scholarly journals A Bionic Spatial Cognition Model and Method for Robots Based on the Hippocampus Mechanism

2022 ◽  
Vol 15 ◽  
Author(s):  
Jinsheng Yuan ◽  
Wei Guo ◽  
Fusheng Zha ◽  
Pengfei Wang ◽  
Mantian Li ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Spatial Cognition ◽  
Calculation Method ◽  
Autonomous Navigation ◽  
Path Integration ◽  
Motion Information ◽  
Robot Perception ◽  
Environmental Cognition ◽  
Biological Principle ◽  
Cognition Model

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

scholarly journals A hand-drawn map-based navigation method for mobile robots using objectness measure

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984633 ◽  
Author(s):  
Jie Niu ◽  
Kun Qian
Keyword(s):  
Mobile Robots ◽  
Autonomous Navigation ◽  
Saliency Map ◽  
Experimental Results ◽  
Environment Map ◽  
Environmental Cognition ◽  
Candidate Regions ◽  
Cognitive Environment ◽  
Candidate Object ◽  
Navigation Method

Correct cognition of the environment is the premise of mobile robots to realize autonomous navigation control tasks. The inconsistency caused by time-varying environmental information is a bottleneck for the development and application of cognitive environment technologies. In this article, we propose an environmental cognition method that uses a hand-drawn map. Firstly, we use the single skeleton refinement and fuzzy c-means algorithms to segment the image. Then, we select candidate regions combining the saliency map. At the same time, we use the superpixels straddling method to filter the windows. The final candidate object regions are obtained based on a fusion of saliency segmentation and superpixels clustering. Based on the above objectness estimation results, we use a human–computer interaction method to construct an inaccurate hand-drawn environment map for navigation. The experimental results from PASCAL VOC2007 validate the efficacy of the proposed objectness measure method, where our result of 41.2% on mean average precision is the best of the tested methods. Furthermore, the experimental results of robot navigation in the actual scene also verified the effectiveness of the proposed approach.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

scholarly journals Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 954
Author(s):  
Abhijeet Ravankar ◽  
Ankit A. Ravankar ◽  
Arpit Rawankar ◽  
Yohei Hoshino
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Collision Avoidance ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Robot Navigation ◽  
Indoor Environments ◽  
Safe Distance ◽  
Field Robots ◽  
Safe Navigation

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

scholarly journals Path integration in place cells of developing rats

2018 ◽  
Vol 115 (7) ◽  
pp. E1637-E1646 ◽  
Author(s):  
Tale L. Bjerknes ◽  
Nenitha C. Dagslott ◽  
Edvard I. Moser ◽  
May-Britt Moser
Keyword(s):  
Path Integration ◽  
Grid Cell ◽  
Cell System ◽  
Place Cells ◽  
Postnatal Life ◽  
Motion Information ◽  
Grid Cells ◽  
Adult Rats ◽  
Self Motion ◽  
Made In

Place cells in the hippocampus and grid cells in the medial entorhinal cortex rely on self-motion information and path integration for spatially confined firing. Place cells can be observed in young rats as soon as they leave their nest at around 2.5 wk of postnatal life. In contrast, the regularly spaced firing of grid cells develops only after weaning, during the fourth week. In the present study, we sought to determine whether place cells are able to integrate self-motion information before maturation of the grid-cell system. Place cells were recorded on a 200-cm linear track while preweaning, postweaning, and adult rats ran on successive trials from a start wall to a box at the end of a linear track. The position of the start wall was altered in the middle of the trial sequence. When recordings were made in complete darkness, place cells maintained fields at a fixed distance from the start wall regardless of the age of the animal. When lights were on, place fields were determined primarily by external landmarks, except at the very beginning of the track. This shift was observed in both young and adult animals. The results suggest that preweaning rats are able to calculate distances based on information from self-motion before the grid-cell system has matured to its full extent.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chittaranjan Paital ◽  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Prasant Ranjan Dhal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Simulation Software ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

Autonomous Navigation of Mobile Robots in Vineyard

2021 ◽  
Vol 2021 (0) ◽  
pp. 1P2-A15
Author(s):  
Abhijeet RAVANKAR ◽  
Ankit A. RAVANKAR ◽  
Yohei HOSHINO
Keyword(s):  
Mobile Robots ◽  
Autonomous Navigation

scholarly journals Reconstruction of Spatial Cognition from Other's View and Motion Information

i-Perception ◽  
10.1068/ic857 ◽  
2011 ◽  
Vol 2 (8) ◽  
pp. 857-857
Author(s):  
Kyo Hattori ◽  
Daisuke Kondo ◽  
Yuki Hashimoto ◽  
Tomoko Yonemura ◽  
Hiroyuki Iizuka ◽  
...  
Keyword(s):  
Spatial Cognition ◽  
Motion Information

Robustness and Controllability Analysis for Autonomous Navigation of Two-Wheeled Mobile Robots

2007 ◽  
Author(s):  
Alessio Salerno ◽  
Jorge Angeles
Keyword(s):  
Mobile Robots ◽  
Autonomous Navigation ◽  
Autonomous Robots ◽  
Upper Bounds ◽  
Small Time ◽  
Dynamics Model ◽  
Wheeled Mobile Robots ◽  
Rank Condition ◽  
Dynamics Response ◽  
Kalman Rank Condition

This work deals with the robustness and controllability analysis for autonomous navigation of two-wheeled mobile robots. The analysis of controllability of the systems at hand is conducted using both the Kalman rank condition for controllability and the Lie Algebra rank condition. We show that the robots targeted in this work can be controlled using a model for autonomous navigation by means of their dynamics model: kinematics will not be sufficient to completely control these underactuated systems. After having proven that these autonomous robots are small-time locally controllable from every equilibrium point and locally accessible from the remaining points, the uncertainty is modeled resorting to a multiplicative approach. The dynamics response of these robots is analyzed in the frequency domain. Upper bounds for the complex uncertainty are established.

Sign in / Sign up

Export Citation Format

Share Document