Research On Mission Planning And Fault Diagnosis Of Mobile-robot Based On Swarm Intelligent

Multi-robot mission planning assign tasks to each robot under the allocation criteria, and plan task execution order in accordance with the optimal path requirement, which could complete tasks efficiently by mobile-robot system. Because of tasks'complexity and diversity, if robots don't coordinate together, it may lead to excessive consumption of robotic system costs, or even result in robots malfunction. Therefore, multi-robot mission planning is the cornerstone of the completion of complex tasks. Meanwhile robot-sensor system fault diagnosis is the basic guarantee for successful mission planning.Based on the establishment of heterogeneous robot team control platform, this paper carries on a in-depth study of multi-robot system tasks detection method, establishes a task allocation model according to the minimum probability of mission failure, and designs a optimization algorithm to solve the allocation model. On this basis, this paper puts forward a strategy for multi-robot task planning methods solving and dynamic incremental mission planning. This strategy is used in MORCS-2 robot team, which has achieved apparent accomplishment. At the same time, in order to ensure the proper completion of the planned tasks, this paper carries on the fault diagnosis study of robot sensor system. Main research work and innovative achievements are as follows:An equal division point ant colony algorithm (EDPACA) is proposed to solve the multi-robot collaboration mission exploration. the algorithm is designed by a novel solution construction through multi-group ants search cooperatively strategy and a more reasonable evaluation function is define which consider sufficiently equal allocation exploration mission, and avoid a max-consuming robot overload. At last, the crossover problems of sub-circular paths are solved by 2-opt method. The experiment results show that the proposed algorithm is available to gain better solution, and solved multi-robot system a large-scale of tasks balance exploration problem.Aiming to multi-robot collaborative tasks allocation problem for the smallest failure probability, tasks allocation mathematical model is established firstly, which considers three factors comprehensively:the efficiency of executing mission, the ability of robot and the nature of the mission. Current learning Discrete Particle Swarm Optimization Algorithm (CLDPSO) is proposed to solve this model. Adaptive perturbation factor is introduced according to the population heterogeneity to keep particle swarm evolutional capability. Based on the excellent performance of particle swarm society learning ability and individual learning ability in DPSO, we propose a new conception current learning factor to improve the DPSO kinetic equation, and the robost of CLDPSO is better. Finally nearby neighbor mutant strategy is added to increase local search capabilities. The experiment results show that CLDPSO has strong optimization ability and robustness, meanwhile the rationality of the task allocation model is verifiedMulti-robot mission planning divides into task allocation and route planning subdivision, we design spatial orthogonal cluster algorithm for multi-robot task assignment problem, and propose novel system architecture of heterogeneous interactive cultural hybrid algorithm to solve the best route planning problem. The tasks assignment problem adopts 3-D space model, utilizes spatial orthogonal test technology. The attractor position are updated according to load balance objective function, this approach is high validity but lower complexity. Heterogeneous interactive cultural hybrid algorithm firstly initializes population space using good-point-set in order to make particles swarm uniform distribution in feasible region. Secondly, novel evolution model and particle evolution ability indexes are redefined, which increase particles swarm diversity and improve algorithm stability. At last the results are shown that SOCHCHA is superior and significant. Meanwhile, we practice SOCHCHA on MORCS-2 robot-team platform which exhibits the algorithm practicability. On this basis, a rule-based greedy algorithm for stochastic incremental task re-planning is designed, which makes mobile-robot load balance after re-planning, the algorithm is reasonable which is verified by using different TSPLIB mission maps.During mission planning process, if mobile-robots dead reckoning system breaks down, and don't diagnose it on time, which may lead to fail for robot execution tasks. A multi-modality Rao-Blackwellized evolutionary particle filter (MERBPF) algorithm is devised for those fault diagnosis problems. Particle filter is utilized to estimate robot fault state and Kalman Filter is used to calculate accurately kinetic state, so as to drop the complexity of high-dimensional state space. The inconsistency from particle degeneration problem is solved by integrating swarms'intercross and mutation strategy, and adding disturbance factors accoding to diversity. Robot moving states are determined by expert rules reasoning mechanism and monitored by each different ERBPF. Finally the multi-modality ERBPF are formed which express complex logic clearly. MERBPF maintains a strong robustness even under the strong process noise. Meanwhile MERBPF reduces diagnostic errors rate for fault diagnosis of robot's dead reckoning system.