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An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

Mobile robots have wide applications in challenging real-world scenarios. Therefore, it is necessary to have an advanced controller to control the robotic systems smoothly. An artificial bee colony optimization algorithm and recurrent neural network are combined to develop a hybrid controller and implemented for multi-robotic navigational problems in unknown static and dynamic environments. The designed controller is validated through MATLAB simulations coupled with real-time experiments. Results obtained via both the testing platforms are analysed, and found a good agreement between them as the deviation is less than 5.5%. Further, the developed controller is compared with existing controllers, and improvements of 20%, 10.19%, 13.53% is noted in terms of path length. [ABSTRACT FROM AUTHOR]

Humanoid robots, with their overall resemblance to a human body, is modeled for flawless interaction with human-made tools or the environment. In this study, navigation of humanoid robot using hybrid Artificial potential field (APF) and Moth flame optimization (MFO) approach have been performed. The hybrid approach provides the final turning angle (FTA), which is optimum to avoid collision with the hindrances. APF utilizes a negative potential field and a positive potential field to find the location of obstacles and target, respectively. The navigation starts towards the target; when the robot interacts with the obstacle, APF provides an intermediate angle (IA). The IA, along with the position of the obstacle, is fed into MFO as an input. This technique provides the FTA (optimum) to avoid collisions and guide a robot to the target. It is implemented in a single humanoid system and a multi-humanoid system. The presence of multiple humanoids can create the chance of inter-collision. It is dismissed by employing a dining philosopher controller to the proposed technique. Simulations and experiments are accomplished on simulated and real humanoid NAO. The coherency in the behavior of the results evaluated by the simulations and real-time experiments demonstrates the efficiency of the proposed AI technique. Comparisons are performed with a previously used method to validate the robustness of the technique. [ABSTRACT FROM AUTHOR]

SUMMARY: Navigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u(o), whereas obstacle angle u(n) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time. [ABSTRACT FROM AUTHOR]

This study proposes the design of two efficient nature inspired intelligent optimal controllers, flower pollination algorithm (FPA) and bat algorithm (BA) for obtaining optimal path using an autonomous mobile robot in an unknown environment. FPA is based on the pollination process of flowering plants, which transfer pollens by using different pollinators. On the contrary, BA depends on echolocation and frequency tuning to solve different types of optimisation problems in engineering. To accomplish the path‐planning task of mobile robot autonomously, a fitness function has been introduced considering the distance between robot‐obstacle and robot‐goal to satisfy the conditions of obstacle avoidance and goal reaching behaviour of robot. Based on the values of objective function of the algorithms, mobile robot avoids obstacles in the unknown environment and moves towards the goal. In this work, the efficiency of such controllers is verified using some simulations in MATLAB environment. Further, an experimental work is carried out in real‐world environment using ARDUINO Mega 2560 microcontroller to ascertain the path length, travelling time and convergence speed of the two algorithms. [ABSTRACT FROM AUTHOR]

This article focuses on the navigational control of underwater mobile robot. Differential evolution approach has been used to navigate the underwater robot from source to destination while avoiding various types of obstacles. Differential evolution algorithm has been employed to find out the robot’s global best pose among a set of possible solutions based on the fitness value with respect to the current sensory data about obstacles and target. Such evolutionary computation scheme can provide desirable convergence, diversity and also robustness depending on proper selection and adaptive tuning of parameters. Self-learning ability of the parameters in the path planning algorithm is crucial to deal with nonlinearities and ambiguities of hydrodynamics as created by high-frequency oscillations during underwater motion. A sequence of intermediate positions chosen by proposed dynamic differential evolution algorithm between start and goal points can be defined as a near-optimum path for underwater robot. During navigation of the robot, the path smoothness and clearance from obstacles and computational time are also considered for performance evaluation of implemented algorithm. The feasibility of the proposed underwater motion planning approach has been authenticated through the simulation and experimental results. [ABSTRACT FROM AUTHOR]

The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller. [ABSTRACT FROM AUTHOR]

Abstract: Optimal path in an environment containing obstacles for underwater vehicle can be computed using a numerical solution of the nonlinear optimal control problem (NOCP). The underwater vehicle is modelled with six-dimensional nonlinear and coupled equations of motion, controlled by DC motors in all degrees of freedom. The intent of this computation is to offer a comprehensive perception of the behaviour of underwater autonomous vehicle and also to obtain the unknown parameters of the model which can be devoted in motion planning strategy of underwater robot. To execute tasks along a distinctive path in a convoluted environment, motion planning necessities to concede the underwater robot to be in motion between its current and final configurations without any collision within the encircling environment. Traditional optimization methods are not very effective to it, which are easy to plunge into local minimum. The optimization of path as well as time taken has been analysed here using modified shuffled frog leaping (SFL) optimization algorithm based on perception, cognition and sensor fusion. Path scheduling has to be executed for achieving integration of different preliminary robotic behaviours (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking) in partially unknown territory (land or water). The optimal path is generated with this method when the robot reaches its target. The simulation studies ensure that the heuristic navigational approach possesses intelligent decision-making capabilities in negotiating hazardous terrain conditions during the under-water robot motion. [Copyright &y& Elsevier]

The development of robotics research toward industrialization has created an enhanced demand for modernization of the automation industry. Humanoid robots being advanced than other forms of robots bear a large resemblance to humans and are very much helpful in replacing humans in tedious and repetitive tasks. Therefore, the navigation and path planning of the humanoids bear a large importance in robotics research. The current investigation deals with the path planning of NAO humanoid robots. In the present work, a classical method of regression analysis and an artificial intelligence technique of fuzzy logic are implemented separately for the purpose of obstacle avoidance during the motion of humanoid NAOs toward respective targets. The simulation analysis of the proposed techniques is carried out using V‐REP software. The experiments are performed in laboratory conditions with a proper environment for working of the humanoid NAOs. Finally, a comparison has been made between the simulation and experimental results. The results obtained from the simulation and experimental analyses are in good agreement with each other, which suggest that the proposed methodologies can be used as methods of robust control for the navigation of humanoids. The current investigation deals with the navigational analysis of humanoids using regression and fuzzy logic as the intelligent algorithms. Here, the proposed methodologies have been applied on both single as well as multiple humanoids on simulation and experimental platforms. Finally, the results obtained from both the platforms are compared against each other with good agreement. [ABSTRACT FROM AUTHOR]