Avances en robótica y automatización

Parallel manipulators, due to its closed-loop structure, possess a number of advantages over traditional serial manipulators such as high rigidity, high load-to-weight ratio, high natural frequencies, high speed and high accuracy [1]. However, they also have a few disadvantages such as a relatively small workspace, relatively complex forward kinematics and the most importantly, existence of singularities inside the workspace [2]. Kinematics analysis of parallel manipulators separate in two types, forward kinematics and inverse kinematics. The inverse kinematics, which maps the task space to joint space, is not difficult to solve. On the other hand, the forward kinematics, which mapsthe jointspace to task space, isso hard to solve. Also, the existence of not only multiple inverse kinematic solutions (or working modes) but also multiple forward kinematic solutions (or assembly modes) is another problem in kinematics analysis [3]. The challenging problem is not to find all possible solutions but to directly determine the unique feasible solutions, the actual physical solution, in among all possible solutions starting from a certain initial configuration[

This paper describes the unique challenges in developing control of complex lateral movements needed by a serpentine robot to ascend steep slopes by climbing over and around affordances/obstacles on the slope. The research extends previous serpentine robot work developing control of sagittal movements for climbing up stairs and over uneven parallel bars. Effective lateral control was developed using an iterative combination of learning, a genetic algorithm, and developer programming. The robot's many simultaneous movements were controlled mostly as a function of very local sensory inputs and little centralized coordination.

Modeling and simulation of robotics hands are significant topics that have been looked into by many robotics Specialists and programming experts. This is due to a demand to build a friendly platform for analyzing proposed hand design and movements, earlier to hand physical construction. For meeting such demands, a dexterous robotic hand software simulator was synthesized. The developed code is dexterity characterized robotic hand modeling and simulation software environment. The simulator was developed for robotics hands research purposes. This manuscript is presenting a brief documentation of such a modeling and simulating environment for simulating dynamic movements of multi-finger robotics hand. The environment is named as the e_GRASP. To make use of other supporting environments, e_GRASP is a Mat lab based simulator, with a quite large number of linked functionalities and routines that helps in simulating hand movements in a defined 3D space. e_GRASP was built after a number of years of experience while dealing with robot hands, hence it is a comprehensive Mat lab based Toolbox that makes use of other Mat lab defined Toolboxes. e_GRASP can also be interfaced to real-time hand control, with an ability to be linked with even higher levels of hierarchy. This includes Mat lab AI Tools, optimization, as considered useful toolboxes for dexterous hands for grasping and manipulation.

This paper presents a unified dynamic modeling framework for differential-drive mobile robots (DDMR). Two formulations for mobile robot dynamics are developed; one is based on Lagrangian mechanics, and the other on Newton-Euler mechanics. Major difficulties experienced when modeling non-holonomic systems in both methods are illustrated and design procedures are outlined. It is shown that the two formulations are mathematically equivalent providing a check on their consistency. The presented work leads to an improved understanding of differentialdrive mobile robot dynamics, which will assist engineering students and researchers in the modeling and design of suitable controllers for DDMR navigation and trajectory tracking.

It is well known that, the main drawback of parallel manipulators is the existence of singularities within its workspace, an Artificial Neural Network (ANN) based solution is proposed in this paper. The proposed approach can certainly learn the input-output data and discover the non-linear relationships which are inherent in the training data. Additionally, the proposed approach can provide solution of the forward kinematic problem with reasonable errors at and in the vicinity of kinematic singularities. The approach is implemented for the 3-RPR, 3-PRR, and 3-RRR planar parallel manipulators.

This paper presents a constructive design of distributed and bounded coordination controllers that force mobile agents with second-order dynamics to track desired trajectories and to avoid collision between them. The control design is based on the new bounded control design technique for second-order systems, and new pairwise collision avoidance functions. The pair wise collision functions are functions of both the relative position and velocity of the agents instead of only the relative position as in the literature. Desired features of the proposed control design include: 1) Boundedness of the control inputs by a predefined bound despite collision avoidance between the agents considered, 2) No collision between any agents, 3) Asymptotical stability of desired equilibrium set, and 4) Instability of all other undesired critical sets of the closed loop system. The proposed control design is then applied to design a coordination control system for a group of vertical take-off and landing (VTOL) aircraft.