Geodesy

Evolutionary algorithm

Membrane computing

Robot

Algorithm

Benchmark (surveying)

Pure mathematics

Motion planning

Field (mathematics)

Set (abstract data type)

Any-angle path planning

Computer science

Mathematical optimization

Geology

Evolutionary computation

Computer network

Physics

Mathematics

Genetic algorithm

Geography

Potential field

Mobile robot

Path (computing)

Artificial intelligence

Programming language

Geophysics

Machine learning

Applied Soft Computing

In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.

Mobile robot path planning using membrane evolutionary artificial potential field