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Robot trajectories comparison: a statistical approach.

Ansuategui A, Arruti A, Susperregi L, Yurramendi Y, Jauregi E, Lazkano E, Sierra B - ScientificWorldJournal (2014)

Bottom Line: Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them.Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided.The proposed method has been applied, as an example, to compare two different motion planners, FM(2) and WaveFront, using different environments, robots, and local planners.

View Article: PubMed Central - PubMed

Affiliation: Autonomous and Smart Systems Unit, IK4 Tekniker, Eibar, Spain.

ABSTRACT
The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM(2) and WaveFront, using different environments, robots, and local planners.

Show MeSH
Pentagon like comparisons.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: Pentagon like comparisons.

Mentions: Therefore, what is important to take into account is the surface obtained (both in area and in shape) after the five edge values are given. Figure 5 shows several pentagon shapes that can be obtained depending on the results of the comparison. Hence, the pentagon should appear clean if no differences are identified (Figure 5(a)), full signigicant differences appear in all the variables (Figure 5(b)), and the different shapes it can take depending on the source of the differences, as shown in Figures 5(c), 5(d), 5(e), and 5(f).


Robot trajectories comparison: a statistical approach.

Ansuategui A, Arruti A, Susperregi L, Yurramendi Y, Jauregi E, Lazkano E, Sierra B - ScientificWorldJournal (2014)

Pentagon like comparisons.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4262753&req=5

fig5: Pentagon like comparisons.
Mentions: Therefore, what is important to take into account is the surface obtained (both in area and in shape) after the five edge values are given. Figure 5 shows several pentagon shapes that can be obtained depending on the results of the comparison. Hence, the pentagon should appear clean if no differences are identified (Figure 5(a)), full signigicant differences appear in all the variables (Figure 5(b)), and the different shapes it can take depending on the source of the differences, as shown in Figures 5(c), 5(d), 5(e), and 5(f).

Bottom Line: Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them.Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided.The proposed method has been applied, as an example, to compare two different motion planners, FM(2) and WaveFront, using different environments, robots, and local planners.

View Article: PubMed Central - PubMed

Affiliation: Autonomous and Smart Systems Unit, IK4 Tekniker, Eibar, Spain.

ABSTRACT
The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM(2) and WaveFront, using different environments, robots, and local planners.

Show MeSH