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

SELF-COLLISION AVOIDANCE OF ARM ROBOT USING GENERATIVE ADVERSARIAL NETWORK AND PARTICLES SWARM OPTIMIZATION (GAN-PSO)

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of  3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator  always gets the same output point which mapped to different input  values. The discriminator  produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis).  The PSO was successfully reduced the number of training epochs of the generator  only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE)

Use Improved Differential Evolution Algorithms to Handle the Inverse Kinetics Problem for Robots with Residual Degrees of Freedom

In this study, the Self-adaptive strategy algorithm for controlling parameters in Differential Evolution algorithm (ISADE) improved from the Differential Evolution (DE) algorithm, as well as the upgraded version of the algorithms has been applied to solve the Inverse Kinetics (IK) problem for the redundant robot with 7 Degree of Freedom (DoF). The results were compared with 4 other algorithms of DE and Particle Swarm Optimization (PSO) as well as Pro-DE and Pro-PSO algorithms. These algorithms are tested in three different Scenarios for the motion trajectory of the end effector of in the workspace. In the first scenario, the IK results for a single point were obtained. 100 points randomly generated in the robot’s workspace was input parameters for Scenario 2, while Scenario 3 used 100 points located on a spline in the robot workspace. The algorithms were compared with each other based on the following criteria: execution time, endpoint distance error, number of generations required and especially quality of the joints’ variable found. The comparison results showed 2 main points: firstly, the ISADE algorithm gave much better results than the other DE and PSO algorithms based on the criteria of execution time, endpoint accuracy and generation number required. The second point is that when applying Pro-ISADE, Pro-DE and Pro-PSO algorithms, in addition to the ability to significantly improve the above parameters compared to the ISADE, DE and PSO algorithms, it also ensures the quality of solved joints’ values

A model-based approach to robot kinematics and control using discrete factor graphs with belief propagation

Much of recent researches in robotics have shifted the focus from traditionally-specific industrial tasks to investigations of new types of robots with alternative ways of controlling them. In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics. We focused on the kinematics aspect of robot control because it provides a fast and systematic solution for the robot agent to move in a dynamic environment. We developed neurally-inspired factor graph models that can be applied on two different robotic systems: a mobile platform and a robotic arm. We also demonstrated that we can extend the static model of the robotic arm into a dynamic model useful for imitating natural movements of a human hand. We tested our methods in a simulation environment as well as in scenarios involving real robots. The experimental results proved the flexibility of our proposed methods in terms of remodeling and learning, which enabled the modeled robot to perform reliably during the execution of given tasks

Synthesis of the Inverse Kinematic Model of Non-Redundant Open-Chain Robotic Systems Using Groebner Basis Theory

[EN] One of the most important elements of a robot's control system is its Inverse Kinematic Model (IKM), which calculates the position and velocity references required by the robot's actuators to follow a trajectory. The methods that are commonly used to synthesize the IKM of open-chain robotic systems strongly depend on the geometry of the analyzed robot. Those methods are not systematic procedures that could be applied equally in all possible cases. This project presents the development of a systematic procedure to synthesize the IKM of non-redundant open-chain robotic systems using Groebner Basis theory, which does not depend on the geometry of the robot's structure. The inputs to the developed procedure are the robot's Denavit-Hartenberg parameters, while the output is the IKM, ready to be used in the robot's control system or in a simulation of its behavior. The Groebner Basis calculation is done in a two-step process, first computing a basis with Faugere's F4 algorithm and a grevlex monomial order, and later changing the basis with the FGLM algorithm to the desired lexicographic order. This procedure's performance was proved calculating the IKM of a PUMA manipulator and a walking hexapod robot. The errors in the computed references of both IKMs were absolutely negligible in their corresponding workspaces, and their computation times were comparable to those required by the kinematic models calculated by traditional methods. The developed procedure can be applied to all Cartesian robotic systems, SCARA robots, all the non-redundant robotic manipulators that satisfy the in-line wrist condition, and any non-redundant open-chain robot whose IKM should only solve the positioning problem, such as multi-legged walking robots.This research was partially funded by Plan Nacional de I+D+i, Agencia Estatal de Investigacion del Ministerio de Economia, Industria y Competitividad del Gobierno de Espana, in the project FEDER-CICYT DPI2017-84201-R.Guzmán-Giménez, J.; Valera Fernández, Á.; Mata Amela, V.; Díaz-Rodríguez, MÁ. (2020). Synthesis of the Inverse Kinematic Model of Non-Redundant Open-Chain Robotic Systems Using Groebner Basis Theory. Applied Sciences. 10(8):1-22. https://doi.org/10.3390/app10082781S122108Atique, M. M. U., Sarker, M. R. I., & Ahad, M. A. R. (2018). Development of an 8DOF quadruped robot and implementation of Inverse Kinematics using Denavit-Hartenberg convention. 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A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Information Sciences, 222, 528-543. doi:10.1016/j.ins.2012.07.051Rokbani, N., Casals, A., & Alimi, A. M. (2014). IK-FA, a New Heuristic Inverse Kinematics Solver Using Firefly Algorithm. Computational Intelligence Applications in Modeling and Control, 369-395. doi:10.1007/978-3-319-11017-2_15Buchberger, B. (2001). Multidimensional Systems and Signal Processing, 12(3/4), 223-251. doi:10.1023/a:1011949421611Kendricks, K. D. (2013). A kinematic analysis of the gmf a-510 robot: An introduction and application of groebner basis theory. Journal of Interdisciplinary Mathematics, 16(2-03), 147-169. doi:10.1080/09720502.2013.800304Wang, Y., Hang, L., & Yang, T. (2006). Inverse Kinematics Analysis of General 6R Serial Robot Mechanism Based on Groebner Base. 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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 Reviewe

Sensing and control for fishlike propulsion in unsteady environments

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 249-263).Fish are equipped with a unique and elaborate flow sensing system, the lateral line, that enables them to reconstruct the near-field three-dimensional flow around their bodies, and hence effect precise control for optimal propulsion and to achieve energy recovery from vortical flows. This is a capability that is not available to engineered underwater systems today. A paradigm example lies in the ability of fish to save energy when swimming in schools, through extracting energy from vortices generated by other fish. For a single fish modeled as an undulating, foil-shaped body at Reynolds number Re=5000, swimming directly behind another fish results in energy savings of 15-20%, compared with swimming alone. This is achieved by properly timing the interaction with vortices generated from the upstream fish, as they travel along its body and tail. Fish that have evolved for sustained fast swimming, such as tunas and dolphins, possess a stiff tail that is morphologically separate from their body. For such fish, the phasing of tail motion is known to be important, and we demonstrate that independent and precise control of the tail is even more critical for flow control in the presence of external vortices. With an independently pitching caudal fin, small variations in phase can alter the energy savings by 15% or more, and precise timing can allow the fish to swim behind another fish with less than 50% of the energy required in quiescent water. We explore the flow mechanisms that lead to this remarkable performance and provide detailed flow visualization documenting the vorticity control effected by the independently pitching tail. We also show that the precise feedback control required to achieve this remarkable swimming performance is feasible using the distributed flow sensing provided by the lateral line. A model-based observer is shown to be capable of extracting the positions of near-field vortices using distributed surface pressure measurements, within an error of less than 1% of the body length. With this precise feedback, we show that the fish can lock in to the frequency of an upstream wake at the correct phase, and fine-tune its tail motion to optimally exploit the wake. This demonstrates that, together, distributed flow sensing and vorticity control provide a powerful tool to control the flow for enhanced swimming performance.by Amy Ruiming Gao.Ph. D