Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

Fuzzy Mobile-Robot Positioning in Intelligent Spaces Using Wireless Sensor Networks

This work presents the development and experimental evaluation of a method based on fuzzy logic to locate mobile robots in an Intelligent Space using Wireless Sensor Networks (WSNs). The problem consists of locating a mobile node using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. The sensor model of these measurements is very noisy and unreliable. The proposed method makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the proposed approach is compared with a probabilistic technique showing that the fuzzy approach is able to handle highly uncertain situations that are difficult to manage by well-known localization methods

Obstacle Avoidance and Proscriptive Bayesian Programming

Unexpected events and not modeled properties of the robot environment are some of the challenges presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a probabilistic approach called Bayesian Programming, which aims to deal with the uncertainty, imprecision and incompleteness of the information handled to solve the obstacle avoidance problem. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. A video illustration of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac

ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains

Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor-intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real-world conditions remains a challenge. We developed ConservationBots-small aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times -- significant for energy-limited aerial systems while avoiding close encounters with potential, counter-productive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: i) planning to determine both trajectory and measurement actions guided by an information-theoretic objective, which allows the robot to strategically select near-instantaneous range-only measurements to achieve faster localization, and time-consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; ii) a bearing detector more robust to noise and iii) a tracking algorithm formulation robust to missed and false detections experienced in real-world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high-resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method.Comment: 33 pages, 21 figure

A Multiagent Approach to Qualitative Navigation in Robotics

Navigation in unknown unstructured environments is still a difficult open problem in the field of robotics. In this PhD thesis we present a novel approach for robot navigation based on the combination of landmark-based navigation, fuzzy distances and angles representation and multiagent coordination based on a bidding mechanism. The objective has been to have a robust navigation system with orientation sense for unstructured environments using visual information. To achieve such objective we have focused our efforts on two main threads: navigation and mapping methods, and control architectures for autonomous robots. Regarding the navigation and mapping task, we have extended the work presented by Prescott, so that it can be used with fuzzy information about the locations of landmarks in the environment. Together with this extension, we have also developed methods to compute diverting targets, needed by the robot when it gets blocked. Regarding the control architecture, we have proposed a general architecture that uses a bidding mechanism to coordinate a group of systems that control the robot. This mechanism can be used at different levels of the control architecture. In our case, we have used it to coordinate the three systems of the robot (Navigation, Pilot and Vision systems) and also to coordinate the agents that compose the Navigation system itself. Using this bidding mechanism the action actually being executed by the robot is the most valued one at each point in time, so, given that the agents bid rationally, the dynamics of the biddings would lead the robot to execute the necessary actions in order to reach a given target. The advantage of using such mechanism is that there is no need to create a hierarchy, such in the subsumption architecture, but it is dynamically changing depending on the specific situation of the robot and the characteristics of the environment. We have obtained successful results, both on simulation and on real experimentation, showing that the mapping system is capable of building a map of an unknown environment and use this information to move the robot from a starting point to a given target. The experimentation also showed that the bidding mechanism we designed for controlling the robot produces the overall behavior of executing the proper action at each moment in order to reach the target

Implementation Of Various Types Of Fuzzy Controls On A Mobile Robot Using Sonar Sensors [QA9.64. N438 2008 f rb].

Dalam penyelidikan ini, sebuah robot bergerak telah digunakan untuk mengimplementasikan kawalan fuzzy jenis ‘Non-Singleton Type-2’ untuk kawalan pergerakan pengemudian dan tepian dinding dengan bantuan penderia sonar. In this work, non-singleton type-2 fuzzy control has been implemented on a mobile robot for steering and sidewall movement control with the aid of ultrasonic sensors to compare its performances with the singleton type-2 and type-1 fuzzy control

Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

The ability to acquire a representation of spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal formation is believed to play a key role in spatial learning and navigation in animals. This paper briefly reviews the relevant neurobiological and cognitive data and their relation to computational models of spatial learning and localization used in mobile robots. It also describes a hippocampal model of spatial learning and navigation and analyzes it using Kalman filter based tools for information fusion from multiple uncertain sources. The resulting model allows a robot to learn a place-based, metric representation of space in a-priori unknown environments and to localize itself in a stochastically optimal manner. The paper also describes an algorithmic implementation of the model and results of several experiments that demonstrate its capabilities

A New Collaborative Knowledge-Based Approach for Wireless Sensor Networks

This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Nowadays, there is a growing interest in the integration of Soft Computing technologies into Wireless Sensor Networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks. The objective of this work is to design a collaborative knowledge-based network, in which each sensor executes an adapted Fuzzy Rule-Based System, which presents significant advantages such as: experts can define interpretable knowledge with uncertainty and imprecision, collaborative knowledge can be separated from control or modeling knowledge and the collaborative approach may support neighbor sensor failures and communication errors. As a real-world application of this approach, we demonstrate a collaborative modeling system for pests, in which an alarm about the development of olive tree fly is inferred. The results show that knowledge-based sensors are suitable for a wide range of applications and that the behavior of a knowledge-based sensor may be modified by inferences and knowledge of neighbor sensors in order to obtain a more accurate and reliable output

Vision based localization of mobile robots

Mobile robotics is an active and exciting sub-field of Computer Science. Its importance is easily witnessed in a variety of undertakings from DARPA\u27s Grand Challenge to NASA\u27s Mars exploration program. The field is relatively young, and still many challenges face roboticists across the board. One important area of research is localization, which concerns itself with granting a robot the ability to discover and continually update an internal representation of its position. Vision based sensor systems have been investigated [8,22,27], but to much lesser extent than other popular techniques [4,6,7,9,10]. A custom mobile platform has been constructed on top of which a monocular vision based localization system has been implemented. The rigorous gathering of empirical data across a large group of parameters germane to the problem has led to various findings about monocular vision based localization and the fitness of the custom robot platform. The localization component is based on a probabilistic technique called Monte-Carlo Localization (MCL) that tolerates a variety of different sensors and effectors, and has further proven to be adept at localization in diverse circumstances. Both a motion model and sensor model that drive the particle filter at the algorithm\u27s core have been carefully derived. The sensor model employs a simple correlation process that leverages color histograms and edge detection to filter robot pose estimations via the on board vision. This algorithm relies on image matching to tune position estimates based on a priori knowledge of its environment in the form of a feature library. It is believed that leveraging different computationally inexpensive features can lead to efficient and robust localization with MCL. The central goal of this thesis is to implement and arrive at such a conclusion through the gathering of empirical data. Section 1 presents a brief introduction to mobile robot localization and robot architectures, while section 2 covers MCL itself in more depth. Section 3 elaborates on the localization strategy, modeling and implementation that forms the basis of the trials that are presented toward the end of that section. Section 4 presents a revised implementation that attempts to address shortcomings identified during localization trials. Finally in section 5, conclusions are drawn about the effectiveness of the localization implementation and a path to improved localization with monocular vision is posited