Intelligent path planning algorithm for autonomous robot based on recurrent neural networks

Brahmi, Hajer; Ammar, Boudour; Alimi, Adel M.

doi:10.1109/icadlt.2013.6568459

Cited by 18 publications

(3 citation statements)

References 12 publications

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“…Robot navigation is one of the most important functions for human-symbiotic autonomous mobile robots that are expected to provide various services such as logistics [1], housework for elderly or handicap people [2,3], personal assistance in an office [4], and attending to a person in a museum or airport lobby [5]. Such mobile robots require the ability to safely move with reasonable efficiency in arbitrary human-coexisting environments, which include relatively open environments, e.g., a city squire (Fig.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Kamezaki

Kobayashi

Yokoyama

et al. 2019

Int J of Soc Robotics

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Human-aware navigation is an essential requirement for autonomous robots in human-coexisting environments. The goal of conventional navigation is to find a path for a robot to pass through safely and efficiently without colliding with human. Note that if such a path cannot be found, the robot stops until a path is clear. Thus, such collision-avoidance based passive navigation does not work in a congested or narrow space. To avoid this freezing problem, the robot should induce humans to make a space for passing by an adequate inducement method, such as body movement, speech, and touch, depending on the situation. A robot that deliberately clears a path with such actions may make humans uncomfortable, so the robot should also utilize inducements to avoid causing negative feelings. In this study, we propose a fundamental framework of interactive navigation with situation-adaptive multimodal inducement. For a preliminary study, we target a passing scenario in a narrow corridor where two humans are standing and adopt a model-based approach focusing on common parameters. The suitable inducement basically varies depending on the largest space through which a robot can pass, distance between the robot and a human, and human behavior such as conversing. We thus develop a situation-adaptive inducement selector on the basis of the relationship between human-robot proximity and allowable inducement strength, considering robot efficiency and human psychology. The proposed interactive navigation system was tested across some contextual scenarios and compared with a fundamental path planner. The experimental results indicated that the proposed system solved freezing problems, provided a safe and efficient trajectory, and improved humans' psychological reaction although the evidence was limited to robot planner and hardware design we used as well as certain scenes, contexts, and participants.

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Section: Introductionmentioning

confidence: 99%

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Kamezaki

Kobayashi

Yokoyama

et al. 2019

Int J of Soc Robotics

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“…Two series RNNs have been used for the robot navigation task in an unknown flat environment. 25 One of them has solved the localization problem and another has been considered for path planning. The robot was equipped with three infrared sensors to detect obstacles.…”

Section: Introductionmentioning

confidence: 99%

Combination of Recurrent Neural Network and Deep Learning for Robot Navigation Task in Off-Road Environment

2019

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SUMMARYThis paper tackles the challenge of the necessity of using the sequence of past environment states as the controller’s inputs in a vision-based robot navigation task. In this task, a robot has to follow a given trajectory without falling in pits and missing its balance in uneven terrain, when the only sensory input is the raw image captured by a camera. The robot should distinguish big pits from small holes to decide between avoiding and passing over. In non-Markov processes such as the abovementioned task, the decision is done using past sensory data to ensure admissible performance. Applying images as sensory inputs naturally causes the curse of dimensionality difficulty. On the other hand, using sequences of past images intensifies this difficulty. In this paper, a new framework called recurrent deep learning (RDL) with combination of deep learning (DL) and recurrent neural network is proposed to cope with the above challenge. At first, the proper features are extracted from the raw image using DL. Then, these represented features plus some expert-defined features are used as the inputs of a fully connected recurrent network (as target network) to generate command control of the robot. To evaluate the proposed RDL framework, some experiments are established on WEBOTS and MATLAB co-simulation platform. The simulation results demonstrate the proposed framework outperforms the conventional controller based on DL for the navigation task in the uneven terrains.

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“…Chohra et al [67] have designed intelligent autonomous navigation structure for a vehicle using multi-layered neural networks (NN). Brahmi et al [68] have solved the path planning and localization problem of mobile robot using recurrent neural network (RNN). This RNN allows the robot to navigate autonomously in the unknown environments.…”

Section: Hybridization Of Neural Network and Nondeterministic Algorithmmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

184

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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Intelligent path planning algorithm for autonomous robot based on recurrent neural networks

Cited by 18 publications

References 12 publications

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

A Preliminary Study of Interactive Navigation Framework with Situation-Adaptive Multimodal Inducement: Pass-By Scenario

Combination of Recurrent Neural Network and Deep Learning for Robot Navigation Task in Off-Road Environment

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

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