# Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics
## 2 Previous Works
In [8; 9], [PERSON] introduced a function called _Force Inducing an Artificial Repulsion from the Surface_ (FIRAS, for its abbreviation in French) which has been largely used. For example, in [25], graph theory is used in a multi-agent system with APF to help agents avoid obstacles cooperatively by taking advantage of their interaction and proposing a method for handling the unreachability of the target. However, the potential function itself remained untouched. In [26], a path-planning method was presented for UAVs using the \(\theta*\) algorithm, which is based on the A* search algorithm. The APF approach is used for aiding the search algorithm to reduce the search space. And, again, the potential function used was the FIRAS function. In [27], a collaborative deep learning method is proposed for lunar rovers. APFs are used with specific types of obstacles to provide the Deep Q-Learning algorithm insight about the obstacle-dependent reward computation, thereby helping the learning process overall. As in previously mentioned cases, the potential functions remained the same. The reader should notice that all aforementioned works use APF as an auxiliary and very useful tool, but not as the core of the methods presented. Maybe, on that reasoning, the potential function is not viewed as a matter of study. However, in [28], the potential function is slightly modified by multiplying the FIRAS function by a distance factor. This has the effect of keeping the general structure of the APF and allowing both the dynamic step-adjustment and the collaborative path-planning method to work as intended to successfully reach the intended target. Once more, the proposal is built around the same potential function. In this work, a completely different potential function is proposed. This function has three parameters focused on the behavior of the agent. It is, in that sense, closer to the concepts introduced by [PERSON], [PERSON] and [PERSON], as mentioned before. In more recent works [29; 30; 31; 32], the use of memory, graphs, detailed maps and bioinspired algorithms is exploited to plan the the agent's path in advance. The main downsides of this are the requirement of previous knowledge of the environment and of several iterations to find a suitable path, necessitating more computational time and resources. As mentioned above, heuristics have also been used to find the path between two points. However, the path calculation requires knowledge about the state of other agents in the environment, thus requiring global knowledge. However, approaches like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) do not use mapping information of the environment--they rely on the knowledge of the state of other agents [33; 34; 35; 36]. The DARPA Urban Challenge [37] is an example of how all the above can be integrated into an agent in a very complex scenario. However, such applications rely completely on the knowledge of the environment. If there is no information available, no strategy or algorithm can be used. In partially observable environments, it is not possible to plan a route beforehand given the lack of information, and, therefore, most of the above-discussed approaches are not suitable. Finally, it is important to clarify that path/route planning refers to computation of points that the agent will visit. Furthermore, trajectory planning refers to the computing of points and velocity vectors. None of the above can be performed when the only information available is the local information perceived by the agent's sensors.