Real time target evaluation search

Abstract

In this paper we propose a real-time search algorithm called RealTime 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 obstacles to determine closed directions and estimate the goal relevance of open directions in order to identify the most beneficial move. The environment is assumed to be a planar grid and the agent has limited perception. In this paper, we compared RTTES with Real-Time A* (RTA*) and Real-Time Edge Follow (RTEF), and observed a significant improvement.

Multi-agent real-time pursuit

“…Recently, two real-time search algorithms, real-time edge follow (RTEF) [37,38] and real-time target evaluation search (RTTES) [39,40] have been proposed for partially observable environments. Although these algorithms were developed for static targets, they were having the potential to handle moving targets with little modification.…”

Moving target search in grid worlds

“…Although these algorithms work fine with partially observable environments, they are not capable of handling moving targets. There are also a number of on-line approaches [6,5,9,11,10,3]. As a matter of fact, only few of these algorithms can be adapted against a moving target.…”

RTTES: Real-time search in dynamic environments

“…Although RTEF is able to determine the closed (non-promising) directions successfully, it is weak in selecting the right move from the remaining alternatives as it uses the poor Euclidian distance heuristic. Therefore, we focused on a new method for better selection and improved the performance of RTEF [17].…”