Team Robot Motion Planning in Dynamics Environments Using a New Hybrid Algorithm (Honey Bee Mating Optimization-Tabu List)

This paper describes a new hybrid algorithm extracted from honey bee mating optimization (HBMO) algorithm (for robot travelling distance minimization) and tabu list technique (for obstacle avoidance) for team robot system. This algorithm was implemented in a C++ programming language on a Pentium computer and simulated on simple cylindrical robots in a simulation software. The environment in this simulation was dynamic with moving obstacles and goals. The results of simulation have shown validity and reliability of new algorithm. The outcomes of simulation have shown better performance than ACO and PSO algorithm (society, nature algorithms) with respect to two well-known metrics included, ATPD (average total path deviation) and AUTD (average uncovered target distance). 1. Introduction One of the most important issues in using robots is working automatically without human intervention. Autonomous robots are robots that self-control itself without human intervention [1]. Today, these robots could be applied in different areas such as working with dangerous materials, working in military service [2], underwater working [3], and rescue robots [4]. But using robots in these working areas is faced with some difficulties such as accuracy and constancy in operation [5]. The robots work in an unknown [6] and dynamics [2] Environments. The obstacles and goals in these environments are variable with a moving situation [7]. So, these robots must perform their tasks in such complex environment. In these environments, the robots must identify their tasks, reading and reaching decision for performing a good performance [8]. Consequently, suitable and accurate programming of robots has a positive influence on their operation. Moreover, we can use hardware capabilities of robots with a good programming of robots [9]. So, robot motion planning has been presented as an important field in robotic science [10]. Robot motion planning refers to process of robot task breakdown in the format of separated and discrete motions [7]. In robot motion planning, general strategies are educated to robots for selecting suitable motion among different motions that are available for it [11]. This helps them in doing their chores without any important problems or obstacle collision. But the working capacity of a robot is limited and when we use it in a real world’s environment, we need to be using a group of them [12]. When we use a group of robots, we faced new problems such as robot cooperation [13], robot obstacle avoidance [10], and robot avoidance deadlock [14]. Furthermore, using