**3. The problem of path planning developed for area monitoring**

#### **3.1 Differences between effectiveness and efficiency**

The overall goal of planning and optimizing routes is to solve practical problems in order to achieve the intended goal. This can focus only on one problem like transportation, e.g. the traveling salesman problem (TSP), in which the previous abovementioned logic can be continued, but the purpose can also be to select the path to optimize the observation of a particular area. Author calls it as the effective

**105**

*Path Planning Optimization with Flexible Remote Sensing Application*

the patrol can also be replaced by an autonomous system.

the above, efficiency can be approached from several sides:

effective; however, we do not take its costs in account.

meaning that using autonomous system is efficient.

point of view, the latest author takes it in account too.

both of them increase proportionally.

**3.2 Path planning is effective in professional point of view**

patrolling path problem (E3P), where the selected area is under observation by the staff who make its supervision by continuous patrol. In this assumption the staff of

Obviously, the purpose of observing an area is to detect a particular event or phenomenon as early as possible. Early detection prevents the escalation of many unwanted events, for example, CCTV camera used for criminal prevention [20], aerial surveillance for forest fire detection [21, 22], or disaster escalation [23, 24]. The effectiveness of the prevention correlates to the early detection. The problem is that the patrol can see only a limited part of the supervised area, so the entire area can be divided in time into observed and not observed parts. However, the event or phenomenon can be noticed not only by the patrol but also by any other person in the area. Therefore, it is questionable who will be the first to detect the event, the patrol in duty or any other person spontaneously. This question focuses not only on the effectiveness of the applications but also on the efficiency of the autonomous systems. Patrol is costly, while spontaneous detection has virtually no cost. From

1.Performing the patrol, the average detection time is shorter than without patrol. In this case the autonomous system used for patrol is professionally

2.Performing the patrol, the average detection time is shorter than without patrol; moreover, the costs of patrol will return. The latter means that the faster the detection of the event, the faster the response of the dedicated service to the event or phenomenon, which can raise the amount of the saved value or reduce the loss of the damage. In this case the saved value or the reduced damage balances or overtakes the total costs of patrolling. At this point, the application is effective not just professionally but even economically,

3.The costs of patrolling can be reduced significantly while its benefit remains. In this case we are looking for different methods to further increase efficiency within a given budget, to make the use of limited resources more efficient.

Each of the above approaches requires different analyses to understand how to optimize autonomous systems with remote sensing application. Since the optimization in the reality means not only the mathematical solution but also the economical

Previously it has been clarified that the purpose of patrol is to detect an incident or phenomenon earlier than it would be performing by other sources. Professional efficiency does not count with anything else, just to make the signal faster with a new system than without it. If the average signals performed by autonomous system are faster than without it, then the autonomous system is efficient from a professional point of view. It is logical that, with increasing number of people present in a given area, the frequency and quickness of the report will increase statistically. The dispersion of signals from the larger population over time is broader; however, only one of the extreme values of the scatter is required, which is manifested by faster detection. Recognizing this, it can be concluded that the quickness of the report depends both on the number of people present and the population density of the area; moreover,

*DOI: http://dx.doi.org/10.5772/intechopen.86500*

## *Path Planning Optimization with Flexible Remote Sensing Application DOI: http://dx.doi.org/10.5772/intechopen.86500*

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

Based on the above, it can be observed that in natural conditions there can be a

*An example for path planning between points of "A" and "B" in case of more options with pathway and with* 

If we assume more than two points to be touched in the course of the route, we find that the number of options increases dramatically again as shown in **Figure 3**. Optimizing a multipoint path planning in a given plane area raises the travelling salesman problem (TSP) that has already been examined by many studies. At elementary level this question is raised hundreds years ago together with the trade development; however, as a classic scientific problem, it was mentioned firstly by Held and Karp [7] and Bellman [8]. They used dynamic programming approaches to find the solution. Later other researchers followed these methods [9, 10], and others developed new ones offering other algorithms as well as time-dependent TSP [11] or TSP with

As new technologies appear in autonomous systems, like unmanned aerial vehicles (UAV) or drone applications, researchers found new problems and tried to give new approaches finding the best or optimized solution. Some of these studies focus only on the drone applications; others tried to combine drone application with the traditional delivery system [13, 14]. In these studies authors used different examining methods, as well as exact methods [15], heuristic methods [16–18], or approximation algorithm [14]. Bouman et al. cites a detailed summary about the

Based on the above, TSP is a very complex therefore not just natural condition, but also some idealistic assumption can generate a significant number of solutions.

The overall goal of planning and optimizing routes is to solve practical problems

**3. The problem of path planning developed for area monitoring**

in order to achieve the intended goal. This can focus only on one problem like transportation, e.g. the traveling salesman problem (TSP), in which the previous abovementioned logic can be continued, but the purpose can also be to select the path to optimize the observation of a particular area. Author calls it as the effective

**3.1 Differences between effectiveness and efficiency**

significant number of solutions even between two points.

**2.2 Path planning problems between several points**

time window and precedence constraints [12].

above researches and results [19].

**104**

**Figure 3.**

*intermediate points.*

patrolling path problem (E3P), where the selected area is under observation by the staff who make its supervision by continuous patrol. In this assumption the staff of the patrol can also be replaced by an autonomous system.

Obviously, the purpose of observing an area is to detect a particular event or phenomenon as early as possible. Early detection prevents the escalation of many unwanted events, for example, CCTV camera used for criminal prevention [20], aerial surveillance for forest fire detection [21, 22], or disaster escalation [23, 24]. The effectiveness of the prevention correlates to the early detection. The problem is that the patrol can see only a limited part of the supervised area, so the entire area can be divided in time into observed and not observed parts. However, the event or phenomenon can be noticed not only by the patrol but also by any other person in the area. Therefore, it is questionable who will be the first to detect the event, the patrol in duty or any other person spontaneously. This question focuses not only on the effectiveness of the applications but also on the efficiency of the autonomous systems. Patrol is costly, while spontaneous detection has virtually no cost. From the above, efficiency can be approached from several sides:


Each of the above approaches requires different analyses to understand how to optimize autonomous systems with remote sensing application. Since the optimization in the reality means not only the mathematical solution but also the economical point of view, the latest author takes it in account too.
