Mobile robot path planning with surrounding point set and path improvement

Han, Jihee; Seo, Yoonho

doi:10.1016/j.asoc.2017.03.035

Cited by 110 publications

(63 citation statements)

References 18 publications

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“…Han and Seo dealt with a complex environment using SPS and PI_FLP for node generation and generate the points in optimal space. The SPS algorithm generated the node and finalized without any variation in the solution, unless environment itself changed; moreover, PI_FLP algorithm is used to find the velocities and identified the optimal point in related space, which is having more obstacle.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SPS algorithm generated the node and finalized without any variation in the solution, unless environment itself changed; moreover, PI_FLP algorithm is used to find the velocities and identified the optimal point in related space, which is having more obstacle. Finally, the optimal path has been planned to a disaster place and ensured safety in rovers involved in path identification …”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, the optimal path has been planned to a disaster place and ensured safety in rovers involved in path identification. 22 Huang and Savkin 23 proposed the shortest feasible path planning algorithm for path planning robots in a field with many obstacle. They formulated the problem in a shortest path planning for a single robot with variant DTSPN and further considered the shortest feasible path planning for multiple robot that presented k-shortest feasible path planning algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Establishing an emergency communication network and optimal path using multiple autonomous rover robots

Sakthitharan¹,

Jayashri²

2018

Concurrency and Computation

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Summary The natural calamity or disaster may destroy all communication networks especially a cellular network that relies on a tower. Although many solutions to an ad hoc wireless network have been proposed, forming a network covering a respective region with mobile robots toward optimal coverage remains to be an open problem. In this paper, we take the initiative to handle the optimal network coverage and path selection in disaster region with the help of multiple movable/rover robots. This paper consists of load balance distribution algorithm and optimal coverage algorithm applied to find the next optimally possible node location for all robots. Next, the robots maneuvering in an unknown disaster environment to identify the optimal path between the source and destination by using a particle swarm optimization algorithm. Finally, simulated results show that the algorithms can significantly improve the network coverage in the entire region, and the optimal path can effectively identify the optimal solution for all rover robots.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Establishing an emergency communication network and optimal path using multiple autonomous rover robots

Sakthitharan¹,

Jayashri²

2018

Concurrency and Computation

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“…Path planning task is often modeled as an optimization problem, where a decision variable represents a given path, i.e., the sequence of points (or movements) by which the robot must move; the cost function is a certain criteria or metric whose value is optimized (e.g., distance, energy consumption, and execution time). Thus, various optimization techniques have been applied to solve path planning problems, e.g., genetic algorithm (GA) [5]- [7], A* [8], particle swarm optimization (PSO) [9], nonlinear programming (NLP) [10], and ant colony [11]. However, these optimization techniques are unable to ensure the global optimality of the robot path, although they are able to provide results sufficiently fast for on-line path planning applications.…”

Section: Introductionmentioning

confidence: 99%

Counterexample guided inductive optimization applied to mobile robots path planning

Araujo

Ribeiro

Bessa

et al. 2017

2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR)

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We describe and evaluate a novel optimization-based off-line path planning algorithm for mobile robots based on the Counterexample-Guided Inductive Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers, in order to guide the optimization process and to ensure global optimization. This paper marks the first application of those solvers for planning mobile robot path. In particular, CEGIO has been successfully applied to obtain optimal two-dimensional paths for autonomous mobile robots using off-the-shelf SAT and SMT solvers.

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“…Because of the convex polygons are more concise and convenient in the point of tangency, intersection and judgment of whether the obstacles are in the polygons or not and so on environment planning algorithm expression into a convex polygon obstacle. The principle of constructing obstacles into convex polygon in planning algorithm is that giving priority to fill a vacancy if it does not pose a threat to the precision of path planning for the case [4,5]. If obstacles in the depth of the underwater robot work area are large and filling a vacancy of convex polygon will have an impact on the precision of path planning then using split method.…”

Section: Environmental Expressionmentioning

confidence: 99%

Path Planning Method in Multi-obstacle Marine Environment

Zhang

Sun

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. In this paper, an improved algorithm for particle swarm optimization is proposed for the application of underwater robot in the complex marine environment. Not only did consider to avoid obstacles when path planning, but also considered the current direction and the size effect on the performance of the robot dynamics. The algorithm uses the trunk binary tree structure to construct the path search space and A * heuristic search method is used in the search space to find a evaluation standard path. Then the particle swarm algorithm to optimize the path by adjusting evaluation function, which makes the underwater robot in the current navigation easier to control, and consume less energy. IntroductionPath planning is one of the most important tasks in underwater robot navigation, and it represents a degree of intelligence on underwater robots. The overall path planning consists of three parts: 1. Model the environmental information of the robot in relation to the environment.2.Get a search space that contains environmental information.3.Search in the search space using the corresponding search algorithm [1]. Before the underwater robot path planning, it must first model environment, described the underwater robot activity space by the external environment of the original form through a series of processing into a suitable planning model of the algorithm. A reasonable environment is conducive to reducing the amount of search in the plan and the overhead of space and time [2]. Path search algorithm search the feasible space of the path from the environment model, while the path generated from search to the path of the feasible to generate a feasible path in the space. Path optimization is considering basis dynamic characteristics of the intelligent underwater robot, in order to let more conducive to the implementation of underwater robot path for a smooth path [3].

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Mobile robot path planning with surrounding point set and path improvement

Cited by 110 publications

References 18 publications

Establishing an emergency communication network and optimal path using multiple autonomous rover robots

Establishing an emergency communication network and optimal path using multiple autonomous rover robots

Counterexample guided inductive optimization applied to mobile robots path planning

Path Planning Method in Multi-obstacle Marine Environment

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