Evolutionary algorithm

Gene

Robot

Algorithm

Engineering

Aerospace engineering

Fitness function

Motion planning

Real-time computing

Mutation

Constraint (computer-aided design)

Selection (genetic algorithm)

Biochemistry

Computer science

Mathematical optimization

Computer network

Mechanical engineering

Operations research

Mathematics

Genetic algorithm

Differential (mechanical device)

Path (computing)

Artificial intelligence

Geometry

Chemistry

Programming language

Differential evolution

Machine learning

Knowledge-Based Systems

Disasters have caused significant losses to humans in the past decades. It is essential to learn about the disaster situation so that rescue works can be conducted as soon as possible. Unmanned aerial vehicle (UAV) is a very useful and effective tool to improve the capacity of disaster situational awareness for responders. In the paper, UAV path planning is modelled as the optimization problem, in which fitness functions include travelling distance and risk of UAV, three constraints involve the height of UAV, angle of UAV, and limited UAV slope. An adaptive selection mutation constrained differential evolution algorithm is put forward to solve the problem. In the proposed algorithm, individuals are selected depending on their fitness values and constraint violations. The better the individual is, the higher the chosen probability it has. These selected individuals are used to make mutation, and the algorithm searches around the best individual among the selected individuals. The well-designed mechanism improves the exploitation and maintains the exploration. The experimental results have indicated that the proposed algorithm is competitive compared with the state-of-art algorithms, which makes it more suitable in the disaster scenario.

A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios