Particle swarm optimization (PSO) for simulating robot movement on two-dimensional space based on odor sensing

Nowadays,researches in robotic field have grown increasingly. there are several types of research categories in the field of robotic. recently,one of the famous research works recently was odor sensing. within the technology that grows rapidly,this topic has become an interest among researchers. an odor sensing is not only applied in the medical field,but it has also been widely used in the industry. the gradient of concentration of an odor is measured by diluting some amount to reach the threshold of an odor. this paper focused on the implementation of the particle swarm optimization (pso) method based on odor sensing in two (2) dimensional space. however,it only discusses and focuses on applying in ideal condition. an ideal condition here means that there is no disturbance included in this simulation. the main idea of this paper was to observe how the particle agents make the movement based on concentration by applying the pso method. the real sensor cannot be implemented in this simulation because the value of concentration is measured due to the distance from the particles agent to the goal of agents. higher gradient concentration is shown at the shorter distance to the goal. the contributions in this paper are mainly to create an algorithms model by using particle swarm optimization (pso) to calculate the paths of movement of mobile robot until they reach the goals (source of odor) with respect to the concepts of odor sensing.