**1. Introduction**

Path planning is a well-known topic in the area of robotics whose main objective is to determine the best way for a robot to navigate autonomously in a work environment. Although many areas of robotics have benefited from research in this field, one of the most recent is its application to the problem of autonomous construction of environment maps, also known as integrated exploration or active SLAM, where the basic principle of operation consists of a mobile robot that must move through an unknown environment while constructing an environment map of it.

In this context, one of the first contributions can be traced to the work of Feder et al. [1], in which the authors describe an adaptive trajectory planning technique applied to the SLAM problem, where through the minimization of the inverse of the error covariance as an objective function, the next position is determined where the

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*Path Planning for Autonomous Vehicles - Ensuring Reliable Driverless Navigation...*

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robot must move with the intention of maximizing the information obtained, while it simultaneously localizes and constructs the environment map.

Commonly, the development of SLAM path planners requires dynamic and agile algorithms that can be adapted to new operating conditions in environments when obstacles are detected; with this in mind, many proposals have been developed by various researchers [2–10], being one of the most popular the sensor-based random tree (SRT) presented by Oriolo, Freda, and Franchi in [11]. This method is based on the random generation of robot configurations within a local security area detected by the robot's sensors, from which a compact tree-type data structure is constructed, which represents the road map of the area explored. In this structure, the leaves represent a previously reached robot position and their respective representation of the environment segment detected by the onboard sensors in that position called local safety region (LSR).

The SRT method randomly selects free borders detected at the current position of the robot where he can continue the exploration task; in case it is not possible to find one, the robot will automatically go to its parent node to look for new areas with exploration possibility. The process ends when the backspace behavior leads the robot to the root of the tree.

However, despite the popularity of the SRT scheme, it has certain problems that should be considered. The first of them lies in the ignorance of the state of the structure that is being built, where it is not possible to know if the nodes of the structure left behind contain more areas available for exploration, and therefore the total coverage of the environment cannot be guaranteed. The second problem depends on the first one, since not knowing which areas of the environment you remain unexplored, it is necessary for the robot to go back to parent nodes to find out if it is possible to continue exploring, which causes the structure to be traveled twice, and consequently the exploration time is very high.

From the above, a new method based on the SRT is developed by Franchi and others [4] for the multirobot case known as the sensor-based random graph (SRG). This method transforms the tree structure generated by the SRT method into an exploration network when the robot finds a safe way to travel between two nodes. In this method, a probability proportional to the length of the arc of the free edges that are in the node in which the robot is located is used to determine which will be the next position to explore; in addition, the way to verify the structure to establish the way to revisit zones already explored to continue the exploration is carried out by means of the generation of a tree of minimum expansion with all the adjacent nodes of the network, choosing that of the adjacent node with the greater weight with respect to the length of the free limits of the frontiers.

The SRG method presents similar problems to those of the SRT method: although the data structure is transformed into an exploration graph, the structure is not fully exploited to make exploration more efficient, because the way of revisiting nodes to verify unexplored areas creates a tree structure, which generates a discontinuous path that forces the robot to go through the parent nodes, ignoring the versatility of the graph. In fact, if the number of adjacent nodes and the number of nodes that conforms the environment are too large, the time to complete the exploration is increased. Also, like the SRT method, the robot decides the next position to explore without considering that the randomness of the selection causes too many orientation changes, which directly affects the odometric system.

More recently, Toriz et al. [12] presented a new approach known as the random exploration graph (REG), which optimizes map coverage in the exploration process. This method is based on the working principle of the SRT method and adapts it to build an exploration graph structure. Although this method has a probabilistic nature that can cause an excess of movements in the robot to complete its task and

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**Figure 1.**

*Extended REG algorithm.*

*Extending the Limits of the Random Exploration Graph for Efficient Autonomous Exploration…*

increase the exploration time, one of its main advantages is the accumulation of knowledge acquired through the concept of border control, which stores information about areas that the robot left behind in the exploration process and that needs to be revisited to complete the exploration. This feature, plus the generated graph structure, allows an optimal return to unexplored areas to complete the exploration. As it can be observed, the methods presented here maintain a random character to define the next position to explore, the problems found in these algorithms are the excess of time required to complete the task, and in some cases the uncertainty on the total coverage of the exploration area, which can have repercussions on partially constructed or low-quality maps, the reason why an integrated exploration

Thus, this work presents an approach to the problem of path planning of unknown environments based on the basic principles of the REG method; however, unlike this method, our proposal eliminates the randomness of the choice of the next frontiers to explore and, instead, relies on an analysis of the best frontier whose choice criterion is based on minimizing the amount of movements the robot has to make to reach it and maximizing the amount of information from the environment that will be acquired.

The exploration strategy presented in this research is a modified version of the REG algorithm, where the main difference lies in the way in which the robot will plan the exploration trajectory by performing a deterministic analysis of the next

*DOI: http://dx.doi.org/10.5772/intechopen.84821*

strategy created from these methods would not be viable.

position to be explored; the algorithm is shown in **Figure 1**.

**2. Extended random exploration graph**

*Extending the Limits of the Random Exploration Graph for Efficient Autonomous Exploration… DOI: http://dx.doi.org/10.5772/intechopen.84821*

increase the exploration time, one of its main advantages is the accumulation of knowledge acquired through the concept of border control, which stores information about areas that the robot left behind in the exploration process and that needs to be revisited to complete the exploration. This feature, plus the generated graph structure, allows an optimal return to unexplored areas to complete the exploration.

As it can be observed, the methods presented here maintain a random character to define the next position to explore, the problems found in these algorithms are the excess of time required to complete the task, and in some cases the uncertainty on the total coverage of the exploration area, which can have repercussions on partially constructed or low-quality maps, the reason why an integrated exploration strategy created from these methods would not be viable.

Thus, this work presents an approach to the problem of path planning of unknown environments based on the basic principles of the REG method; however, unlike this method, our proposal eliminates the randomness of the choice of the next frontiers to explore and, instead, relies on an analysis of the best frontier whose choice criterion is based on minimizing the amount of movements the robot has to make to reach it and maximizing the amount of information from the environment that will be acquired.
