The inverse kinematics of mobile manipulators is a challenging problem due to the high degree of kinematic redundancy added by the coupling of the mobile platform and the manipulator. Some different approaches have been proposed to solve this problem, but most of them are either complex in terms of modelling and matrix calculation, with high computational cost and even with singularity problems, or slow in terms of convergence. This paper proposes a new approach for inverse kinematics of mobile manipulators based on the algorithm FABRIK. This new method, named M-FABRIK, has as main advantages the simplicity to implement, a fast convergence and a low computational cost, allowing real-time applications. Furthermore, this solution allows the robot to be positioned according to various criteria, such as decreasing convergence time, avoiding contact with obstacles, avoiding joint angle limits, increasing robot manipulability or even decreasing joint effort, besides avoiding matrix inversion and being robust to singularities. The proposed approach is illustrated by simulation considering a 5 DOF manipulator mounted on an omnidirectional base for a path-tracking task in different environments, including obstacles. A comparison between the proposed approach and classical methods is also presented.

This paper analyses the impact of parametric uncertainties of a mobile robot kinematic model on velocity and pose estimation, providing models and quantifiable knowledge about them. Most works neglect how the uncertainty regarding the robot’s construction aspects (such as wheels radii, distance between them and the robot’s center of mass) affects both velocities and pose estimation. To help readers understand the influence of such parametric uncertainties, we performed experiments in a simulator and used the collected data on the proposed models. The paper also analyses how the magnitude of velocities considered by the controllers and the followed path can decrease or increase the impact of the parametric uncertainties. The proposed models and presented analysis help understanding the influence (isolated or simultaneous) of different sources of uncertainty in the robot’s velocities and pose estimation. This knowledge can be applied to estimate uncertainties for localization methods based on data fusion, complementing or even avoiding experimental procedures. Also, the development of controllers, robot simulators and new methodologies for parameters’ calibration and the design and construction of new robots can also profit from the results presented in this paper.

Inverse kinematics involves calculating joint configurations for a robot manipulator to perform tasks. To solve this problem, various approaches have been proposed, including meta-heuristics methods, which are not specific to a manipulator class and do not require matrix inversion. Particle Swarm Optimization (PSO) is a commonly used meta-heuristic due to its simplicity and effectiveness, but it is prone to local minima. In response to this challenge, several PSO variants have been introduced, incorporating strategies such as multiple populations, adaptive parameters, and particle resampling. This paper focuses on the variant Fully Resample PSO (FRPSO), which performs full particle resampling in each iteration. The study investigates how the number of joints and FRPSO parameters impact its performance. Additionally, the paper proposes a novel extension to FRPSO called Modified-FRPSO. The novel approach involves one less parameter compared to the original method, simplifying the process of fine-tuning the performance of FRPSO. Furthermore, the experiments conducted in environments with obstacles indicate that the new approach outperforms the original FRPSO and PSO in terms of convergence rate and average number of iterations.

This paper develops on the process of inverse kinematics (IK) for manipulator robots based on a recent technique from the computer graphics area that has been highlighted by its simplicity and low computational cost. This IK solver is known as Forward And Backward Reaching Inverse Kinematics (FABRIK) suffers from singularities when applied in kinematic chains composed of one degree of freedom (1-DOF) joints. Some extensions of this method have been proposed to incorporate the constraints in some of the most common joints, however, their application over kinematic chains with only 1-DOF joints is still not possible. Since several manipulators have kinematic chains composed of 1-DOF joints, this work presents a new method, named FABRIK-R, to extend the original method for applications in the robotics manipulators field.

For robotics to become more accessible to people not specialized in the area, it is of fundamental importance to improve and simplify the way people interact with robots. Despite human-robot interaction (HRI) being an effervescent research area, most of the works published so far on the use of gesture interfaces for human-robot communication do not clearly describe how the used gestures were elicited, thus hindering the reproducibility of those works. Considering this, we propose a new and reproducible Frustration-Based Approach (FBA), scientifically established on previous research, which can be used to obtain an intuitive and robust gesture vocabulary for HRI. To accomplish this, we propose Intuitiveness Level (IL), a score to rank gestures according with its intuitiveness. Using IL, it is possible to conceive a complex vocabulary, allowing an increasing of robustness, since more than one gesture can be associated to a task. In a general sense, the proposed methodology is not limited only for HRI, and it can also be used for human-machine interaction in general. In short, the contributions of this work are: ( i ) A complete methodology to elicit gestures to be used as intuitive communication interface between humans and robots. ( ii) A metric of intuitiveness which takes into account at least three different characteristics about the elicited gestures.