Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach

Robotica ◽  
2019 ◽  
Vol 38 (9) ◽  
pp. 1627-1641
Author(s):  
Krishna Kant Pandey ◽  
Dayal R. Parhi

SUMMARYNavigation 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.

Author(s):  
Ian Flood ◽  
Kenneth Worley

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.


Robotica ◽  
1997 ◽  
Vol 15 (6) ◽  
pp. 627-632 ◽  
Author(s):  
Minglu Zhang ◽  
Shangxian Peng ◽  
Qinghao Meng

This paper is concerned with a mobile robot reactive navigation in an unknown cluttered environment based on neural network and fuzzy logic. Reactive navigation is a mapping between sensory data and commands without planning. This article's task is to provide a steering command letting a mobile robot avoid a collision with obstacles. In this paper, the authors explain how to perform a currently perceptual space partitioning for a mobile robot by the use of an ART neural network, and then, how to build a 3-dimensional fuzzy controller for mobile robot reactive navigation. The results presented, whether experimented or simulation, show that our method is well adapted to this type of problem.


2018 ◽  
Vol 26 (5) ◽  
pp. 842-857 ◽  
Author(s):  
Brian Matthews ◽  
Jamie Daigle ◽  
Melissa Houston

Purpose The purpose of this paper is to examine the linkages between leadership and satisfaction models with neural networks to epistemologically explore both the theoretical and practical basis of these paradigms to analyze the effect employee readiness has on job satisfaction. A review of the literature indicates an absence of a paradigmatic precursor to the satisfaction-performance dyadic. Revisiting theoretical frameworks builds a reconceptualized prism that amalgamates leadership and job satisfaction constituents to form a theoretical scaffold and linkage between employee readiness and job satisfaction. Design/methodology/approach Reviewing the literature explores a theoretical existence of a readiness model preceding the satisfaction-performance paradigm that measures how the amalgam of readiness variables affects job satisfaction. This conceived theory uses a unidirectional model that extends the linear progression and institutes a backwards propagation linkage to the satisfaction-performance linkage using the following unidirectional correlation: readiness-satisfaction→ satisfaction-performance. Using a neural network approach, a total of 160 companies are integrated into a simulation using leadership, satisfaction and readiness variables, with an emphasize on high relationship, to ascertain the effect of readiness on job satisfaction. Findings While there are studies that interchangeably link satisfaction and performance, revisiting the literature provides theoretical insight that validates the formation of a preceding construct that converges leadership and satisfaction constituencies to form a dyadic relationship between readiness and satisfaction. Research has tirelessly attempted to discover variable correlation between job performance and job satisfaction. However, these attempts are met with contradictory results. To truly link employee readiness to the job satisfaction/job performance dyad, a neural network is created, which deduces that random probabilities confirm the continuous exactitude of a positive correlation between readiness and job satisfaction. This, in turn, confirms an existent theoretical precursor to the satisfaction-performance paradigm. The implications of not linking job readiness to satisfaction and performance can potentially leave managers amiss when triangulating performance decline. Reclassifying the satisfaction-performance dyadic corroborates Judge et al.’s (2001) theory that reinventions of this impression should be researched, and Graen and Uhl-Bien’s (1991) conclusive remarks that an evaluation beyond “trait-like” individual differences of leaders is necessary to recognize the leadership paradigm loop, which is inclusive of the leader, the follower and the dyadic relationship. Originality/value This research paper is useful for practitioners and academics to refer as the comparative and intersecting explanation of leadership and job satisfaction models, as it peripherally conveys a legitimate view of a preceding relational construct that will add value to the relevance of employee readiness as it affects job satisfaction. In addition, the neural network approach is a sound and unique method to algorithmically validate the correlation between job satisfaction models and leadership. Through codifying, the environmental variables comprised Herzberg et al.’s (1959) motivation and hygiene factors that are directly related to a leader-member exchange function, an evidentiary linkage validates the literature works of Hersey and Blanchard (2001) and directly links it to job satisfaction precursors.


10.5772/46200 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
Mehmet Serdar Guzel ◽  
Robert Bicker

Autonomous robots operating in an unknown and uncertain environment must be able to cope with dynamic changes to that environment. For a mobile robot in a cluttered environment to navigate successfully to a goal while avoiding obstacles is a challenging problem. This paper presents a new behaviour-based architecture design for mapless navigation. The architecture is composed of several modules and each module generates behaviours. A novel method, inspired from a visual homing strategy, is adapted to a monocular vision-based system to overcome goal-based navigation problems. A neural network-based obstacle avoidance strategy is designed using a 2-D scanning laser. To evaluate the performance of the proposed architecture, the system has been tested using Microsoft Robotics Studio (MRS), which is a very powerful 3D simulation environment. In addition, real experiments to guide a Pioneer 3-DX mobile robot, equipped with a pan-tilt-zoom camera in a cluttered environment are presented. The analysis of the results allows us to validate the proposed behaviour-based navigation strategy.


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