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Book ChapterDOI

Global Path Optimization of Humanoid NAO in Static Environment Using Prim’s Algorithm

TL;DR: In this paper, a minimum spanning tree (MST) method with greedy approach which uses the concept of sets was used to navigate a humanoid robot cluttered with obstacles, avoiding collisions in static environment using Prim's algorithm.
Abstract: This paper focuses on navigation of a humanoid robot cluttered with obstacles, avoiding collisions in static environment using Prim’s algorithm. Prim’s algorithm is a minimum spanning tree (MST) method with greedy approach which uses the concept of sets. It generates the MST by selecting least weights from the weighted graph and randomly forms disjoint sets with picking one least weight edge from the ones remaining for creating node incident to form the tree. Similar approach repeats for selecting all ‘n – 1’ edges to the tree which is the path direction to humanoid NAO. The developed algorithm is implemented in both simulation and experimental platforms to obtain the navigational results. The simulation and experimental navigational results confirm the efficiency of the path planning strategy as the percentage of deviations of navigational parameters is below 6%.
Citations
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Journal ArticleDOI
02 Jun 2022-Robotica
TL;DR: In this paper , the krill herd optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments.
Abstract: Abstract Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

4 citations

Journal ArticleDOI
02 Jun 2022-Robotica
TL;DR: The krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments.
Abstract: Abstract Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

3 citations

Journal ArticleDOI
TL;DR: In this paper , an end-to-end strategy is proposed to solve a maze in an autonomous way, by using computer vision and path planning, and this robot shares the generated knowledge to another robot by means of communication protocols.
Abstract: Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub, airports, or for evacuation of disaster zones. Many methodologies have been explored to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have become more popular in this kind of scenarios, given their robustness and scalability. In this paper, we implement an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be capable of solving the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we showcase some initial experimentation in a pair of robots with different mechanical characteristics. Further implementations of this work include communicating the robots for other tasks, such as teaching assistance, remote classes, and other innovations in higher education.

1 citations

Book ChapterDOI
01 Jan 2022
TL;DR: In this paper , a self-organizing migrating algorithm (SOMA) is used to plan optimal paths for many mobile robots in both static and dynamic environments, and the results have been validated in an experimental platform with real Khepera III robots under laboratory conditions.
Abstract: Science and technology have progressed in recent years as robots gained their popularity in industrial applications with real-time scenarios. The effective and efficient use of robots in real-time applications become a challenging task for the researchers. Use of intelligent algorithms for trajectory generation with proper motion planning while performing required task is required criterion for robotic agents. The Self-Organizing Migrating algorithm (SOMA) is used in this study to plan optimal paths for many mobile robots in both static and dynamic environments. This technique was simulated in V-REP simulator, and the outcomes have been validated in an experimental platform with real Khepera III robots under laboratory conditions. The simulation and experimental outcomes with very less navigational parameter deviation depict the effectiveness of the implemented intelligent path planning algorithm.

1 citations

References
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Journal ArticleDOI
TL;DR: This work proposes that the robot should use the model’s current predictive accuracy to inform the degree of confidence in its future predictions, and generates probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly.
Abstract: One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about...

88 citations

Journal ArticleDOI
TL;DR: The results show that the proposed hybrid path planning method, used for global path planning, tracking and obstacles avoidance, can meet the application needs of mobile robots in complex dynamic environments.
Abstract: When mobile robot used in large-scale dynamic environments, it face more challenging problems in real-time path planning and collision-free path tracking. This paper presents a new hybrid path planning method that combines A* algorithm with adaptive window approach to conduct global path planning, real-time tracking and obstacles avoidance for mobile robot in large-scale dynamic environments. Firstly, a safe A* algorithm is designed to simplify the calculation of risk cost function and distance cost. Secondly, key path points are extracted from the planned path which generated by the safe A* to reduce the number of the grid nodes for smooth path tracking. Finally, the real-time motion planning based on adaptive window approach is adopted to achieve the simultaneous path tracking and obstacle avoidance (SPTaOA) together the switching of the key path points. The simulation and practical experiments are conducted to verify the feasibility and performance of the proposed method. The results show that the proposed hybrid path planning method, used for global path planning, tracking and obstacles avoidance, can meet the application needs of mobile robots in complex dynamic environments.

81 citations

Journal ArticleDOI
TL;DR: The results, obtained from both simulation and real-world experiment, confirm the superiority of the proposed QFA over other contender algorithms in terms of solution quality as well as run-time complexity.
Abstract: Over the past few decades, Firefly Algorithm (FA) has attracted the attention of many researchers by virtue of its capability of solving complex real-world optimization problems. The only factor restricting the efficiency of this FA algorithm is the need of having balanced exploration and exploitation while searching for the global optima in the search-space. This balance can be established by tuning the two inherent control parameters of FA. One is the randomization parameter and another is light absorption coefficient, over iterations, either experimentally or by an automatic adaptive strategy. This paper aims at the later by proposing an improvised FA which involves the Q-learning framework within itself. In this proposed Q-learning induced FA (QFA), the optimal parameter values for each firefly of a population are learnt by the Q-learning strategy during the learning phase and applied thereafter during execution. The proposed algorithm has been simulated on fifteen benchmark functions suggested in the CEC 2015 competition. In addition, the proposed algorithm's superiority is tested by conducting the Friedman test, Iman–Davenport and Bonferroni Dunn test. Moreover, its suitability for application in real-world constrained environments has been examined by employing the algorithm in the path planning of a robotic manipulator amidst various obstacles. To avoid obstacles one mechanism is designed for the robot-arm. The results, obtained from both simulation and real-world experiment, confirm the superiority of the proposed QFA over other contender algorithms in terms of solution quality as well as run-time complexity.

62 citations

Journal ArticleDOI
TL;DR: A simple, short and efficient chaotic path planning algorithm for autonomous mobile robots, with the aim of covering a given terrain using chaotic, unpredictable motion, which shows a fast and efficient scanning of the given area.

47 citations

Journal ArticleDOI
TL;DR: The experiments show that the proposed algorithm can successfully identify and avoid moving and static obstacles with different quantities in the environment, and realize the autonomous navigation of the robot in a complex environment.
Abstract: Autonomous navigation in complex environment is an important requirement for the design of a robot. Active SLAM (simultaneous localization and mapping) combining, which combine path planning with SLAM, is proposed to improve the ability of autonomous navigation in complex environment. In this paper, fully convolutional residual networks are used to recognize the obstacles to get depth image. The avoidance obstacle path is planned by Dueling DQN algorithm in the robot’s navigation; at the same time, the 2D map of the environment is built based on FastSLAM. The experiments show that the proposed algorithm can successfully identify and avoid moving and static obstacles with different quantities in the environment, and realize the autonomous navigation of the robot in a complex environment.

46 citations