Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm

The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical, side we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 200 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.Comment: Final version as submitted to ICAPS-2017 (main track); 8 pages; 4 figures; 1 algorithm; 2 table

Toward human-like pathfinding with hierarchical approaches and the GPS of the brain theory

Pathfinding for autonomous agents and robots has been traditionally driven by finding optimal paths. Where typically optimality means finding the shortest path between two points in a given environment. However, optimality may not always be strictly necessary. For example, in the case of video games, often computing the paths for non-player characters (NPC) must be done under strict time constraints to guarantee real time simulation. In those cases, performance is more important than finding the shortest path, specially because often a sub-optimal path can be just as convincing from the point of view of the player. When simulating virtual humanoids, pathfinding has also been used with the same goal: finding the shortest path. However, humans very rarely follow precise shortest paths, and thus there are other aspects of human decision making and path planning strategies that should be incorporated in current simulation models. In this thesis we first focus on improving performance optimallity to handle as many virtual agents as possible, and then introduce neuroscience research to propose pathfinding algorithms that attempt to mimic humans in a more realistic manner.In the case of simulating NPCs for video games, one of the main challenges is to compute paths as efficiently as possible for groups of agents. As both the size of the environments and the number of autonomous agents increase, it becomes harder to obtain results in real time under the constraints of memory and computing resources. For this purpose we explored hierarchical approaches for two reasons: (1) they have shown important performance improvements for regular grids and other abstract problems, and (2) humans tend to plan trajectories also following an topbottom abstraction, focusing first on high level location and then refining the path as they move between those high level locations. Therefore, we believe that hierarchical approaches combine the best of our two goals: improving performance for multi-agent pathfinding and achieving more human-like pathfinding. Hierarchical approaches, such as HNA* (Hierarchical A* for Navigation Meshes) can compute paths more efficiently, although only for certain configurations of the hierarchy. For other configurations, the method suffers from a bottleneck in the step that connects the Start and Goal positions with the hierarchy. This bottleneck can drop performance drastically.In this thesis we present different approaches to solve the HNA* bottleneck and thus obtain a performance boost for all hierarchical configurations. The first method relies on further memory storage, and the second one uses parallelism on the GPU. Our comparative evaluation shows that both approaches offer speed-ups as high as 9x faster than A*, and show no limitations based on hierarchical configuration. Then we further exploit the potential of CUDA parallelism, to extend our implementation to HNA* for multi-agent path finding. Our method can now compute paths for over 500K agents simultaneously in real-time, with speed-ups above 15x faster than a parallel multi-agent implementation using A*. We then focus on studying neurosience research to learn about the way that humans build mental maps, in order to propose novel algorithms that take those finding into account when simulating virtual humans. We propose a novel algorithm for path finding that is inspired by neuroscience research on how the brain learns and builds cognitive maps. Our method represents the space as a hexagonal grid, based on the GPS of the brain theory, and fires memory cells as counters. Our path finder then combines a method for exploring unknown environments while building such a cognitive map, with an A* search using a modified heuristic that takes into account the GPS of the brain cognitive map.El problema de Pathfinding para agentes autónomos o robots, ha consistido tradicionalmente en encontrar un camino óptimo, donde por óptimo se entiende el camino de distancia mínima entre dos posiciones de un entorno. Sin embargo, en ocasiones puede que no sea estrictamente necesario encontrar una solución óptima. Para ofrecer la simulación de multitudes de agentes autónomos moviéndose en tiempo real, es necesario calcular sus trayectorias bajo condiciones estrictas de tiempo de computación, pero no es fundamental que las soluciones sean las de distancia mínima ya que, con frecuencia, el observador no apreciará la diferencia entre un camino óptimo y un sub-óptimo. Por tanto, suele ser suficiente con que la solución encontrada sea visualmente creíble para el observado. Cuando se simulan humanoides virtuales en aplicaciones de realidad virtual que requieren avatares que simulen el comportamiento de los humanos, se tiende a emplear los mismos algoritmos que en video juegos, con el objetivo de encontrar caminos de distancia mínima. Pero si realmente queremos que los avatares imiten el comportamiento humano, tenemos que tener en cuenta que, en el mundo real, los humanos rara vez seguimos precisamente el camino más corto, y por tanto se deben considerar otros aspectos que influyen en la toma de decisiones de los humanos y la selección de rutas en el mundo real. En esta tesis nos centraremos primero en mejorar el rendimiento de la búsqueda de caminos para poder simular grandes números de humanoides virtuales autónomos, y a continuación introduciremos conceptos de investigación con mamíferos en neurociencia, para proponer soluciones al problema de pathfinding que intenten imitar con mayor realismo, el modo en el que los humanos navegan el entorno que les rodea. A medida que aumentan tanto el tamaño de los entornos virtuales como el número de agentes autónomos, resulta más difícil obtener soluciones en tiempo real, debido a las limitaciones de memoria y recursos informáticos. Para resolver este problema, en esta tesis exploramos enfoques jerárquicos porque consideramos que combinan dos objetivos fundamentales: mejorar el rendimiento en la búsqueda de caminos para multitudes de agentes y lograr una búsqueda de caminos similar a la de los humanos. El primer método presentado en esta tesis se basa en mejorar el rendimiento del algoritmo HNA* (Hierarchical A* for Navigation Meshes) incrementando almacenamiento de datos en memoria, y el segundo utiliza el paralelismo para mejorar drásticamente el rendimiento. La evaluación cuantitativa realizada en esta tesis, muestra que ambos enfoques ofrecen aceleraciones que pueden llegar a ser hasta 9 veces más rápidas que el algoritmo A* y no presentan limitaciones debidas a la configuración jerárquica. A continuación, aprovechamos aún más el potencial del paralelismo ofrecido por CUDA para extender nuestra implementación de HNA* a sistemas multi-agentes. Nuestro método permite calcular caminos simultáneamente y en tiempo real para más de 500.000 agentes, con una aceleración superior a 15 veces la obtenida por una implementación paralela del algoritmo A*. Por último, en esta tesis nos hemos centrado en estudiar los últimos avances realizados en el ámbito de la neurociencia, para comprender la manera en la que los humanos construyen mapas mentales y poder así proponer nuevos algoritmos que imiten de forma más real el modo en el que navegamos los humanos. Nuestro método representa el espacio como una red hexagonal, basada en la distribución de ¿place cells¿ existente en el cerebro, e imita las activaciones neuronales como contadores en dichas celdas. Nuestro buscador de rutas combina un método para explorar entornos desconocidos mientras construye un mapa cognitivo hexagonal, utilizando una búsqueda A* con una nueva heurística adaptada al mapa cognitivo del cerebro y sus contadores

Reconfigurable and Agile Legged-Wheeled Robot Navigation in Cluttered Environments with Movable Obstacles

Legged and wheeled locomotion are two standard methods used by robots to perform navigation. Combining them to create a hybrid legged-wheeled locomotion results in increased speed, agility, and reconfigurability for the robot, allowing it to traverse a multitude of environments. The CENTAURO robot has these advantages, but they are accompanied by a higher-dimensional search space for formulating autonomous economical motion plans, especially in cluttered environments. In this article, we first review our previously presented legged-wheeled footprint reconfiguring global planner. We describe the two incremental prototypes, where the primary goal of the algorithms is to reduce the search space of possible footprints such that plans that expand the robot over the low-lying wide obstacles or narrow into passages can be computed with speed and efficiency. The planner also considers the cost of avoiding obstacles versus negotiating them by expanding over them. The second part of this article presents our new work on local obstacle pushing, which further increases the number of tight scenarios the planner can solve. The goal of the new local push-planner is to place any movable obstacle of unknown mass and inertial properties, obstructing the previously planned trajectory from our global planner, to a location devoid of obstruction. This is done while minimising the distance traveled by the robot, the distance the object is pushed, and its rotation caused by the push. Together, the local and global planners form a major part of the agile reconfigurable navigation suite for the legged-wheeled hybrid CENTAURO robot

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

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

Contributions to autonomous robust navigation of mobile robots in industrial applications

151 p.Un aspecto en el que las plataformas móviles actuales se quedan atrás en comparación con el punto que se ha alcanzado ya en la industria es la precisión. La cuarta revolución industrial trajo consigo la implantación de maquinaria en la mayor parte de procesos industriales, y una fortaleza de estos es su repetitividad. Los robots móviles autónomos, que son los que ofrecen una mayor flexibilidad, carecen de esta capacidad, principalmente debido al ruido inherente a las lecturas ofrecidas por los sensores y al dinamismo existente en la mayoría de entornos. Por este motivo, gran parte de este trabajo se centra en cuantificar el error cometido por los principales métodos de mapeado y localización de robots móviles,ofreciendo distintas alternativas para la mejora del posicionamiento.Asimismo, las principales fuentes de información con las que los robots móviles son capaces de realizarlas funciones descritas son los sensores exteroceptivos, los cuales miden el entorno y no tanto el estado del propio robot. Por esta misma razón, algunos métodos son muy dependientes del escenario en el que se han desarrollado, y no obtienen los mismos resultados cuando este varía. La mayoría de plataformas móviles generan un mapa que representa el entorno que les rodea, y fundamentan en este muchos de sus cálculos para realizar acciones como navegar. Dicha generación es un proceso que requiere de intervención humana en la mayoría de casos y que tiene una gran repercusión en el posterior funcionamiento del robot. En la última parte del presente trabajo, se propone un método que pretende optimizar este paso para así generar un modelo más rico del entorno sin requerir de tiempo adicional para ello

Admissible Velocity Propagation : Beyond Quasi-Static Path Planning for High-Dimensional Robots

Path-velocity decomposition is an intuitive yet powerful approach to address the complexity of kinodynamic motion planning. The difficult trajectory planning problem is solved in two separate, simpler, steps: first, find a path in the configuration space that satisfies the geometric constraints (path planning), and second, find a time-parameterization of that path satisfying the kinodynamic constraints. A fundamental requirement is that the path found in the first step should be time-parameterizable. Most existing works fulfill this requirement by enforcing quasi-static constraints in the path planning step, resulting in an important loss in completeness. We propose a method that enables path-velocity decomposition to discover truly dynamic motions, i.e. motions that are not quasi-statically executable. At the heart of the proposed method is a new algorithm -- Admissible Velocity Propagation -- which, given a path and an interval of reachable velocities at the beginning of that path, computes exactly and efficiently the interval of all the velocities the system can reach after traversing the path while respecting the system kinodynamic constraints. Combining this algorithm with usual sampling-based planners then gives rise to a family of new trajectory planners that can appropriately handle kinodynamic constraints while retaining the advantages associated with path-velocity decomposition. We demonstrate the efficiency of the proposed method on some difficult kinodynamic planning problems, where, in particular, quasi-static methods are guaranteed to fail.Comment: 43 pages, 14 figure