| Summary: | Nós refinamos o planejador introduzindo uma nova forma para o núcleo da equação que permite facilmente lidar com terrenos não-homogêneos. Isto é obtido através de mudanças locais na concavidade/convexidade do potencial, criando regiões com altas ou baixas preferências de navegação. Nós integramos esta nova equação ao planejador hierárquico, surgindo uma ampla variedade de aplicações. Nossa proposta contribui para diversas áreas incluindo a navegação de agentes, pathfinding em jogos, simulação de multidões, e a navegação de robôs. Nossas publicações reforçam a relevância e robustez do método proposto. === In this work, we propose a new solution to agent navigation based upon boundary value problems (BVP), called Configurable Flows, to control steering behaviors of characters in dynamic environments. We use a potential field formalism that allows synthetic actors to move negotiating space, avoiding collisions, and attaining goals while producing very individual paths. The individuality of each character can be set by changing its inner field parameters leading to a broad range of possible behaviors without jeopardizing its performance. BVP Path Planners generate potential fields through a differential equation whose gradient descent represents navigation routes from any point of the environment to a goal position. Resulting paths are smooth and free from local minima. In spite of these advantages, these kind of planners consumes a lot of time to produce a solution. Our approach combines a BVP Path Planner with the Full Multigrid Method, which solves elliptic partial differential equations using a hierarchical strategy. The proposed planner enables real-time performance in large environments. Results show that our proposal spends less than 1% of the time needed to compute a solution using the original BVP planners in several environments. We refine our Path Planner by introducing a new form of the core equation that permits to easily cope with terrain inhomogeneities. This is accomplished by locally changing the concavity/ convexity of the potential, and then creating regions with higher or lower navigation preferences. As the potential field requires several steps to converge, this approach can be expensive computationally. To overcome this problem, we integrate this novel core equation to the hierarchical planner, emerging a wide variety of applications. We believe our proposal can contribute to several areas of research including agent navigation, pathfinding for games, crowd simulation and robotics. Our publications reinforce the relevance of the proposed method.
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