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

An Innovative Mission Management System for Fixed-Wing UAVs

This paper presents two innovative units linked together to build the main frame of a UAV Mis- sion Management System. The first unit is a Path Planner for small UAVs able to generate optimal paths in a tridimensional environment, generat- ing flyable and safe paths with the lowest com- putational effort. The second unit is the Flight Management System based on Nonlinear Model Predictive Control, that tracks the reference path and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves on-line (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that min- imizes the tracking error with respect to the ref- erence path, driving the aircraft far from sensed obstacles and towards the desired trajectory

Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

A general learning co-evolution method to generalize autonomous robot navigation behavior

Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems

An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning

Mobile robotic platforms are an indispensable tool for various scientific and industrial applications. Robots are used to undertake missions whose execution is constrained by various factors, such as the allocated time or their remaining energy. Existing solutions for resource constrained multi-robot sensing mission planning provide optimal plans at a prohibitive computational complexity for online application [1],[2],[3]. A heuristic approach exists for an online, resource constrained sensing mission planning for a single vehicle [4]. This work proposes a Genetic Algorithm (GA) based heuristic for the Correlated Team Orienteering Problem (CTOP) that is used for planning sensing and monitoring missions for robotic teams that operate under resource constraints. The heuristic is compared against optimal Mixed Integer Quadratic Programming (MIQP) solutions. Results show that the quality of the heuristic solution is at the worst case equal to the 5% optimal solution. The heuristic solution proves to be at least 300 times more time efficient in the worst tested case. The GA heuristic execution required in the worst case less than a second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and Automation Letters (RA-L