Prema sigurnoj navigaciji vozila u dinamičkim urbanim scenarijima

This paper describes the deliberative part of a navigation architecture designed for safe vehicle navigation in dynamic urban environments. It comprises two key modules working together in a hierarchical fashion: (a) the Route Planner whose purpose is to compute a valid itinerary towards the a given goal. An itinerary comprises a geometric path augmented with additional information based on the structure of the environment considered and traffic regulations, and (b) the Partial Motion Planner whose purpose is to ensure the proper following of the itinerary while dealing with the moving objects present in the environment (eg other vehicles, pedestrians). In the architecture proposed, a special attention is paid to the motion safety issue, ie the ability to avoid collisions. Different safety levels are explored and their operational conditions are explicitly spelled out (something which is usually not done).Ovaj članak opisuje ciljno orijentirani dio navigacijske arhitekture za sigurnu navigaciju vozilima u dinamičkim urbanim sredinama. Sastoji se od dva važna modula, koji su hierarhijski povezani: (a) Planer puta koji je odgovoran za pronalaženje valjane globalne rute prema zadanom cilju – ta ruta se sastoji od geometrijskog puta sa dodatnim informacijama u odnosu na zadanu strukturu okoline i regulaciju prometa; (b) Parcijalni planer gibanja čiji zadatak je slijeđenje zadane globalne rute uz navigaciju u prisutnosti pokretnih objekata u okolini (npr. ostala vozila i pješaci). U predloženoj arhitekturi posebna pažnja se pridodaje sigurnosti gibanja, dakle sposobnosti izbjegavanja sudara. Razmotrene su različite razine sigurnosti uz izričiti opis njihovih zadanih režima rada (što je uobičajeno izostavljenou analizama)

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Robot Team Formation Control Using Communication Throughput Approach

In this thesis, we consider a team of robots forming a mobile robot network cooperating to accomplish a mission in an unknown but structured environment. The team has no a-priori knowledge of the environment. Robots have limited memory storage capabilities, not enough to map the environment. Each robot also has limited sensor capability and computational power. Due to the need to avoid obstacles and other environment effects, some robots get delayed from the rest. Using tracking controller, the robot team should follow the leader in a flexible formation shape without losing network connectivity, and that was achieved by monitoring the end-to-end throughput level

An Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data

In this thesis, we introduce a novel architecture called Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data (iARTEC ) . The proposed architecture integrates different terrain characterization and classification with other robotic system components. Within iARTEC , we consider the problem of having a legged robot autonomously learn to identify different terrains. Robust terrain identification can be used to enhance the capabilities of legged robot systems, both in terms of locomotion and navigation. For example, a robot that has learned to differentiate sand from gravel can autonomously modify (or even select a different) path in favor of traversing over a better terrain. The same knowledge of the terrain type can also be used to guide a robot in order to avoid specific terrains. To tackle this problem, we developed four approaches for terrain characterization, classification, path planning, and control for a mobile legged robot. We developed a particle system inspired approach to estimate the robot footâ ground contact interaction forces. The approach is derived from the well known Bekkerâ s theory to estimate the contact forces based on its point contact model concepts. It is realistically model real-time 3-dimensional contact behaviors between rigid body objects and the soil. For a real-time capable implementation of this approach, its reformulated to use a lookup table generated from simple contact experiments of the robot foot with the terrain. Also, we introduced a short-range terrain classifier using the robot embodied data. The classifier is based on a supervised machine learning approach to optimize the classifier parameters and terrain it using proprioceptive sensor measurements. The learning framework preprocesses sensor data through channel reduction and filtering such that the classifier is trained on the feature vectors that are closely associated with terrain class. For the long-range terrain type prediction using the robot exteroceptive data, we present an online visual terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs). In addition, we described a terrain dependent navigation and path planning approach that is based on E* planer and employs a proposed metric that specifies the navigation costs associated terrain types. This generated path naturally avoids obstacles and favors terrains with lower values of the metric. At the low level, a proportional input-scaling controller is designed and implemented to autonomously steer the robot to follow the desired path in a stable manner. iARTEC performance was tested and validated experimentally using several different sensing modalities (proprioceptive and exteroceptive) and on the six legged robotic platform CREX. The results show that the proposed architecture integrating the aforementioned approaches with the robotic system allowed the robot to learn both robot-terrain interaction and remote terrain perception models, as well as the relations linking those models. This learning mechanism is performed according to the robot own embodied data. Based on the knowledge available, the approach makes use of the detected remote terrain classes to predict the most probable navigation behavior. With the assigned metric, the performance of the robot on a given terrain is predicted. This allows the navigation of the robot to be influenced by the learned models. Finally, we believe that iARTEC and the methods proposed in this thesis can likely also be implemented on other robot types (such as wheeled robots), although we did not test this option in our work

Mobile robots in indoor logistics

Robotics is a field capturing a large interest within the academia and the industrialist, during last years the interest has also spread to the general audience. Traditionally, the sector of robot manipulators has been the one capturing the largest share of markets and the general audience. However, the latest advances on perception and cognition have paved the way for the wide use of mobile robots in general, and in industrial systems in particular. The thesis presents a survey of the main components to take into account at the time of deciding to implement mobile robots for indoor logistics in automation systems. In particular, the work presents different locomotion methods, navigation systems, technologies for implementing path panning of the robots and some solutions for the fleet management in case of large population of robots at the factory floor. In addition, the thesis also introduces some of the most promising commercial products and, when possible, presents the application in real industrial use cases. Understanding the initial stage of the current market situation, this survey is complemented with a patent analysis of the field, providing a landscape of some 500 patents. A few of the most relevant patents have been also studied and presented in great detail. The concluding chapter of the thesis summarizes the main findings and proved the original perception of the author regarding the availability of new technological solutions, solution that will allow mobile robots to stress their presence at the factory floor becoming essential component for future indoor logistics systems

Indoor Navigation and Manipulation using a Segway RMP

This project dealt with a Segway RMP, utilizing it in an assistive-technology manner, encompassing navigation and manipulation aspects of robotics. First, background research was conducted to develop a blueprint for the robot. The hardware, software, and configuration of the RMP was updated, and a robotic arm was designed to extend the RMP’s capabilities. The robot was programmed to accomplish autonomous multi-floor navigation through the use of the navigation stack in ROS, image detection, and a GUI. The robot can navigate through the hallways of the building utilizing the elevator. The robotic arm was designed to accomplish tasks such as pressing a button and picking an object up off of a table. The Segway RMP is designed to be utilized and expanded upon as a robotics research platform