Hybrid PSO-cubic spline for autonomous robots optimal trajectory planning

This paper presents a new version of the Particle Swarm Optimization algorithm where the particles are replaced by spline functions. The developed algorithm generates smooth motion trajectories with two times continuously differentiable curvature avoiding obstacles placed in the workspace. It can be used for autonomous robot path planning or transport problems. The spline based trajectory generation gives us continuous, smooth and optimized path trajectories. Simulation and experimental results demonstrate the effectiveness of the proposed method.info:eu-repo/semantics/publishedVersio

Path Planning Based on Parametric Curves

Parametric curves are extensively used in engineering. The most commonly used parametric curves are, Bézier, B-splines, (NURBSs), and rational Bézier. Each and every one of them has special features, being the main difference between them the complexity of their mathematical definition. While Bézier curves are the simplest ones, B-splines or NURBSs are more complex. In mobile robotics, two main problems have been addressed with parametric curves. The first one is the definition of an initial trajectory for a mobile robot from a start location to a goal. The path has to be a continuous curve, smooth and easy to manipulate, and the properties of the parametric curves meet these requirements. The second one is the modification of the initial trajectory in real time attending to the dynamic properties of the environment. Parametric curves are capable of enhancing the trajectories produced by path planning algorithms adapting them to the kinematic properties of the robot. In order to avoid obstacles, the shape modification of parametric curves is required. In this chapter, an algorithm is proposed for computing an initial Bézier trajectory of a mobile robot and subsequently modifies it in real time in order to avoid obstacles in a dynamic environment

Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A* and Dijkstra) into an efficient kinodynamic search, by exploring the properties of B-spline parameterization. The RBK search is greedy and produces a dynamically feasible time-parameterized trajectory efficiently, which facilitates non-static initial state of the quadrotor. To cope with the limitation of the greedy search and the discretization induced by a grid structure, we adopt an elastic optimization (EO) approach as a post-optimization process, to refine the control point placement provided by the RBK search. The EO approach finds the optimal control point placement inside an expanded elastic tube which represents the free space, by solving a Quadratically Constrained Quadratic Programming (QCQP) problem. We design a receding horizon replanner based on the local control property of B-spline. A systematic comparison of our method against two state-of-the-art methods is provided. We integrate our replanning system with a monocular vision-based quadrotor and validate our performance onboard.Comment: 8 pages. Published in International Conference on Robotics and Automation (ICRA) 2018. IEEE copyrigh

Toolpath Smoothing using Clothoids for High Speed CNC Machines

As a result of this research, new methods for CNC toolpath smoothing were developed. Utilising these methods can increase the speed, decrease vibrations and improve the cut quality of a CNC machine. In the developed techniques, Euler spirals have been used to smooth the corners

Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks

This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm

Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm

The purpose of this paper is to present an algorithm to determine the shortest path for quadrotor to be able to navigate in an unknown area. The problem in robot navigation is that a robot has incapability of finding the shortest path while moving to the goal position and avoiding obstacles. Hence, a modification of several algorithms are proposed to enable the robot to reach the goal position through the shortest path. The algorithms used are fuzzy logic and cell decomposition algorithms, in which the fuzzy algorithm which is an artificial intelligence algorithm is used for robot path planning and cell decomposition algorithm is used to create a map for the robot path, but the merger of these two algorithms is still incapable of finding the shortest distance. Therefore, this paper describes a modification of the both algorithms by adding potential field algorithm that is used to provide weight values on the map in order for the quadrotor to move to its goal position and find the shortest path. The modification of the algorithms have shown that quadrotor is able to avoid various obstacles and find the shortest path so that the time required to get to the goal position is more rapid

Smooth Three-Dimensional Route Planning for Fixed-Wing Unmanned Aerial Vehicles With Double Continuous Curvature

This paper presents a smooth flight path planner for maneuvering in a 3D Euclidean space, which is based on two new space curves. The first one is called 'Elementary Clothoid-based 3D Curve (ECb3D)', which is built by concatenating two symmetric Clothoid-based 3D Curves (Cb3D). The combination of these curves allows to reach an arbitrary orientation in 3D Euclidean space. This new curve allows to generate continuous curvature and torsion profiles that start and finish with a null value, which means that they can be concatenated with other curves, such as straight segments, without generating discontinuities on those variables. The second curve is called 'Double Continuous Curvature 3D Curve (DCC3D)' which is built as a concatenation of three straight line segments and two ECb3D curves, allowing to reach an arbitrary configuration in position and orientation in the 3D Euclidean space without discontinuities in curvature and torsion. This trajectory is applied for autonomous path planning and navigation of unmanned aerial vehicles (UAVs) such as fixed-wing aircrafts. Finally, the results are validated on the FlightGear 2018 flight simulator with the UAV kadett 2400 platform

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences