Swarm Robotic Search For Target: Cooperative Control Techniques, Strategies, And Simulations

As a novel branch of intelligent robotics, the focus of swarm robotics is on ap-plying swarm intelligence principles or techniques to the control of large groups of cooperating autonomous robots. In such a system, lots of homogenous robots hav-ing relative simple structure are controlled in a distributed fashion, being expected to emerge intelligence from limited sense and local interactions for accomplishing complex tasks. As to the task named target search, particle swarm optimization (PSO) is extended to model and control swarm robots. Being focused on this topic, some works are done and corresponding contributions are achieved in this thesis:(1) Due to the similarities and differences between particle and robot, swarm search can be mapped to PSO based on the common working mechanism.. To be-gin with, two concepts of communication neighborhood structure at systemic level and time varying characteristic swarm (TVCM) at individual level are introduced. Viewed as a particle moving in a closed two-dimensional workspace, each robot is abstracted as one order inertial element, and further, the model of extended PSO (EPSOM) can be given under an ideal environment. As for individual robots, each one is controlled by a three-state finite state machine.(2) In case of possible failure in deployment of global localization, relative po-sitioning mechanism is required. For this end, a short-time memory is introduced where each robot is capable of remembering signals measurement readings and po-sition coordinates at present and in the near previous time step. In this case, the cognitive position of each robot and the best-found position within its TVCM must be one of the two remembered positions. Then the relative observations including distance and bearing between the current position and the two best ones can be used to decide the expected velocity and position.(3) Local interactions among robots affect computation efficiency to a large ex-tent. Accordingly, an asynchronous parallel communication strategy is presented. In this case, virtual trigger with combinational logic is set for the shared social in-formation updating. The logical components are both finding signals fusion beyond the best social position by robot itself and monitoring update by other robots. Each robot updates its cognitive information at every time step, while its memory about social information updating occurs only when the trigger is sparked. If then, the robot will broadcast the updating information within its TVCM.(4) In the presence of obstacles and neighbors, path planning for each robot hav-ing physical size and kinematic properties have to be considered. Thus, a modified sensor-based artificial potential field (SBAPF) method and EPSOM is integrated for the problem solving after the kinematic model of individual robots are introduced. Each evolution position belonging to certain robot is taken as temporary target that attracts this robot. However, other robots and obstacles within its proximity sensors coverage repulse it. The moving direction depends on the composition of virtual forces. Besides, robot moving has to comply with its kinematic constraints.(5) PSO-type algorithm works on the premise that each robot fuses target sig-nals independently like fitness evaluate. Therefore, one typical environment where propagation of multi-source signals including intermittent call-for-help sound, peri-odic RF waves and continuous gas is brought forth. Let target and individual robot be virtual source and sink of information respectively. Then virtual continuous sink-source communication can be used to describe signals measurement. Because of the independence of heterogeneous signals emission from source, the signals can be encoded with three-bit codes at every time step and be transmitted to different sinks. Meanwhile, a concept of detection event can be proposed based on signals detection threshold at sinks. After then, those signals beyond threshold will be normalized and taken as distinguishing resolution. Also, some signal characteristics such as statistical properties, detection range, localization type and accuracy (if possible) can be used to decide the weights of contribution to fusion in dependence on comentropy-based criterion.All the above presented strategies are validated through a series of simulations conducted under satisfying corresponding conditions.