scholarly journals Autonomous navigation in unknown environment using sliding mode SLAM and genetic algorithm

2021 ◽  
Author(s):  
Salvador Ortiz ◽  
Wen Yu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Sliding Mode Control ◽  
Autonomous Navigation ◽  
Sliding Mode ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Bounded Uncertainties

In this paper, sliding mode control is combined with the classical simultaneous localization and mapping (SLAM) method. This combination can overcome the problem of bounded uncertainties in SLAM. With the help of genetic algorithm, our novel path planning method shows many advantages compared with other popular methods.

scholarly journals A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090320
Author(s):  
Peng Li ◽  
Cai-yun Yang ◽  
Rui Wang ◽  
Shuo Wang
Keyword(s):  
Path Planning ◽  
Information Entropy ◽  
High Efficiency ◽  
Dead Reckoning ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Coverage Area ◽  
High Coverage ◽  
Full Coverage ◽  
Localization And Mapping

The efficiency of exploration in an unknown scene and full coverage of the scene are essential for a robot to complete simultaneous localization and mapping actively. However, it is challenging for a robot to explore an unknown environment with high efficiency and full coverage autonomously. In this article, we propose a novel exploration path planning method based on information entropy. An information entropy map is first constructed, and its boundary features are extracted. Then a Dijkstra-based algorithm is applied to generate candidate exploration paths based on the boundary features. The dead-reckoning algorithm is used to predict the uncertainty of the robot’s pose along each candidate path. The exploration path is selected based on exploration efficiency and/or high coverage. Simulations and experiments are conducted to evaluate the proposed method’s effectiveness. The results demonstrated that the proposed method achieved not only higher exploration efficiency but also a larger coverage area.

scholarly journals A Review: Simultaneous Localization and Mapping in Application to Autonomous Robot

2018 ◽  
Author(s):  
Olusanya Agunbiade ◽  
Tranos Zuva
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Research Direction ◽  
Autonomous Robot ◽  
Unknown Environment ◽  
Computational Problem ◽  
Localization And Mapping ◽  
Open Issues

The important characteristic that could assist in autonomous navigation is the ability of a mobile robot to concurrently construct a map for an unknown environment and localize itself within the same environment. This computational problem is known as Simultaneous Localization and Mapping (SLAM). In literature, researchers have studied this approach extensively and have proposed a lot of improvement towards it. More so, we are experiencing a steady transition of this technology to industries. However, there are still setbacks limiting the full acceptance of this technology even though the research had been conducted over the last 30 years. Thus, to determine the problems facing SLAM, this paper conducted a review on various foundation and recent SLAM algorithms. Challenges and open issues alongside the research direction for this area were discussed. However, towards addressing the problem discussed, a novel SLAM technique will be proposed.

Indoor Autonomous Navigation Technology Research Based on Lidar for Quad-Copter Aerial Robot

2014 ◽  
Vol 538 ◽  
pp. 371-374
Author(s):  
Zhi Jun Bai ◽  
Yang Feng Ji ◽  
Liao Ni Wu ◽  
Qi Lin
Keyword(s):  
Navigation System ◽  
Autonomous Navigation ◽  
Simultaneous Localization And Mapping ◽  
Technology Research ◽  
Unknown Environment ◽  
Monocular Camera ◽  
Localization And Mapping ◽  
Aerial Robot ◽  
Test Result ◽  
Autonomous Navigation System

An indoor autonomous navigation system without GPS has been developed, based on the platform of quad-copter, which may fulfill the task of searching, identifying and entering the target room in a building with multi-rooms corridor. A pose sensor was utilized to stabilize the aircraft. The SLAM (Simultaneous Localization and Mapping) and plan route in unknown environment have been created by a 2D lidar. A calibrated monocular camera has been used to recognize different marks to make sure the vehicle to enter the target room. The test result showed that the indoor autonomous navigation technology based on lidar for quad-copter aerial robot is feasible and successful.

Control of quadrotor trajectory tracking with sliding mode control optimized by neural networks

2020 ◽  
Vol 234 (10) ◽  
pp. 1101-1119
Author(s):  
Somayeh Raiesdana
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Simultaneous Localization And Mapping ◽  
Indoor Navigation ◽  
Mapping Technique ◽  
Sliding Mode Controller ◽  
Mode Control ◽  
Localization And Mapping

Quadrotor or unmanned helicopter is a mobile robot which often flies in unknown environment to perform special missions. In navigational tasks, the robot is intended to fly autonomously toward a target position by following an optimum trajectory. For a successful navigation, controlled attitude, minimum position and velocity error and obstacles collision avoidance are often considered during trajectory tracking procedure. By considering environmental variabilities and due to the existence of noises, uncertainties and unpredictable factors, it is indispensable to steer and control moving robots using sophisticated autonomous algorithms. In this work, a nonlinear model of four-rotor helicopter is simulated. An optimized terminal sliding mode control is then designed to control trajectory tracking. In order to improve the time indices for sliding mode controller, this controller is modified with neural networks. The idea is to optimize the controller parameters through a network learning process which is based on the control process error. The proposed method is evaluated with simulated and real-world indoor navigation tasks. Trajectories that are tracked by quadrotor are generated by a simultaneous localization and mapping algorithm and refined with an optimization technique. A well-known simultaneous localization and mapping technique (a camera-based extended Kalman filter-simultaneous localization and mapping) is employed to generate maps, and a path planning algorithm (particle swarm optimization) is utilized to optimize a collision-free flight path using the probability-based maps generated by simultaneous localization and mapping. Simulations and experiment are done in unknown but structured indoor environments containing a number of obstacles. The steady state error, the reaching and settle time and the chattering effect are all quantified and assessed. The controlled experimental flight robustness and sensitivity are further verified for noises occurred on vision and data acquisition system. Results indicate suitable performance for the proposed neural network-sliding mode controller. Less error and more stability were achieved comparative to the conventional sliding mode controllers.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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