IK-FA, a new heuristic inverse kinematics solver using firefly algorithm

In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, ß, ¿, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10-3 seconds with a position error fitness around 3.116 × 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10-9.Peer ReviewedPostprint (author's final draft

Inverse Kinematic Modelling of a 3-DOF Robotic Manipulator using Hybrid Deep Learning Models

As the degrees of freedom (DOF) for a manipulator rise, so does the complexity of inverse kinematic modeling. This research provides an inverse kinematic model mapped with the aid of a Multilayer Deep Neural Network (DNN) trained using a unique meta-heuristic approach, namely the Gannet Optimization Algorithm (GOA), to decrease the computational weight and time lag for desired output transformation. The suggested design can automatically pick up on the kinematic characteristics of the manipulator. The sole observational basis for repeated learning is the link between input and output. Using the Robot Operating System (ROS), related simulations on a 3-DOF manipulator are performed. The simulation-generated dataset is split 65:35 for the purpose of training and testing the suggested model. Cost, time for the training data, mean relative error, normal mean square error, and mean absolute error for the test data are the metrics utilized for model validation. The efficacy and superiority of the suggested method are demonstrated by a comparison of the GOA-DNN model with the particle swarm optimization (PSO)-DNN and Grey Wolf Optimization (GWO)-DNN meta-heuristic DNN models.publishedVersionPaid open acces

Mathematical modeling and kinematic analysis of 5 degrees of freedom serial link manipulator for online real-time pick and place applications

Modeling and kinematic analysis are crucial jobs in robotics that entail identifying the position of the robot’s joints in order to accomplish particular tasks. This article uses an algebraic approach to model the kinematics of a serial link, 5 degrees of freedom (DOF) manipulator. The analytical method is compared to an optimization strategy known as sequential least squares programming (SLSQP). Using an Intel RealSense 3D camera, the colored object is picked up and placed using vision-based technology, and the pixel location of the object is translated into robot coordinates. The LOBOT LX15D serial bus servo controller was used to transmit these coordinates to the robotic arm. Python3 programming language was used throughout the entire analysis. The findings demonstrated that both analytical and optimized inverse kinematic solutions correctly identified colored objects and positioned them in their appropriate goal points

NICOL: A Neuro-inspired Collaborative Semi-humanoid Robot that Bridges Social Interaction and Reliable Manipulation

Robotic platforms that can efficiently collaborate with humans in physical tasks constitute a major goal in robotics. However, many existing robotic platforms are either designed for social interaction or industrial object manipulation tasks. The design of collaborative robots seldom emphasizes both their social interaction and physical collaboration abilities. To bridge this gap, we present the novel semi-humanoid NICOL, the Neuro-Inspired COLlaborator. NICOL is a large, newly designed, scaled-up version of its well-evaluated predecessor, the Neuro-Inspired COmpanion (NICO). NICOL adopts NICO's head and facial expression display and extends its manipulation abilities in terms of precision, object size, and workspace size. Our contribution in this paper is twofold -- firstly, we introduce the design concept for NICOL, and secondly, we provide an evaluation of NICOL's manipulation abilities by presenting a novel extension for an end-to-end hybrid neuro-genetic visuomotor learning approach adapted to NICOL's more complex kinematics. We show that the approach outperforms the state-of-the-art Inverse Kinematics (IK) solvers KDL, TRACK-IK and BIO-IK. Overall, this article presents for the first time the humanoid robot NICOL, and contributes to the integration of social robotics and neural visuomotor learning for humanoid robots

MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT

This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions: First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control. Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle. Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence. The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available