A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Software for Embedded Module for Image Processing

katedra kybernetik

A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics

School Logo Cleveland State University Logo Title Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

School Logo Cleveland State University Logo Title Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

HCTNav: A path planning algorithm for low-cost autonomous robot navigation in indoor environments

© 2013 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes.Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group’s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra’s algorithms show that HCTNav’s memory peak is nine times lower than Dijkstra’s in maps with more than 150,000 cells.This work has been partially supported by the Spanish “Ministerio de Ciencia e Innovación”, under project TEC2009-09871