Mobile Robot Path Planning in Dynamic Environments

Wang, Yang; Sillitoe, Ian Pw; Mulvaney, David

doi:10.1109/robot.2007.363767

Cited by 26 publications

(18 citation statements)

References 19 publications

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“…The basic idea of the proposed dynamic module is to generate a virtual static obstacle for the dynamic obstacle, which is based on the movement parameters of the moving obstacle. The basic work flow of the dynamic module is as follows [25], [26]: (1) when a moving obstacle is observed, the trajectory of this moving obstacle is predicted based on its movement parameters and (2) if the robot collides with the moving obstacle, the path of the robot will be planned again based on the trajectory and the direction of the moving obstacle. A schematic diagram of this working process for robot path planning in dynamic environment is shown in Fig.…”

Section: The Dynamic Modulementioning

confidence: 99%

A Memetic Algorithm With Variable Length Chromosome for Robot Path Planning Under Dynamic Environments

Ni¹,

Wang²,

Cao³

et al. 2017

International Journal of Robotics and Automation

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There are some shortcomings of the traditional methods for robot path planning, such as the local minimum problem and the low convergence speed in the genetic algorithm (GA)-based methods.In this paper, an improved memetic algorithm is proposed for robot path planning. In the global search process of the proposed memetic algorithm, a GA with variable length chromosome based on improved two-point crossover and bacterial mutation is used to avoid the local optimum problem. And a search method which combines a neighbouring local search and a disorder strategy is used in the local search process of the proposed approach to speed up the convergence procedure. Furthermore, a dynamic module is added in the proposed approach to deal with the path planning in dynamic environments. Finally, some simulation and real robot experiments are carried out and the experimental results show the efficiency and the effectiveness of the proposed algorithm in the path planning of mobile robots.

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Section: The Dynamic Modulementioning

confidence: 99%

A Memetic Algorithm With Variable Length Chromosome for Robot Path Planning Under Dynamic Environments

Ni¹,

Wang²,

Cao³

et al. 2017

International Journal of Robotics and Automation

View full text Add to dashboard Cite

show abstract

“…Wang et al [18] have presented a GA planner to determine optimal or nearoptimal path solutions for mobile robots in dynamic environments. A hybrid GA technique is also employed to deal with multicriteria-multistage path planning [19].…”

Section: Related Work On Dynamic Path Planningmentioning

confidence: 99%

“…As an effective tool for solving optimization problems, the GA algorithm, a popular evolutionary computation algorithm, has been investigated for dynamic path planning [18,19]. However, when dealing with complex environments, GA based approaches consume significant time [18], thus limiting their applications in many practical systems.…”

Section: Motivations Of This Workmentioning

confidence: 99%

“…For example, in order to guide a robot to avoid any potential collisions with obstacles, the GA method in Wang, et al [18] assumes that an adequately large time interval is allowed between the detection of obstacle movements and the implementation of newly generated actions. The corresponding results [18] show that for an environment with 14 static obstacles and 5 dynamic obstacles, about 10 seconds are required to find the first feasible path.…”

Section: Motivations Of This Workmentioning

confidence: 99%

“…2, a mathematical model can be established for calculating the possibility of clashing of the robot with the moving obstacle [18]. The first crossing point of the current robot path and the predicted trajectory of the moving obstacle in Fig.…”

Section: Architecture Of the Enhanced Sa Approachmentioning

confidence: 99%

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