NafisNav: an Indoor Navigation Algorithm for Embedded Systems and based on Grid Maps

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. N. O. Eraghi, F. López-Colino, A. de Castro and J. Garrido, "NafisNav: An indoor navigation algorithm for embedded systems and based on grid maps," 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, 2015, pp. 345-350. doi: 10.1109/ICIT.2015.7125122An important goal in navigation of low cost robots is low memory usage. In this paper, we propose a novel navigation algorithm (NafisNav) suitable for embedded systems with low resources, mainly memory. The proposed path finding algorithm is designed and implemented in grid maps. Unlike existing algorithms, that mainly focus on obtaining the shortest possible path for navigation, the proposed algorithm focuses on reducing memory consumption, even at the cost of not always obtaining the best path. Experimental results show the trade-off between path length and memory consumption that is obtained, comparing it with typical algorithms such as Dijkstra or A*.This work has been supported by the Spanish Ministerio de Ciencia e Innovacion under project TEC2009-09871

ERA*: Enhanced Relaxed A* algorithm for Solving the Shortest Path Problem in Regular Grid Maps

This paper introduces a novel algorithm for solving the point-to-point shortest path problem in a static regular 8-neighbor connectivity (G8) grid. This algorithm can be seen as a generalization of Hadlock algorithm to G8 grids, and is shown to be theoretically equivalent to the relaxed $A^*$ ($RA^*$) algorithm in terms of the provided solution's path length, but with substantial time and memory savings, due to a completely different computation strategy, based on defining a set of lookup matrices. Through an experimental study on grid maps of various types and sizes (1290 runs on 43 maps), it is proven to be 2.25 times faster than $RA^*$ and 17 times faster than the original $A^*$, in average. Moreover, it is more memory-efficient, since it does not need to store a G score matrix

Contributions to mobile robot navigation based embedded systems and grid mapping

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 13-07-2015Path planning is a problem as old as humankind. The necessity of optimizing the resources to reach a location has been a concern since prehistory. Technology has allowed approaching this problematic using new resources. However, it has also introduced new requirements. This thesis is focused on path planning from the perspective of an embedded system using grid maps. For battery-dependent robots, path length is very relevant because it is directly related to motor consumption and the autonomy of the system. Nevertheless, a second aspect to be considered when using embedded systems is the HW requirements. These requirements comprise floating point units or storage capacity. When computer-based path planning algorithms are directly ported to these embedded systems, their HW requirements become a limitation. This thesis presents two novel path planning algorithms which take into account both the search of the shortest path and the optimization of HW resources. These algorithms are HCTNav and NafisNav. The HCTNav algorithm was developed using the intuitive approach as trying to reach the goal in a straight trajectory until an obstacle is found. When an obstacle is found, it must be surrounded until the straight path to the goal can be continued, reaching this goal or another obstacle. Considering HCTNav as a path planning algorithm, both possible surrounding trajectories can be explored and then choose the best solution. Therefore, for each obstacle the algorithm finds, there is a branch in the search of the solution. Finally, the algorithm includes an optimization procedure which reduces the length of the obtained paths if it is possible to go between nonconsecutive waypoints in straight line. The NafisNav algorithm evolves from a depth-first search. For each iteration of the algorithm, the straight trajectory to the goal position is verified. If this trajectory is not available, the algorithm selects from the unexplored neighbor cells the closest one to the target. If two neighbors were at the same distance, the algorithm would branch evaluating both alternatives. This algorithm includes a backtracking procedure just in case it finds a dead end. Finally, from every possible solution, the algorithm proposes the one that, after optimization, provides the shortest path. The new algorithms have been evaluated and compared with the most extended algorithms of the state of the art: Dijkstra and A*. The two chosen evaluation metrics have been final path length and required dynamic memory. HCTNav provides an average penalization in the path length of 2.1% and NafisNav has this penalization increased to 4.5%. However, these algorithms present a decrease of the memory requirements of a 19% for HCTNav and of a 49% for the NafisNav algorithmLa planificación de una ruta es un problema casi tan antiguo como la humanidad. La necesidad de optimizar esfuerzos para alcanzar un objetivo ha sido una gran preocupación desde la prehistoria. La tecnología ha permitido abordar la solución de esta problemática con nuevos medios, pero también ha planteado otros requisitos distintos. Esta tesis aborda el problema de navegación desde la perspectiva de los sistemas empotrados en entornos de mapas de rejilla. En todo robot dependiente de batería, la longitud final es un factor relevante porque se traduce directamente en el consumo de los motores y repercute en la autonomía del sistema. No obstante, un segundo factor que aparece al utilizar sistemas empotrados es el uso de recursos HW, ya sean unidades de coma flotante o capacidad de almacenamiento. Cuando se intenta adaptar los algoritmos diseñados para ser ejecutados en un ordenador nos enfrentamos a una gran demanda de estos recursos. La tesis plantea dos algoritmos novedosos que tienen en cuenta tanto la búsqueda de un camino lo más corto posible como la optimización de recursos HW: HCTNav y NafisNav. El algoritmo HCTNav se desarrolló siguiendo el movimiento intuitivo de quien trata de ir en línea recta hasta que encuentra un obstáculo y lo rodea hasta que puede continuar en línea recta hasta el destino, o en caso contrario hasta otro obstáculo. Dado que se trata un algoritmo de planificación, se puede plantear rodear el obstáculo por ambos lados y elegir cuál es la mejor opción. Por lo tanto, cada obstáculo genera una bifurcación en la búsqueda de solución. Este algoritmo incluye un proceso de optimización por el que se reduce el recorrido final si se pueden saltar puntos intermedios viajando en línea recta. El algoritmo NafisNav plantea una búsqueda en profundidad modificada. En cada iteración se intenta alcanzar el destino verificando si se puede alcanzar en línea recta. En caso de no poder alcanzarlo, se avanza al vecino, de entre los contiguos no explorados, aplicando un criterio de mínima distancia al objetivo. Si hubiera dos candidatos posibles, la búsqueda se bifurca, evaluando ambas opciones. Por último, se incluye un proceso de retroceso para el caso en el que se llegara a un punto sin salida. De entre las soluciones posibles se presenta aquella que, tras la optimización, obtiene el mínimo recorrido. Los nuevos algoritmos han sido evaluados y comparados con los algoritmos más extendidos en el estado del arte: Dijkstra y A*. Los dos criterios utilizados han sido la longitud final del camino y el espacio de memoria que se necesita. HCTNav tiene una penalización promedio del 2,1 % en la longitud de la solución, mientras que NafisNav aplica una penalización promedio del 4,5 %. HCTNav obtiene una reducción del consumo de memoria del 19 % comparado con la mejor solución entre Dijkstra y A*. NafisNav mejora estos resultados con una reducción del 49

Search methods for an autonomous underwater vehicle using scalar measurements

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution July 1996The continuing development of the autonomous underwater vehicle as an oceanographic research tool has opened up the realm of scientific possibility in the field of deep ocean research. The ability of a vehicle to travel to the ocean floor untethered, collect data for an extended period of time and return to the surface for recovery can make precise oceanographic surveying more economically practical and more efficient. This thesis investigates several scalar parameter searching techniques which have their basis in mathematical optimization algorithms and their applicability for use specifically within the context of autonomous underwater vehicle dynamics. In particular, a modified version of the circular gradient evaluation in the simulated environment of a hydrothermal plume is examined as a test case. Using a priori knowledge of the expected structure of the scalar parameter contour is shown to be advantageous in optimizing the search

Optimisation sous contraintes de problèmes distribués par auto-organisation coopérative

Quotidiennement, divers problèmes d'optimisation : minimiser un coût de production, optimiser le parcours d'un véhicule, etc sont à résoudre. Ces problèmes se caractérisent par un degré élevé de complexité dû à l'hétérogénéité et la diversité des acteurs en jeu, à la masse importante des données ainsi qu'à la dynamique des environnements dans lesquels ils sont plongés. Face à la complexité croissante de ces applications, les approches de résolution classiques ont montré leurs limites. Depuis quelques années, la communauté scientifique s'intéresse aux développements de nouvelles solutions basées sur la distribution du calcul et la décentralisation du contrôle plus adaptées à ce genre de problème. La théorie des AMAS (Adaptive Multi-Agents Systems) propose le développement de solutions utilisant des systèmes multi-agents auto-adaptatifs par auto-organisation coopérative. Cette théorie a montré son adéquation pour la résolution de problèmes complexes et dynamiques, mais son application reste à un niveau d'abstraction assez élevé. L'objectif de ce travail est de spécialiser cette théorie pour la résolution de ce genre de problèmes. Ainsi, son utilisation en sera facilitée. Pour cela, le modèle d'agents AMAS4Opt avec des comportements et des interactions coopératifs et locaux a été défini. La validation s'est effectuée sur deux problèmes clés d'optimisation : le contrôle manufacturier et la conception de produit complexe. De plus, afin de montrer la robustesse et l'adéquation des solutions développées, un ensemble de critères d'évaluation permettant de souligner les points forts et faibles des systèmes adaptatifs et de les comparer à des systèmes existants a été défini.We solve problems and make decisions all day long. Some problems and decisions are very challenging: What is the best itinerary to deliver orders given the weather, the traffic and the hour? How to improve product manufacturing performances? etc. Problems that are characterized by a high level of complexity due to the heterogeneity and diversity of the participating actors, to the increasing volume of manipulated data and to the dynamics of the applications environments. Classical solving approaches have shown their limits to cope with this growing complexity. For the last several years, the scientific community has been interested in the development of new solutions based on computation distribution and control decentralization. The AMAS (Adaptive Multi-Agent-Systems) theory proposes to build solutions based on self-adaptive multi-agent systems using cooperative self-organization. This theory has shown its adequacy to solve different complex and dynamic problems, but remains at a high abstraction level. This work proposes a specialization of this theory for complex optimization problem solving under constraints. Thus, the usage of this theory is made accessible to different non-AMAS experts' engineers. Thus, the AMAS4Opt agent model with cooperative, local and generic behaviours and interactions has been defined.This model is validated on two well-known optimization problems: scheduling in manufacturing control and complex product design. Finally, in order to show the robustness and adequacy of the developed solutions, a set of evaluation criteria is proposed to underline the advantages and limits of adaptive systems and to compare them with already existing systems