Date of Award

27-5-2024

Document Type

Thesis

School

School of Computing

Programme

Ph.D.-Doctoral of Philosophy

First Advisor

Dr.S.Venugopal

Second Advisor

Dr.R.Venkatesan

Keywords

Multi-Objective Genetic Algorithm, Fuzzy TOPSIS, PARETO Optimization, Artificial Potential Field Algorithm, Three Dimensional Path Planning, Layered Approach

Abstract

Mobile Robot Path Planning problem (MRPPP) is the most prominent research area employed in different real-time environments. The research domain of robotics offers abundant opportunities for researchers in various engineering fields with different dimensions. The automation of physical movements of the robots with intelligence to make dynamic decisions for interacting with the environment opens up a lot of challenges to the research community.

A variety of approaches are employed to solve the Mobile Robot Path Planning Problem (MRPP), which is to derive a feasible collision-free path to reach the destination from the given starting point by avoiding obstacles. The solution for the MRPP is not only influenced by the physical design of the robot but also by the environment it explores. In the field of mobile robot path planning, a lot of research has been done on both static and dynamic working environments. The determination of an optimal path from the start to the target point, while avoiding collisions in mobile robot path planning with the obstacles in an environment is an NP-hard problem.

Various traditional approaches for solving the path planning problem have been developed using deterministic and non-deterministic approaches. The optimal path, if it exists, is guaranteed by deterministic techniques. However, as the complexity of the working environment rises, so does the time complexity for the techniques. On the other hand, non-deterministic approaches are developed to produce optimal paths with constraints. The principles of the Genetic Algorithm, with many modifications and hybridization with other algorithms, are frequently used to select an optimal path for robot navigation. The major challenges are identified and addressed to explore the solution.

a. The GA is a well-accepted evolutionary algorithm for the MRPP, which requires a large initial population to predict the quality of the path. However, because of the computational complexity, the performance is reduced. In the Environment Specific Strategy(ESS), the performance is improved by reducing computational cost which is achieved by determining the initial population size. Hence the proposed methodology is adapted to address this issue by prescribing three strategies decided based on the density of the obstacles present in the environment.

b. The specific goals of the research work is not only the length of the path is considered as the prime objective as commonly suggested objective, but the smoothness of the path and safety of the path is also considered to improve the quality of the path. This objective is achieved by hybridizing APF and MOGA, exploiting the advantage of both methods and overcoming the drawbacks. In addition to that the derived path is smoothened by applying the three-phase technique.

c. When in the multi-objective case, it is very difficult to customize the level of preferences for the different objectives based on the demand for the environment. The GA technique with a single primary objective is used as the preprocessing technique to apply Fuzzy TOPSIS. Decision makers state the level of preference by linguistic variables rather than absolute values when using the Fuzzy TOPSIS approach.

d. The MOGA approach is applied to determine the optimal path is very complicated for three-dimensional environments. The proposed 3DLAP-MOGA is accomplished by constructing an occupancy matrix for the 3D space and employing MOGA. The methodology 3DLAP-4OGA is additionally considering vertical movement as one of the objectives which give more quality optimal path.

e. The MRPS-MOGA is another methodology proposed where the objectives are added to consider the distance from the obstacles while traversing, the number of obstacles that interacted with the robot and the traveling time in addition to the distance traveling.

So the objectives for the research work are consolidated to address the issues in the MRPP problem as reducing the computational cost, improving the quality of the optimal path, provision to customize the preference levels of objectives and extending the MOGA methodology to 3D space. The suggested ESS has undergone two stages of analysis and proof in order to both quantitatively and qualitatively support the results. In the case of quantitative analysis, the size of the population is compared for the various environments and analyzed which directly influences the computational cost. In qualitative analysis, the length of the path is considered as a parameter to be compared to determine the quality of the paths obtained by the different strategies.

In the proposed DPA, the average of objective values of paths produced by the proposed algorithm is compared with well-known algorithms such as A*, Dijkstra, EGA(enhanced GA) and APF for 8 distinct maps. The findings indicate that the suggested hybrid algorithm is delivering superior outcomes at the level of the individual objectives. The fuzzy-based TOPSIS method was implemented and analyzed with different combinations of the preference level of DM. Then for the quality confirmation of the proposed method, best and worst cases are analyzed for the different pref levels. In addition to that the computational advantage of the proposed methodology is proved by comparing the computational score of GA, Fuzzy TOPSIS and hybridized algorithm.

In the 3DLAP-MOGA technique, Cumulative Normalized Performance Score (CNPS) and Cumulative Normalized Environment Score (CNES) are two scales that are used to measure performance with respect to environmental parameters. To justify the above results another metric is called assessment point. In the extension of this method, 3DLAP-4OGA, the results are analyzed using z-score estimation for standardization which indicates the deviation from the mean. The greater performance was illustrated by the MRPS-MOGA approach from the results implementing the method. The different parameters are analyzed to prove the performance of the methodology. The experimental results of these proposed methodologies revealed that the above approaches are useful for finding an optimal feasible collision-free path for the given environment.

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