Robot navigation in dynamic environments using global-appearance descriptors. State of the art

2015 ◽  
Vol 2 (1) ◽  
pp. 2
Author(s):  
Yerai Berenguer Fernández
Keyword(s):  
Mobile Robots ◽  
Visual Information ◽  
State Of The Art ◽  
Robot Navigation ◽  
Continuous Optimization ◽  
Dynamic Environments ◽  
Autonomous Mobile Robots ◽  
Map Building ◽  
Future Directions ◽  
Robot Position

Map building and localization are two impor- tant abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents a state of the art report on map building and localization using global appearance descriptors. In this approach, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance descriptor for each image. Using these descriptors, the robot is able to estimate its location in a map previously built, which is also composed of a set of global appearance descriptors. Several previous investigations that have led to the approach of this research are summarized in this paper, such as researches that compare several methods of creating global appearance descriptors. In these works we observe how the continuous optimization of the algorithms has lead to better estimations of the robot position within the environment. Finally a number of future directions in which researches are currently working are listed. 

scholarly journals CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS

2002 ◽  
Vol 16 (03) ◽  
pp. 361-374 ◽  
Author(s):  
JUAN ANDRADE-CETTO ◽  
ALBERTO SANFELIU
Keyword(s):  
Learning Rule ◽  
Dynamic Environments ◽  
Position Estimation ◽  
Indoor Environments ◽  
Map Building ◽  
Robust Learning ◽  
Map Construction ◽  
Map Updating ◽  
Dynamic Map ◽  
Robot Position

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.

Robot navigation: implications from search strategies in exploring crayfish

Robotica ◽  
2009 ◽  
Vol 28 (3) ◽  
pp. 465-475 ◽  
Author(s):  
Edith Heußlein ◽  
Blair W. Patullo ◽  
David L. Macmillan
Keyword(s):  
Mobile Robots ◽  
Computer Model ◽  
Sensory Input ◽  
Robot Navigation ◽  
Search Strategies ◽  
Search And Rescue ◽  
Autonomous Mobile Robots ◽  
Biological Knowledge ◽  
Baseline Algorithm ◽  
Navigation Strategies

SUMMARYBiomimetic applications play an important role in informing the field of robotics. One aspect is navigation – a skill automobile robots require to perform useful tasks. A sub-area of this is search strategies, e.g. for search and rescue, demining, exploring surfaces of other planets or as a default strategy when other navigation mechanisms fail. Despite that, only a few approaches have been made to transfer biological knowledge of search mechanisms on surfaces along the ground into biomimetic applications. To provide insight for robot navigation strategies, this study describes the paths a crayfish used to explore terrain. We tracked movement when different sets of sensory input were available. We then tested this algorithm with a computer model crayfish and concluded that the movement of C. destructor has a specialised walking strategy that could provide a suitable baseline algorithm for autonomous mobile robots during navigation.

Probabilistic and Count Methods in Map Building for Autonomous Mobile Robots

2000 ◽  
pp. 120-137 ◽  
Author(s):  
Miguel Rodríguez ◽  
José Correa ◽  
Roberto Iglesias ◽  
Carlos V. Regueiro ◽  
Senén Barro
Keyword(s):  
Mobile Robots ◽  
Autonomous Mobile Robots ◽  
Map Building

Mobile Robots Navigation, Mapping, and Localization Part I

2011 ◽  
pp. 1072-1079
Author(s):  
Lee Gim Hee ◽  
Marcelo H. Ang Jr.
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Material Handling ◽  
Robot Navigation ◽  
Emerging Market ◽  
Autonomous Mobile Robots ◽  
Mobile Robot Navigation ◽  
The Military ◽  
The Given ◽  
Fundamental Requirement

The development of autonomous mobile robots is continuously gaining importance particularly in the military for surveillance as well as in industry for inspection and material handling tasks. Another emerging market with enormous potential is mobile robots for entertainment. A fundamental requirement for autonomous mobile robots in most of its applications is the ability to navigate from a point of origin to a given goal. The mobile robot must be able to generate a collision-free path that connects the point of origin and the given goal. Some of the key algorithms for mobile robot navigation will be discussed in this article.

scholarly journals Deep reinforcement learning for map-less goal-driven robot navigation

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199262
Author(s):  
Matej Dobrevski ◽  
Danijel Skočaj
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Real World ◽  
Robot Navigation ◽  
Dynamic Environments ◽  
Learning Framework ◽  
Local Navigation ◽  
Real Robot ◽  
Navigation Method

Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy of such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or even impossible to use. We present a new local navigation method for steering the robot to global goals without relying on an explicit map of the environment. The proposed navigation model is trained in a deep reinforcement learning framework based on Advantage Actor–Critic method and is able to directly translate robot observations to movement commands. We evaluate and compare the proposed navigation method with standard map-based approaches on several navigation scenarios in simulation and demonstrate that our method is able to navigate the robot also without the map or when the map gets corrupted, while the standard approaches fail. We also show that our method can be directly transferred to a real robot.

scholarly journals Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

2021 ◽  
Vol 2129 (1) ◽  
pp. 012018
Author(s):  
R J Musridho ◽  
H Hasan ◽  
H Haron ◽  
D Gusman ◽  
M A Mohammad
Keyword(s):  
Mobile Robots ◽  
Convergence Analysis ◽  
Firefly Algorithm ◽  
Estimation Error ◽  
Position Estimation ◽  
Autonomous Mobile Robots ◽  
Localization And Mapping ◽  
Robot Position ◽  
Modified Firefly Algorithm ◽  
Over Time

Abstract In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

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