Multi-robot Exploration And Mapping In Unknown Environment

The success of explorating unknown environment using mobile robots will be thepremise of smoothly executing a variety of tasks later. As long as the environment iscognized by mobile robots, the problem from unknown environment could be easilysolved according to the known environment acquired so far. The only global optimaloption for the special task can be obtained by our robots, which benefits from theenough environment knowledge supported by the exploration. So there is a need ofstudying the exploration and mapping in unknown environment, which has importanttheoretical significance and research value. The exploration can be considered as aprocess of cognizing the environment,which will be done using the sensor from trobots.Meanwhile,our robots aslo need to handle with obstacle avoidancesimultaneously. The other action named mapping could be seen as a process ofinterpretating the data returned by sensors,which could form a map about theenvironment with the special chosen model.Compared with one single robot, multi-robot system show us its better adaptation,good expandability and higher reliability, which has drawn the attention of manyexperts and researchers. Supposing multi-robots system is applied to the environmentexploration and mapping, we could believe the result will be highly efficient andaccurate. Unfortunately, the existing exploration strategy for multiple robots strugglewith their own drawbacks anyway, which resists the strategies being applied tomassive robots. So, after doing some research on some papers, we now release aspecial algorithm based on Robotic Rractional Order Darwinian PSO, which is namedR-FODPSO here for short. The R-FODPSO is subject to swarm intelligencealgorithm, which enjoys some swarms existing meanwhile. During the process, thenumber of swarms and the number of robots from the same swarm could bothchanged anytime. what’s more,the aforementioned phenomenon, which means theswarms live with each other, is always doing good to relieving the communicationburden of the whole system. We conclude this algorithm is well suited for multi-robot application. However, the parameter zone can aslo be fixed just for better perfomance.After that, we design a fuzzy controller to realize parameters’ self-adjusting in theperiod of algorithm. In the end, we successfully simulate the algorithm in twoways,one for fixed parameters and the other for parameters’ self-adjusting..Meanwhile,we give some criteria here just for the compare between the two ways.According to the criteria values, the compare can be done and we finally draw aconclusion that the revised one, which means the self-adjusting for parameters, canmake one robot to move, averagely, in a shorter distance, makes itself to be activelonger and has a better result for mapping. We surely point out the R-FODPSOalgorithm owns all the advantages, which the multi-robot exploration strategiesshould need, like being efficient, being accurate and some transportability.