The Research On Problems Of Terrain Coverage In Swarm Robotics Based On Wasp Swarm Algorithm

Swarm robotic system is composed of numerous of autonomous mobile robots, which functions and structure are relatively simple. Robots with limited sense and interaction can fulfill tasks cooperatively that a single robot can not be undertaken. Such a system has the characters of robust, flexibility and scalability, etc. The coordination strategy of the system, which is inspired of the self-organization behavior of biological society, has advantages of (a) simple rule,(b) decentralized control,(c) adapt to the complex environment, etc. In this thesis, it is mainly focus on the problem of terrain coverage on swarm robotics, prompted the control strategy of swarm robot and simulation research, which is inspired of response threshold model of wasp swarm. The detailed research work and results are as follows:(1) Formal description of terrain coverage problem is presented on the basis of the review on the state-of-art on terrain coverage on robotics. Due to the similarities and differences between wasp and robot, terrain coverarge can be mapped to response threshold model based on the common working mechanism. After the problem description of terrain coverage, the terrain coverage algorithm based on fixed response threshold model on swarm robotics is depicted. In this "algorithm, terrain is modeled as free closed grid environment, and robots decide their moving directions in term of stimuli of its neighbor cells.(2) In order to guide the selection of parameters in terrain coverage algorithm based on fixed response threshold, the performance of the algorithm should be anlysized. The performances of algorithm are evaluated by microscopic anlysis and probabilistic macroscopic anlysis. The macroscopic anlysis considers the effects of parameters, such as response threshold, stilumus, modified stimulus parameter etc. Verified by simulation results, the anlytic result is reliable under some conditions. This method provides us with a mathematical foundation of the research and application of the terrain coverage algorithm.(3) Another analytic method to evaluate the performance of this algorithm is stochastic discrete process. We set up this model to analyze the coverage performance of algorithm, such as mathematical expectation of average coverage rate. Results show that this analysis method is flexible and effective under certain conditions. This method based on probobalitic discrete process has the advantage of analyzing the effect of various parameters and guiding the selection of the parameters.(4) Obstacle avoidance is an important issue in automatic robotics. The moving strategy of swarm robots based on fixed response threshold under obstacle environment is put forward. In order to make the research more detailed, the modeling of the robot, and obstacle avoidance is presented. The coverage performance of the algorithm is tested through various shapes of obstacle in simulation experiments. Simulation results show the method is effective and feasible.(5) On the basis of the analysis of the advantage and disadvantage of terrain coverage algorithm based on fixed response threshold model, we introduces self-reinforcement model of response threshold and puts forward terrain coverage algorithm based on self-adjustable response threshold. The moving directions of robots are decided by the external stimuli and response threshold, which will be adjusted by robots based on the previous experience. The coverage performances of the algorithm are analyzed and tested in the simulation experiments which are conducted in free closed environments and obstacle environments. Final results show that this method can improve the coverage performance effectively.(6) Task allocation of multi-target search in swarm robotics is how to coordinate the tasks of searching targets among robots. The formal description of the problem of multi-target search and task allocation are presented^Inspired of division labor of wasp swarm, the strategy of task allocation of multi-target search based on the response threshold model in swarm robotics is prompted. The states of robots are divided into wander, decision, waiting, marking target and etc. Robots adopts random search in global search phase and particle swarm algorithm in local search in order to illustrate the effectiveness of the strategy. Simulation results show that the proposed task allocation method can be effectively applied to multi-target search.