Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

Autonomna navigacija za invalidska kolica s detekcijom prepreka u stvarnom vremenu korištenjem 3D senzora

Autonomous wheelchairs operating in dynamic environments need to sense its surrounding environment and adapt the control signal, in real-time, to avoid collisions and protect the user. In this paper we propose a robust, simple and real-time autonomous navigation module that drives a wheelchair toward a desired target, along with its capability to avoid obstacles in a 3D dynamic environment. To command the mobile robot to the target, we use a Fuzzy Logic Controller (FLC). For obstacle avoidance, we use the Kinect Xbox 360 to provide an actual map of the environment. The generated map is fed to the reactive obstacle avoidance control Deformable Virtual Zone (DVZ). Simulations and real world experiments results are reported to show the feasibility and the performance of the proposed control system.Autonomna invalidska kolica koja se kreću u dinamičkim okruženjima moraju biti sposobna detektirati prepreke u svojoj okolini, te prilagoditi upravljački signal u stvarnom vremenu kako bi se izbjegli sudari i zaštitio korisnik. U ovom radu predlaže se jednostavan, robustan modul za autonomnu navigaciju u stvarnom vremenu koji vodi invalidska kolica prema željenom odredištu, te omogućuje izbjegavanje prepreka u 3D okruženju. Za upravljanje koristi se regulator baziran na neizravnoj logici (FLC). Za izbjegavanje prepreka koristi se Kinect Xbox 360 senzor koji gradi kartu okoline. Generirana karta se predaje reaktivnoj kontroli za izbjegavanje prepreka Deformiranoj Virutalnoj Zoni (DVZ). Prikazani su rezultati simulacija i eksperimenata u stvarnom svijetu kako bi se pokazala izvedivost i kvaliteta izvođenja predloženog sustava upravljanja

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

Achieving intelligence in mobility

This paper presents an integrated approach to the application of machine learning tasks that can be observed throughout a number of typical applications of mobile robots and puts those tasks into persepective with respect to both existing and newly developed learning techniques. The actual realization of the approach has been carried out on the two mobile robots PRIAMOS and TESEO, which are both operating in a real office environment. In this context, several experimental results are presented. This paper appeared in: IEEE-Expert: Special Track on Intelligent Robotic Systems, Vol. 10, No. 2, April 1995

Autonomous navigation of a wheeled mobile robot in farm settings

This research is mainly about autonomously navigation of an agricultural wheeled mobile robot in an unstructured outdoor setting. This project has four distinct phases defined as: (i) Navigation and control of a wheeled mobile robot for a point-to-point motion. (ii) Navigation and control of a wheeled mobile robot in following a given path (path following problem). (iii) Navigation and control of a mobile robot, keeping a constant proximity distance with the given paths or plant rows (proximity-following). (iv) Navigation of the mobile robot in rut following in farm fields. A rut is a long deep track formed by the repeated passage of wheeled vehicles in soft terrains such as mud, sand, and snow. To develop reliable navigation approaches to fulfill each part of this project, three main steps are accomplished: literature review, modeling and computer simulation of wheeled mobile robots, and actual experimental tests in outdoor settings. First, point-to-point motion planning of a mobile robot is studied; a fuzzy-logic based (FLB) approach is proposed for real-time autonomous path planning of the robot in unstructured environment. Simulation and experimental evaluations shows that FLB approach is able to cope with different dynamic and unforeseen situations by tuning a safety margin. Comparison of FLB results with vector field histogram (VFH) and preference-based fuzzy (PBF) approaches, reveals that FLB approach produces shorter and smoother paths toward the goal in almost all of the test cases examined. Then, a novel human-inspired method (HIM) is introduced. HIM is inspired by human behavior in navigation from one point to a specified goal point. A human-like reasoning ability about the situations to reach a predefined goal point while avoiding any static, moving and unforeseen obstacles are given to the robot by HIM. Comparison of HIM results with FLB suggests that HIM is more efficient and effective than FLB. Afterward, navigation strategies are built up for path following, rut following, and proximity-following control of a wheeled mobile robot in outdoor (farm) settings and off-road terrains. The proposed system is composed of different modules which are: sensor data analysis, obstacle detection, obstacle avoidance, goal seeking, and path tracking. The capabilities of the proposed navigation strategies are evaluated in variety of field experiments; the results show that the proposed approach is able to detect and follow rows of bushes robustly. This action is used for spraying plant rows in farm field. Finally, obstacle detection and obstacle avoidance modules are developed in navigation system. These modules enables the robot to detect holes or ground depressions (negative obstacles), that are inherent parts of farm settings, and also over ground level obstacles (positive obstacles) in real-time at a safe distance from the robot. Experimental tests are carried out on two mobile robots (PowerBot and Grizzly) in outdoor and real farm fields. Grizzly utilizes a 3D-laser range-finder to detect objects and perceive the environment, and a RTK-DGPS unit for localization. PowerBot uses sonar sensors and a laser range-finder for obstacle detection. The experiments demonstrate the capability of the proposed technique in successfully detecting and avoiding different types of obstacles both positive and negative in variety of scenarios