Real-Time Edge Follow: A Real-Time Path Search Approach

Real-time path search is the problem of searching a path from a starting point to a goal point in real-time. In dynamic and partially observable environments, agents need to observe the environment to track changes, explore to learn unknowns, and search suitable routes to reach the goal rapidly. These tasks frequently require real-time search. In this paper, we address the problem of real-time path search for grid-type environments; we propose an effective heuristic method, namely a real-time edge follow alternative reduction method (RTEF-ARM), which makes use of perceptual information in a real-time search. We developed several heuristics powered by the proposed method. Finally, we generated various grids (random-, maze-, and U-type), and compared our proposal with real-time A*, and its extended version real-time A* with n-look-ahead depth; we obtained very significant improvements in the solution quality.     

RTTES: Real-time search in dynamic environments

In this paper we propose a real-time search algorithm called Real-Time Target Evaluation Search (RTTES) for the problem of searching a route in grid worlds from a starting point to a static or dynamic target point in real-time. The algorithm makes use of a new effective heuristic method which utilizes environmental information to successfully find solution paths to the target in dynamic and partially observable environments. The method requires analysis of nearby obstacles to determine closed directions and estimate the goal relevance of open directions in order to identify the most beneficial move. We compared RTTES with other competing real-time search algorithms and observed a significant improvement on solution quality.     

Multi-agent real-time pursuit

In this paper, we address the problem of multi-agent pursuit in dynamic and partially observable environments, modeled as grid worlds; and present an algorithm called Multi-Agent Real-Time Pursuit (MAPS) for multiple predators to capture a moving prey cooperatively. MAPS introduces two new coordination strategies namely Blocking Escape Directions and Using Alternative Proposals, which help the predators waylay the possible escape directions of the prey in coordination. We compared our coordination strategies with the uncoordinated one against a prey controlled by Prey A*, and observed an impressive reduction in the number of moves to catch the prey.     

Real-Time Moving Target Evaluation Search

In this correspondence, we address the problem of real-time moving target search in dynamic and partially observable environments, and propose an algorithm called real-time moving target evaluation search (MTES). MTES is able to detect the closed directions around the agent and determines the estimated best direction to capture a moving target avoiding the obstacles nearby. We have also developed a new prey algorithm (Prey-A*) to test the existing and our predator algorithms in our experiments. We have obtained an impressive improvement over moving target search, real-time target evaluation search, and real-time edge follow with respect to path length. Furthermore, we have also tested our algorithm against A*.