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Peer-Review Record

Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

Electronics 2019, 8(3), 306; https://doi.org/10.3390/electronics8030306

by Franklin Samaniego^*

, Javier Sanchis

, Sergio García-Nieto

and Raúl Simarro

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Electronics 2019, 8(3), 306; https://doi.org/10.3390/electronics8030306

Submission received: 28 December 2018 / Revised: 22 February 2019 / Accepted: 5 March 2019 / Published: 8 March 2019

(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)

Round 1

Reviewer 1 Report

The paper is about path planning for UAV in 3D environment using 'Recursive Rewarding' modified adaptive cell decomposition.

There are several points that the paper should not be published as it is.

There is no section for literature review. if the authors do not want to have the section separately, then the background literature may mentioned in introduction section. There are many papers related to 'path-planning' (I can even find 6 papers published in 2018 in Electronics journal) . I didn't mean that the authors did not present any background, I mean the background should be improved for properly addressing the research.

Basically, it would be very simple to have specific path-planning if UAV does not have restriction of flight altitude. a UAV can directly fly from origin to destination if there is no obstacle (case of high flight altitude). Thus, it would be better what specific restriction should be apply to have such type problem.(ex. the low flight of mountain area)

I could not find the UAV means fixed wing type until conclusion section. It should be explained in, at least, problem definition section.

As I understood, the objective of the model is to minimize the travel distance. If it is right, be more specific to present the model.

It is natural that smaller boxes generated by decomposition produce would drive shorter trajectory of UAV traveling to certain destination

How the authors guarantee that the proposed method generate 'optimal' path.

Table 3 is not quite clear to understand. Any distance comparison? the size of voxel is identical in all problems?

The problem instances are limited. It would be better if the author can test different type of instances for supporting the result reliability of proposed method

Author Response

Point 1: There is no section for literature review. if the authors do not want to have the section separately, then the background literature may mentioned in introduction section. There are many papers related to 'path-planning' (I can even find 6 papers published in 2018 in Electronics journal) . I didn't mean that the authors did not present any background, I mean the background should be improved for properly addressing the research.

Response 1: Although an additional "Literary Review" section has not been added, new content has been inserted into the Introduction section. Two new tables have been inserted to complete this background literature. Table 1 summarizes the literature review studied in relation to the work of 3D path planning and Table 2 summarizes the computational complexity of the revised methodologies. Manuscript have been updated over the course of pages 1, 2, and 3. From line 11 to 70.

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Point 2: Basically, it would be very simple to have specific path-planning if UAV does not have restriction of flight altitude. a UAV can directly fly from origin to destination if there is no obstacle (case of high flight altitude). Thus, it would be better what specific restriction should be apply to have such type problem.(ex. the low flight of mountain area).

Response 2: That's right, thank for your recommendation. In that way, a defined 3D flight environment has been specified into an urban environment with buildings, introduced on the lines (100-111). In the same way, a new environment has been added (section 5.1, table 4) and some content has been added, describing an experimental environment full of buildings with a defined height, according to the Spanish UAV flight regulations (lines 310-313).

Point 3: I could not find the UAV means fixed wing type until conclusion section. It should be explained in, at least, problem definition section.

Response 3: The following modifications have been done in order to clarify this issue. The specific UAV flight and trajectory tracking capability has been clarified in lines (106-111). Furthermore, in section 6, lines 374-376, future works and other types of UAVs in which the paper proposal could be deployed are mentioned.

Point 4: As I understood, the objective of the model is to minimize the travel distance. If it is right, be more specific to present the model.

Response 4: The main aim is not the minimization of travel distance. Some clarification about this concept has been inserted (lines 65-70). Achieving a minimal distance could be an effect of the algorithm in different instances. Nevertheless, assigning a higher probability pvalue to the corresponding distance function R(m) could return a better response in terms of minimal distance traveled.

Point 5: It is natural that smaller boxes generated by decomposition produce would drive shorter trajectory of UAV traveling to certain destination How the authors guarantee that the proposed method generate 'optimal' path.

Response 5: A paragraph has been added (lines 286-290) for clarification purposes about this comment.

Point 6: How the authors guarantee that the proposed method generate 'optimal' path.

Response 6: Equation 15 determines the best option for movement to the next state (x) (3D spatial point). Once the voxel set in the current 3D adaptive cells decomposition is generated, the voxel that meets better the flight conditions R(m), m=1...N, is added to the set of optimal nodes (ρx(F)) in the final path search (lines 253-266). On the other hand, the aim of the paper is detailed in the lines (65-70).

Point 7: Table 3 is not quite clear to understand. Any distance comparison? the size of voxel is identical in all problems?

Response 7: Table 3 is the state model representation described in figure 8 (consequence of the first 3D adaptive cells decomposition). All mlevels of table 3 are constructed with the same state model, however, each mlevel is different from the previous ones due to each R(m)function associated with the the flight characteristics (section 4.1). First function, m=1,g(R(1))defines the cost in terms of distance from one state to the next. Finally, as a subsequent step, two transition priority vectors stemming from Table 3, will determine the next node during the searching process of the best path.

Since the environments can be defined with different sizes, consequently the decomposed voxels will have different sizes in each case. In lines 224-229 of section 4.2, the initial environment conditions has been detailed. Furthermore, new information has been added detailing results in terms of distance in Table 6 and new explanations has been written in lines 346-348.

Point 8: The problem instances are limited. It would be better if the author can test different type of instances for supporting the result reliability of proposed method.

Response 8: A new scenario has been added in Table 4 of Section 5, where an environment with several buildings is presented, (explained in lines 310-313). A new figure has been added, showing this new scenario (Figure 10(a)), as well as the algorithm results (Figure 10(b)). On the other hand, numerical results of this new experiment have been added to Tables 5 and 6.

Author Response File: Author Response.pdf

Reviewer 2 Report

The subject and methodology is interesting and novel. 3 suggestions are as follows:

1. For application purpose, most of the flight spaces consist of continuous 3D topographic terrain with sparse objects, e.g. building, tree, etc. The authors evaluate MACD and RRMACD with isolated obstacles, which is not consistent with most real-world scenarios, e.g. continuous 3D surface or 2D linear obstacles pipe line, etc. For the methodology evaluation, the condition with simplified 1D point obstacles is acceptable. However the limitation of the conditions should be good to address.

2. Line 299-302, the authors argue that the study vehicle is a fixed wing, but it is not the case, since most of the fixed with is not capable to ascending/descending vertically, regarding to Fig 11. The authors not necessary to mention that the “fixed wing” is used in this study.

3. Line 299-302, if rotor-wing used, the hovering option is an acceptable option. Thus, for the further study, the time domain with dynamic obstacles is another topic that may applicable for the next study.

Author Response

Point 1: For application purpose, most of the flight spaces consist of continuous 3D topographic terrain with sparse objects, e.g. building, tree, etc. The authors evaluate MACD and RRMACD with isolated obstacles, which is not consistent with most real-world scenarios, e.g. continuous 3D surface or 2D linear obstacles pipe line, etc. For the methodology evaluation, the condition with simplified 1D point obstacles is acceptable. However the limitation of the conditions should be good to address.

Response 1: That's right, thank for your recommendation. In that way, a defined 3D flight environment has been specified into an urban environment with buildings, introduced on the lines (100-111). In the same way, a new environment has been added (section 5.1, table 4) and some content has been added, describing an experimental environment full of buildings with a defined height, according to the Spanish UAV flight regulations (lines 310-313).

Point 2: Line 299-302, the authors argue that the study vehicle is a fixed wing, but it is not the case, since most of the fixed with is not capable to ascending/descending vertically, regarding to Fig 11. The authors not necessary to mention that the “fixed wing” is used in this study.

Response 2: The following modifications have been done in order to clarify this issue. The specific UAV flight and trajectory tracking capability has been clarified in lines (106-111). Furthermore, in section 6, lines 374-376, future works and other types of UAVs in which the paper proposal could be deployed are mentioned.

Point 3: Line 299-302, if rotor-wing used, the hovering option is an acceptable option. Thus, for the further study, the time domain with dynamic obstacles is another topic that may applicable for the next study.

Response 3: Regarding your suggestion, since our work is focused in fixed wings UAVs which do not support this alternative, we include this topic for future research.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Authors have accordingly responded reviewer's comments.

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Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

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