the collision cost

Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

A Neuro-Symbolic Approach for Enhanced Human Motion Prediction

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy

A neuromuscular-model based control strategy to minimize muscle effort in assistive exoskeletons

In literature, much attention has been devoted to the design of control strategies of exoskeletons for assistive purposes. While several control schemes were presented, their performance still has limitations in minimizing muscle effort. According to this principle, we propose a novel approach to solve the problem of generating an assistive torque that minimizes muscle activation under stability guarantees. First, we perform a linear observability and controllability analysis of the human neuromuscular dynamic system. Based on the states that can be regulated with the available measurements and taking advantage of knowledge of the muscle model, we then solve an LQR problem in which a weighted sum of muscle activation and actuation torque is minimized to systematically synthesize a controller for an assistive exoskeleton.We evaluate the performance of the developed controller with a realistic non-linear human neuromusculoskeletal model. Simulation results show better performance in comparison with a well known controller in the literature, in the sense of closed loop system stability and regulation to zero of muscle effort

A compact soft articulated parallel wrist for grasping in narrow spaces

The increasing presence of high density logistic warehouses demands the deployment of fast and flexible robotic solutions. One of the open challenges toward this objective is manipulation in narrow settings. This work addresses such a problem from a design perspective. By observing human arm dexterity and grasp strategies, the role of the wrist emerges as fundamental in providing both a large workspace and a minimal clearance. We compare the kinematic envelope of robotic manipulators wrist to their human counterpart through the introduction of the reversed workspace, defined as the volume required by a kinematic chain for a set of end-effector orientations. Results suggest to combine the properties of serial and parallel architectures, to obtain a suitable tradeoff between compactness and workspace. On this base, we present a novel soft articulated parallel wrist device that can be easily interfaced with industrial off-the-shelf manipulators to enhance their manipulation capabilities in constrained environments