Clear and smooth path planning

Davoodi, Mansoor; Panahi, Farhad; Mohades, Ali; Hashemi, Seyed Naser

doi:10.1016/j.asoc.2015.04.017

Cited by 57 publications

(37 citation statements)

References 17 publications

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“…We therefore do not consider this group in the simulations. For the other two groups (i.e., heuristic and probabilistic algorithms) we chose GAMOPP (multi-objective path planning) (Davoodi et al, 2015), RRT (Kala, 2014b) and PRM (Kala, 2014a).…”

Section: Resultsmentioning

confidence: 99%

“…Alternatively, SPP produces a single path for each problem. Davoodi et al (2015) highlight that sometimes MOPSO is not able to find any feasible solution in 100 generations, especially when there is a narrow passage framework, or numerous obstacles have cluttered the environment, but GAMOPP always provides a feasible solution per each run. Therefore, we select GAMOPP presented by Mohanta et al (2011) and compare its results with SPP in terms of running time and quality of solutions.…”

Section: Resultsmentioning

confidence: 99%

“…To have a clear comparison between these algorithms, we use the same test problems from the work of Davoodi et al (2015) to evaluate the performance of the SPP algorithm. These four cases examine the capabilities of each algorithm under difference scenarios.…”

Section: Resultsmentioning

confidence: 99%

“…These algorithms, usually find a solution close to the optimal one and they find it fast. Among the heuristic algorithms, genetic algorithms (GAs) (Davoodi et al, 2015;Jafarzadeh et al, 2017), artificial neural networks (ANNs) (Ni et al, 2017), and ant colony optimization (ACO) (Liu et al, 2017) have been the most popular for solving path planning problems (Masehian and Sedighizadeh, 2007). These algorithms take into account different objectives at the same time and can be adjusted for a wide variety of problems.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Exact Geometry–Based Algorithm for Path Planning

Jafarzadeh

Fleming

2018

International Journal of Applied Mathematics and Computer Science

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A novel, exact algorithm is presented to solve the path planning problem that involves finding the shortest collision-free path from a start to a goal point in a two-dimensional environment containing convex and non-convex obstacles. The proposed algorithm, which is called the shortest possible path (SPP) algorithm, constructs a network of lines connecting the vertices of the obstacles and the locations of the start and goal points which is smaller than the network generated by the visibility graph. Then it finds the shortest path from start to goal point within this network. The SPP algorithm generates a safe, smooth and obstacle-free path that has a desired distance from each obstacle. This algorithm is designed for environments that are populated sparsely with convex and nonconvex polygonal obstacles. It has the capability of eliminating some of the polygons that do not play any role in constructing the optimal path. It is proven that the SPP algorithm can find the optimal path in O(nnr2) time, where n is the number of vertices of all polygons and n ̓ is the number of vertices that are considered in constructing the path network (n ̓ ≤ n). The performance of the algorithm is evaluated relative to three major classes of algorithms: heuristic, probabilistic, and classic. Different benchmark scenarios are used to evaluate the performance of the algorithm relative to the first two classes of algorithms: GAMOPP (genetic algorithm for multi-objective path planning), a representative heuristic algorithm, as well as RRT (rapidly-exploring random tree) and PRM (probabilistic road map), two well-known probabilistic algorithms. Time complexity is known for classic algorithms, so the presented algorithm is compared analytically.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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An Exact Geometry–Based Algorithm for Path Planning

Jafarzadeh

Fleming

2018

International Journal of Applied Mathematics and Computer Science

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“…Since the framework of EAs is independent of the properties of search space and optimisation function(s), they have been extensively applied to many complex problems in operations research, as well as industrial engineering problems such as storage allocation in order picking operations (Bottani et al 2012), assembly and disassembly (Galantucci, Percoco, and Spina 2004), supply chain distribution problem (Jawahar and Balaji 2009), motion planning (Davoodi et al, 2015), facility location (Davoodi, Mohades, and Rezaei 2009) and supply chain management (Min 2010). The key idea in EAs is to combine the exploration of the search space with the exploitation of the solutions that were obtained, which has a very important impact on the efficiency of both activities.…”

Section: Multi-objective Eamentioning

confidence: 99%

A multi-objective model for lot-sizing with supplier selection for an assembly system

Rezaei

Davoodi

Tavasszy

et al. 2015

International Journal of Logistics Research and Applications

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Lot-sizing with supplier selection (LS-SS) is a fast-growing offspring of two major problem parents in logistics and supply chain management ('lot-sizing' and 'supplier selection'). The model proposed in this paper is an attempt to extend it to an assembly system, by formulating a multi-objective model for an integrative problem of LS-SS for assembly items. The total costs of the system, consisting of purchasing, ordering, transportation, assembly, and holding, is considered the first objective function, while the total reliability of the finished products is considered the second objective function. The decision-maker aims to minimise the total costs while maximising the total reliability. Several constraints of the system (e.g. storage capacity, supplier production capacity) are taken into account. Given the complexity of the model, a heuristic evolutionary algorithm is proposed to solve the model. The results indicate which assembly items to order in which quantities, from which suppliers and in which time periods.

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A multi‐objective approach for local path planning of autonomous mobile robot based on metaheuristics

Pattnaik

Panda

Mishra

2021

Concurrency and Computation

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The traditional methods of mobile robot (MR) path planning very often fail in finding the optimal path in a cluttered, complex, and unknown environment. Therefore, in this article, a novel constrained multi-objective function involving route length, smoothness of the path, and path safety is formulated for a disc-shaped MR integrated with sensors. The imposed constraints to the problem are the size of the robot and the collision-free path. A new population-based algorithm called the chemical reaction optimization algorithm is used to solve the path planning problem. The results obtained through the proposed controller are compared with the results of other methods available in the literature. To demonstrate the effectiveness and usefulness of the proposed algorithm in finding the solution to the path planning problem, statistically significant tests, that is, the coefficient of variation analysis and Friedman's test are conducted.It is observed that the proposed method outperforms the state-of-the-art methods in terms of route length, path smoothness, average sensing, and planning time, and time is taken to reach the goal.

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Clear and smooth path planning

Cited by 57 publications

References 17 publications

An Exact Geometry–Based Algorithm for Path Planning

An Exact Geometry–Based Algorithm for Path Planning

A multi-objective model for lot-sizing with supplier selection for an assembly system

A multi‐objective approach for local path planning of autonomous mobile robot based on metaheuristics

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