Cooperative genetic algorithms: a new approach to solve the path planning problem for cooperative robotic manipulators sharing the same work space

The present paper applies a heuristic and genetic algorithms approaches to the path planning problem for Unmanned Aerial Vehicles (UAVs), during an emergency landing, without putting at risk people and properties. The path re-planning can be caused by critical situations such as equipment failures or extreme environmental events, which lead the current UAV mission to be aborted by executing an emergency landing. This path planning problem is introduced through a mathematical formulation, where all problem constraints are properly described. Planner algorithms must define a new path to land the UAV following problem constraints. Three path planning approaches are introduced: greedy heuristic, genetic algorithm and multi-population genetic algorithm. The greedy heuristic aims at quickly find feasible paths, while the genetic algorithms are able to return better quality solutions within a reasonable computational time. These methods are evaluated over a large set of scenarios with different levels of diffculty. Simulations are also conducted by using FlightGear simulator, where the UAV’s behaviour is evaluated for different wind velocities and wind directions. Statistical analysis reveal that combining the greedy heuristic with the genetic algorithms is a good strategy for this problem.

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A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

The Scientific World JOURNAL ◽

10.1100/2012/583973 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 18

Author(s):

Gaige Wang ◽

Lihong Guo ◽

Hong Duan ◽

Heqi Wang ◽

Luo Liu ◽

...

Keyword(s):

Path Planning ◽

Optimization Problem ◽

Cuckoo Search ◽

Planning Problem ◽

Three Dimension ◽

New Approach ◽

Hybrid Metaheuristic ◽

Air Vehicle ◽

Flight Route ◽

Path Planning Problem

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

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A Bat Algorithm with Mutation for UCAV Path Planning

The Scientific World JOURNAL ◽

10.1100/2012/418946 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 34

Author(s):

Gaige Wang ◽

Lihong Guo ◽

Hong Duan ◽

Luo Liu ◽

Heqi Wang

Keyword(s):

Path Planning ◽

Optimization Problem ◽

Bat Algorithm ◽

Optimization Methods ◽

Population Based ◽

Planning Problem ◽

New Approach ◽

Air Vehicle ◽

Flight Route ◽

Path Planning Problem

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.

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A multiresolution work space, multiresolution configuration space approach to solve the path planning problem

Proceedings., IEEE International Conference on Robotics and Automation ◽

10.1109/robot.1990.126316 ◽

2002 ◽

Cited By ~ 18

Author(s):

B.J.H. Verwer

Keyword(s):

Path Planning ◽

Configuration Space ◽

Planning Problem ◽

Work Space ◽

Path Planning Problem ◽

Space Approach

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A new approach to solve nonlinear path planning problem via measure theory

2009 IEEE 13th International Multitopic Conference ◽

10.1109/inmic.2009.5383166 ◽

2009 ◽

Author(s):

A. Jajarmi ◽

H. Ramezanpour ◽

M. D. Nayyeri ◽

A. V. Kamyad

Keyword(s):

Path Planning ◽

Measure Theory ◽

Planning Problem ◽

New Approach ◽

Path Planning Problem

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Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications

Applied Sciences ◽

10.3390/app11052408 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2408

Author(s):

José Oñate-López ◽

Loraine Navarro ◽

Christian G. Quintero M. ◽

Mauricio Pardo

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Path Planning ◽

Utility Function ◽

Indoor Environment ◽

Planning Problem ◽

Multi Agent ◽

The Individual ◽

Intelligent Approach ◽

Path Planning Problem

In this work, the problem of exploring an unknown environment with a team of agents and search different targets on it is considered. The key problem to be solved in multiple agents is choosing appropriate target points for the individual agents to simultaneously explore different regions of the environment. An intelligent approach is presented to coordinate several agents using a market-based model to identify the appropriate task for each agent. It is proposed to compare the fitting of the market utility function using neural networks and optimize this function using genetic algorithms to avoid heavy computation in the Non-Polynomial (NP: nondeterministic polynomial time) path-planning problem. An indoor environment inspires the proposed approach with homogeneous physical agents, and its performance is tested in simulations. The results show that the proposed approach allocates agents effectively to the environment and enables them to carry out their mission quickly.

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