Improved Modified Chaotic Invasive Weed Optimization Approach to Solve Multi-Target Assignment for Humanoid Robot

The paper presents an improved modified chaotic invasive weed optimization (IMCIWO) approach for solving a multi-target assignment for humanoid robot navigation. MCIWO is improved by utilizing the Bezier curve for smoothing the path and replaces the conventional split lines. In order to efficiently determine subsequent locations of the robot from the present location on the provided terrain, such that the routes to be specifically generated for the robot are relatively small, with the shortest distance from the barriers that have been generated using the IMCIWO approach. The MCIWO approach designed the path based on obstacles and targets position which is further smoothened by the Bezier curve. Simulations are performed which is further validated by real-time experiments in WEBOT and NAO robot respectively. They show good effectiveness with each other with a deviation of under 5%. Ultimately, the superiority of the developed approach is examined with existing techniques for navigation, and findings are substantially improved

Gait identification and optimisation for amphi-underwater robot by using ant colony algorithm

Manoeuvrable robot commonly has become the focus of the latest heated issues especially in applications that involved disaster rescue, military missions and underwater or extra-terrestrial explorations. Currently, the manoeuvrable robot is controlled manually by the operator and it’s a wheeled type. It is used for rescue missions to transport people from disaster area to the safe zone. However, the robot is incapable of moving automatically, and it goes through terrain or landscape like swarm. Therefore, a suitable platform is required to transport or for other uses especially in dangerous mission. It is very difficult to estimate the movement of the robot to avoid obstacles and choose the alternative path. Hence, this research presents the point-to-point gait identification or path planning of the behavious of the robot to manuever autonomously on both on-land and underwater environment. For the optimization, the robot will travel from one specific point to another with the predefined position within optimized gait and fastest time by using Ant Colony Optimization (ACO) technique. The algorithm being compared, between Ant Colony Algorithm (ACO) and the Particle Swarm Optimisation (PSO) in terms of time and distance. The ACO been chosen because of the positive feedback for rapid discovery and able to use in dynamic applications for example adapts to changes like new distances. The performance of the algorithm showed that the execution time of ACO is more realistic. Hence, Matlab is used to determine the best cost extracted from the ACO with the pre-define of number of iteration and the number of ants. The laboratory-scaled prototype for amphibious vehicle was developed to test the design controlled with ACO technique where Global Positioning System (GPS) is used for the coordination of the robot and Magnetometer for the position of the robot. The robot prototype is able to move autonomously and optimized by the ant colony optimization with predefined position and terrain condition © BEIESP

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

No abstract available

Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model

Path planning is an essential algorithm in autonomous mobile robots, including agricultural robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm is one of the reinforcement learning methods used for path planning. However, for multi-robot system, this algorithm tends to produce the same path for each robot. This research modifies the Q-Learning algorithm in order to produce path variations by utilizing the motivation model, i.e. achievement motivation, in which different motivation parameters will result in different optimum paths. The Motivated Q-Learning (MQL) algorithm proposed in this study was simulated in an area with three scenarios, i.e. without obstacles, uniform obstacles, and random obstacles. The results showed that, in the determined scenario, the MQL can produce 2 to 4 variations of optimum path without any potential of collisions (Jaccard similarity = 0%), in contrast to the Q-Learning algorithm that can only produce one optimum path variation. This result indicates that MQL can solve multi-robots path planning problems, especially when the number of robots is large, by reducing the possibility of collisions as well as decreasing the problem of queues. However, the average computational time of the MQL is slightly longer than that of the Q-Learning

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Motion Planning for Mobile Robots

This chapter introduces two kinds of motion path planning algorithms for mobile robots or unmanned ground vehicles (UGV). First, we present an approach of trajectory planning for UGV or mobile robot under the existence of moving obstacles by using improved artificial potential field method. Then, we propose an I-RRT* algorithm for motion planning, which combines the environment with obstacle constraints, vehicle constraints, and kinematic constraints. All the simulation results and the experiments show that two kinds of algorithm are effective for practical use