Motion Planning for Autonomous Ground Vehicles Using Artificial Potential Fields: A Review

Autonomous ground vehicle systems have found extensive potential and practical applications in the modern world. The development of an autonomous ground vehicle poses a significant challenge, particularly in identifying the best path plan, based on defined performance metrics such as safety margin, shortest time, and energy consumption. Various techniques for motion planning have been proposed by researchers, one of which is the use of artificial potential fields. Several authors in the past two decades have proposed various modified versions of the artificial potential field algorithms. The variations of the traditional APF approach have given an answer to prior shortcomings. This gives potential rise to a strategic survey on the improved versions of this algorithm. This study presents a review of motion planning for autonomous ground vehicles using artificial potential fields. Each article is evaluated based on criteria that involve the environment type, which may be either static or dynamic, the evaluation scenario, which may be real-time or simulated, and the method used for improving the search performance of the algorithm. All the customized designs of planning models are analyzed and evaluated. At the end, the results of the review are discussed, and future works are proposed

Групповое управление движением мобильных роботов в неопределенной среде с использованием неустойчивых режимов

The paper is devoted to the methods and algorithms of group control, providing a consistent movement of a moving objects group in a partially uncertain three-dimensional environment with obstacles. The uncertainty of the environment is a priori unknown moving obstacles. Mobile robots of the group should create a formation in a given rectangular area in the plane. The robots formation must move in the given direction. The problem solution is carried out by using algorithms based on unstable modes. The unstable modes allow to transform obstacles into repellers. The proposed algorithms can be implemented decentralized. Two algorithms options for group control are analyzed in the paper. Also a numerical simulation of a hexacopters group in an uncertain environment with obstacles is performed. The developed algorithms are used in the control system of mobile robots in their group locomotion in an uncertain 3-d environment. A group of mobile robots should be self-organized into a structure that does not require a preliminary assignment of the place of each robot. The group of robots can adjust the formation at occurrence of obstacles.В настоящей статье рассматриваются алгоритмы управления, обеспечивающие согласованное перемещение группы роботов в неопределенной трехмерной среде с препятствиями. Неопределенность среды заключается в наличии априори неизвестных препятствий, часть которых может быть нестационарными. Мобильные роботы группы должны автоматически распределиться в заданной прямоугольной области на плоскости и двигаться в направлении, перпендикулярном указанной области, по возможности сохраняя заданное взаимное расположение. В данной статье предлагаются новые алгоритмы автоматического распределения роботов на плоскости, не предполагающие предварительного назначения места каждого робота. Эта задача решается с применением триангуляции Делоне и дальнейшей оптимизации положения робота. Для коррекции движения отдельного робота и всей группы при сближении с препятствием предложены алгоритмы, базирующиеся на неустойчивых режимах, позволяющих трансформировать препятствия в репеллеры. Рассмотрено два варианта алгоритмов обхода препятствий. В первом варианте используются только неустойчивые режимы, а во втором варианте — гибридный алгоритм, включающий интеллектуальный анализ текущей ситуации и неустойчивый режим движения. Предложенные алгоритмы могут реализовываться децентрализовано. В статье анализируются два варианта алгоритмов группового управления, а также выполняется численное моделирование группы из 5 гексакоптеров в неопределенной среде с неподвижными и подвижными препятствиями. Также приведены экспериментальные данные, подтверждающие работоспособность предлагаемых алгоритмов на примере полета двух гексакоптеров в среде с неподвижным препятствием. Разработанные алгоритмы могут применяться в системах управления мобильными роботами при их групповом движении в неопределенных 3-D средах

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAV) using nonlinear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant

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

Desain dan Uji Coba Sederhana Pada Obstacle Avoiding Robot Menggunakan Mikrokontroler Arduino

Pada tulisan ini membahas tentang desain, konstruksi dan kontrol robot penyeimbang diri dari obstacle avoiding robot roda dua. Rangkaian sistem terdiri dari sepasang motor DC dan papan mikrokontroler Arduino, dengan menggunakan sebuah sensor ping HC-SR04 yang merupakan sensor ultrasonik untuk mendeteksi jarak ke rintangan dan dari posisi robot berada. Hasil yang diperoleh dari uji coba menunjukkan nilai jarak terhadap nilai ping (ping number) tegak lurus, semakin bertambahnya jarak maka akan semakin tinggi waktu ping yang di hasilkan. Nilai jarak sensor ke objek dan waktu ping paling rendah yaitu 2 cm, dan 132 µS, dan yang paling tinggi dengan jarak 20 cm dengan waktu ping yaitu 1160 µS. Semakin jauh jarak obstacle maka waktu yang diperlukan sensor untuk mengirimkan kembali hasil deteksi juga lebih lama.  This paper discusses the design, construction and control of the self-balancing robot obstacle avoiding thetwo-wheeled robot. The system circuit consists of a pair of DC motors and an Arduino microcontroller board,using a HC-SR04 ping sensor which is an ultrasonic sensor to detect the distance to the obstacle and from wherethe located of the robot. The results obtained from the test show the value of distance to the ping value (pingnumber) perpendicular, the more the distance will increase the higher the ping time generated. The value of thesensor distance to the object and the lowest ping time is 2 cm, and 132 μS, and the highest with a distance of 20 cmwith a ping time of 1160 μS. The further the obstacle distance, the time it takes the sensor to send back thedetection result is also longer

D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments

Finding the optimum path for a robot for moving from start to the goal position through obstacles is still a challenging issue. This paper presents a novel path planning method, named D-point trigonometric, based on Q-learning algorithm for dynamic and uncertain environments, in which all the obstacles and the target are moving. We define a new state, action and reward functions for the Q-learning by which the agent can find the best action in every state to reach the goal in the most appropriate path. The D-point approach minimizes the possible number of states. Moreover, the experiments in Unity3D confirmed the high convergence speed, the high hit rate, as well as the low dependency on environmental parameters of the proposed method compared with an opponent approach