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The quadrotor often flies in an environment where external disturbances can occur and decrease its tracking performances. Hence, the robustness and effectiveness of nonlinear controllers are required to reject the external disturbance. The aim of this article is to design and verify an integral backstepping and sliding mode control techniques to track the desired trajectory for the quadrotor. The performance and effectiveness of the nonlinear controllers are illustrated through representative simulations showing the tracking of several reference trajectories with disturbances, model uncertainties, and wind gusts effect. To this end, the Dryden turbulence model for wind velocity fluctuations was used as a benchmark wind model to better represent the impact of the accuracy of the wind field model on the trajectory tracking performance. The tracking performances of these approaches are discussed using the root mean square error (RMSE).

Localizing the mobile robot in an indoor environment is one of the problems encountered repeatedly. Achieving the target precisely in any environment is not an easy task since there are noises and obstacles in the surrounding environment. Therefore, filtering the signals to reduce noises is essential for more accurate and precise motion. In this paper, we selected the extended Kalman filter, which is used for non-linear models’ signals to predict the coordinates of a wheeled mobile robot. We tested the efficiency of this filter under three noise cases: no noise, Gaussian noise and non-Gaussian noise using MATLAB software.

It is essential to learn a robot navigation environment. We describe research outcomes for KSU-IMR mapping and intelligence. This is for navigating and robot behavior learning. The mobile maps learning and intelligence was based on hybrid paradigms and AI functionaries. Intelligence was based on ANN-PCA for dimensionality reduction, and Neuro-Fuzzy architecture.

Robot navigation and obstacle avoidance using fuzzy logic controller is presented.Soft computing techniques are used to optimize the performance of fuzzy logic.The automatic tuning was done by using three soft computing techniques: GA, PSO, and NN.The best performance in terms of travelling time and speed is based on GA-Fuzzy.The PSO-Fuzzy and Neuro-Fuzzy methods have better performance in terms of distance travelled. An autonomous mobile robot operating in an unstructured environment must be able to deal with dynamic changes of the environment. Navigation and control of a mobile robot in an unstructured environment are one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, fuzzy logic controller is optimized by integrating fuzzy logic with other soft computing techniques like genetic algorithm, neural networks, and Particle Swarm Optimization (PSO). Soft computing techniques are used in this work to tune the membership function parameters of fuzzy logic controller to improve the navigation performance. Four methods have been designed and implemented: manually constructed fuzzy logic (M-Fuzzy), fuzzy logic with genetic algorithm (GA-Fuzzy), fuzzy logic with neural network (Neuro-Fuzzy), and fuzzy logic with PSO (PSO-Fuzzy). The performances of these approaches are compared through computer simulations and experiment number of scenarios using Khepera III mobile robot platform. Hybrid fuzzy logic controls with soft computing techniques are found to be most efficient for mobile robot navigation. The GA-Fuzzy technique is found to perform better than the other techniques in most of the test scenarios in terms of travelling time and average speed. The performances of both PSO-Fuzzy and Neuro-Fuzzy are found to be better than the other methods in terms of distance travelled. In terms of bending energy, the PSO-Fuzzy and Neuro-Fuzzy are found to be better in simulation results. Although, the M-Fuzzy is found to be better using real experimental results. Hence, the most important system parameter will dictate which of the four methods to use.

The presented approach was focused around building intelligence for mobile robot navigation. That was achieved by creating navigation intelligence capabilities while the robot is in motion. The adopted learning paradigm was a five layers Neuro-Fuzzy (NF) learning architecture, due to ability to create and inference for enhanced navigation. To meet such visual data gathering, the mobile robot platform have fully computer-interfaced stereo vision, and reliable 3D perception. Mobile robot intelligence (NF), hence learns navigation (SLAM) maps visual features, as it travels within spaces. Blinding intelligence with visual maps has resulted in better navigation capabilities.

Navigation and obstacle avoidance in an unknown environment is proposed in this paper using hybrid neural network with fuzzy logic controller. The overall system is termed as Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS combines the benefits of fuzzy logic and neural networks for the purpose of achieving robotic navigation task. Simulation results are presented using Khepera Simulator (KiKs) within MATLAB environment. Moreover, experimental results are obtained using Khepera III platform.

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.

This manuscript presents an autonomous navigation of a mobile robot using SLAM, while relying on an active stereo vision. We show a framework of low-level software coding which is necessary when the vision is used for multiple purposes such as obstacle discovery. The built system incorporated a number of SLAM based routines while replying on stereo vision mechanism. The system was implemented and tested on a mobile robot platform, and perform an experiment of autonomous navigation in an indoor environment.

An autonomous mobile robot operating in an unstructured environment must be able to learn with dynamic changes to that environment. Learning navigation and control of mobile robot in an unstructured environment is one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, we optimized a performance of fuzzy logic controller by evolutionary learning technique. Two proposed approaches have been designed and implemented: Fuzzy Logic Controller (FLC) and Genetic-Fuzzy Controller (GA-FLC). The Genetic Algorithm is used for automatically learning to tune the membership function parameters for mobile robot motion control. Moreover, the performance of these approaches are compared through simulation.

This manuscript looks into details into the hardware implementations of an active stereo vision path planning navigation for a mobile robotics system. This mobile robotics system has been designated as the (KSU-IMR), as it a test-bed for research within the area of mobile robotics system navigation through the use of an active stereo vision for planning a path within an unstructured environment at King Saud University in KSA. The system is making use of fast image processing tools, as in addition to fast algorithms for processing the dynamic images. The approach followed here is to employ the Epipolar Geometry to compute the 3-D details of the environment where the mobile is moving. The algorithm mixes stereo vision, occupancy grid mapping, and potential field path planning procedures to form a robu

D* based navigation algorithms provide robust and realtime means of achieving path planning in dynamic environments. Author of this paper introduces a notion of predictable time-based obstacles. The algorithm proposed in the paper defines a centralized obstacle-map that is shared among multiple agents (robots) performing path planning. Each robot plans its path individually on an obstacle-map using a slightly modified version of D* Lite and then shares an updated version of the map, which includes its planned path as a new obstacle, with its peers. The planned paths appear as temporary time-based obstacles to peer robots. Planned paths are divided into discrete temporal sections so as to help peer robots optimize paths temporally. The proposed algorithm also presents a priority measure which helps us decide the optimized sequence of individual pathplanning order followed by cooperating robots. Since the implemented algorithm is tested in simulation using Mobile robot Programming Toolkit, the Real–time performance analysis is done to confirm the real-time execution time of the proposed algorithm. Keywordsrobotics; path-planning; D*; navigation; multi