Geometric Algebra (GA) provides a unified, compact mathematical framework for geometric computing, simplifying relations typically handled with more complex tools like matrix multiplication. In fields like robotics, GA replaces conventional coordinate-based approaches with the multiplication of special elements called rotors, offering greater efficiency. Despite its advantages, GA’s complexity and the lack of symbolic tools hinder its broader adoption among applied mathematicians and engineers. To address this, this article introduces Symbolic and User-friendly Geometric Algebra Routines (SUGAR), an open source Matlab toolbox. SUGAR streamlines GA usage in Matlab through a collection of user-friendly functions that support both numeric and symbolic computations, even in high-dimensional algebras. Designed for applied mathematics and engineering, it enables intuitive manipulation of geometric elements and transformations in two- and three-dimensional projective and conformal GAs, consistent with established computational methods. Moreover, SUGAR manages multivector functions such as exponential, logarithmic, sinusoidal, and cosine operations, enhancing its applicability in domains like robotics, control systems, and power electronics. Finally, this article also presents three validation examples across these fields, showcasing SUGAR’s practical utility in solving real-world engineering and applied mathematics problems.

Dual-arm manipulation poses many challenges in order to actually perform manipulation tasks with enough efficiency. To cope with this challenges, this work-in-progress describes a system for motion coordination of a mobile anthropomorphic dual-arm robot that focuses on the capability for performing manipulations actions with precision by means of a visual guidance provided by a camera mounted on an articulated head. The proposal presents different control modes, evaluated with real experiments, and discusses the main future research lines.

This paper introduces a novel strategy to enhance the dexterous in-hand manipulation capabilities of robotic hands, focusing on reorienting unknown objects around any specified axis. The proposed method leverages tactile sensing and sensor-to-motor mapping to achieve precise and adaptive manipulation without prior object knowledge. The strategy employs circular finger movements to maintain stable and secure grasps, ensuring even pressure distribution. Preliminary experiments conducted with the Allegro robotic hand validate the efficacy of the approach across various orientations, demonstrating its potential for practical applications.

This work introduces a fully automatic and adaptable pipeline for synthetic depth dataset generation that later on can be used for the training of deep learning algorithms for robotic manipulation. From any available set of 3D object model meshes, the pipeline outputs rendered depth image data with labeled grasp candidates represented as a set of grasping points relative to the camera with associated metrics. The proposed pipeline allows adaptability in various characteristics such as the input dataset of objects, the sampling method, the gripper type, or the grasp evaluation metrics to allow the generation of a more customized, task-oriented collection of labeled grasps relevant for different robotic applications. The implementation is done using Blender’s Python API workspace, avoiding the use of multiple software tools or libraries, reducing the pipeline complexity, facilitating the extensibility, and providing benefits in terms of visualization and debugging.

One major problem in industrial automation is the workload balancing problem. It consists of making the robots or, more generally, the machines, involved in the assembly process to work exactly the same, either by picking and placing the same number of pieces or by having the same number of operational cycles. This paper presents a novel strategy for solving such a problem by means of an evolutionary algorithm. The specific application of this strategy is to balance the workload of a pick-and-place process developed in the facilities of the industrial company Fameccanica Spa Data within the framework of an industrial project between the company and our research group. The novelties concerning the state-of-the-art contributions are: (1) instead of using an explicit fitness function, the candidate solutions at each iteration are evaluated by using a simulation of the entire process; (2) the parameters optimized are the velocity and acceleration of the robots involved in the line and (3) the strategy includes an algorithm for distributing the workload between the robots during the process.