A 2-dimensional ACO-based path planner for off-line robot path planning

2013-2014 > Academic research: refereed > Refereed conference paperAccepted ManuscriptPublishe

A 2-Dimensional ACO-Based Path Planner for Off-Line Robot Path Planning

Wireless sensor networks are usually deployed in scenarios that are too hostile for human personnel to perform maintenance tasks. Wireless sensor nodes usually exchange information in a multi-hop manner. Connectivity is crucial to the performance of a wireless sensor network. In case a network is partitioned due to node failures, it is possible to re-connect the fragments by setting up bridges using mobile platforms. Given the landscape of a terrain, the mobile platforms should be able reach the target position using a desirable path. In this paper, an off-line robot path planner is proposed to find desirable paths between arbitrary points in a given terrain. The proposed path planner is based on ACO algorithms. Unlike ordinary ACO algorithms, the proposed path planner provides its artificial ants with extra flexibility in making routing decisions. Simulation results show that such enhancement can greatly improve the qualities of the paths obtained. Performances of the proposed path planner can be further optimized by fine-tuning its parameters.Department of Electronic and Information EngineeringRefereed conference pape

An ACO-based off-line path planner for nonholonomic mobile robots

2013-2014 > Academic research: refereed > Refereed conference paperAccepted ManuscriptPublishe

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

Finding energy-efficient paths on uneven terrains

2014-2015 > Academic research: refereed > Refereed conference paperAccepted ManuscriptPublishe

Autonomous Navigation for Unmanned Aerial Systems - Visual Perception and Motion Planning

L'abstract è presente nell'allegato / the abstract is in the attachmen

Rapid replanning of energy-efficient paths for navigation on uneven terrains

2015-2016 > Academic research: refereed > Refereed conference paperAccepted ManuscriptPublishe

A constraint-aware heuristic path planner for finding energy-efficient paths on uneven terrains

2014-2015 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Distributed multi-robot exploration under complex constraints

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería InformáticaClave Programa: DBICódigo Línea: 19Mobile robots have emerged as a prime alternative to explore physical processes of interest. This is particularly relevant in situations that have a high risk for humans, like e.g. in search and rescue missions, and for applications in which it is desirable to reduce the required time and manpower to gather information, like e.g. for environmental analysis. In such context, exploration tasks can clearly benefit from multi-robot coordination. In particular, distributed multi-robot coordination strategies offer enormous advantages in terms of both system¿s efficiency and robustness, compared to single-robot systems. However, most state-of-the-art strategies employ discretization of robots¿ state and action spaces. This makes them computationally intractable for robots with complex dynamics, and limits their generality. Moreover, most strategies cannot handle complex inter-robot constraints like e.g. communication constraints. The goal of this thesis is to develop a distributed multi-robot exploration algorithm that tackles the two aforementioned issues. To achieve this goal we first propose a single-robot myopic approach, in which we build to develop a non-myopic informative path planner. In a second step, we extend our non-myopic single-robot algorithm to the multi-robot case. Our proposed algorithms build on the following techniques: (i) Gaussian Processes (GPs) to model the spatial dependencies of a physical process of interest, (ii) sampling-based planners to calculate feasible paths; (iii) information metrics to guide robots towards informative locations; and (iv) distributed constraint optimization techniques for multi-robot coordination. We validated our proposed algorithms in simulations and experiments. Specifically, we carried out the following experiments: mapping of a magnetic field with a ground-based robot, mapping of a terrain profile with two quadcopters equipped with an ultrasound sensor, and exploration of a simulated wind field with three quadcopters. Results demonstrate the effectiveness of our approach to perform exploration tasks under complex constraints.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformáticaPostprin