# Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics
###### Abstract  
When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of autonomous agent navigation under partial-observability constraints by using a novel approach: Artificial Potential Fields (APF) assisted by heuristics. The well-known problem of local minima is addressed by providing the agents with the ability to make individual choices that can be exploited in a swarm. We propose a new potential function, which provides precise control of the potential field's reach and intensity, and the use of auxiliary heuristics provides temporary target points while the agent explores, in search of the position of the real intended target. Artificial Potential Fields, together with auxiliary search heuristics, are integrated into a novel navigation model for autonomous agents who have limited or no knowledge of their environment. Experimental results are shown in 2D scenarios that pose challenging situations with multiple obstacles, local minima conditions and partial-observability constraints, clearly showing that an agent driven using the proposed model is capable of completing the navigation task, even under the partial-observability constraints.  
Agnesi witch; artificial potential fields; curve of Agnesi; heuristics; partial observability; autonomous agents; navigation +
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The purpose of this work is to show an interdisciplinary approach in autonomous agent navigation that aims to take up animal-imitation principles by combining techniques of Artificial Potential Fields (APF) and heuristics. In order to validate our hypothesis, we need to show that an agent with no knowledge about the environment and only partial-observation capabilities can still successfully reach its intended target using our proposed behavioral model. That is why all the experiments presented are evaluated strictly qualitatively and no quantitative measure is taken, compared or considered significant in any sense.  
Experiments carried out are motivated by modern problems such as search and rescue operations and are inspired by the behavior of animals when searching for food or hunting prey. In such scenarios, the location of the intended target is unknown and the agent has insufficient or no knowledge about the environment. All experiments place agents into a predefined environment, with no previous knowledge and under partial-observability constraints.  
The present work is divided as follows. In Section 2, some previous works are presented. These are works with particular significance given their impact over the years, and they are described in detail, with particular attention on the concepts that they introduced. Although there is a notable number of recent works in the field of APF, the structure of the potential function has remained the same in all of them. This fact is also discussed at the end of the section. The proposed approach is presented in Section 3, highlighting the difference between a classical APF approach and the proposed one. In this section, the need for an extra element, the heuristics, is justified and explained under the partial-observability constraints. Given the complexity added by the proposed potential function, in Section 3.4, the impact of the parameters on the behavior of the function is explained in detail. Some experiments are presented in Section 4. All of them are 2D environments where the agent's behavior can be clearly observed and tracked. All experiments were performed in MATLAB R2022b with the _Mobile Robotics Simulation_ toolbox. Conclusions are drawn in Section 5, and future work is outlined in Section 6, where interesting applications and expansions of the proposed model are discussed in the context of intelligent agents and behavioral models. The estimated potential of this approach in multi-agent behavior and navigation models is also discussed in that final section.