Fault Diagnosis For Mobile Robots In Unknown Environment

With the remarkable progress in robotics, mobile robots can be used in many applications including exploration in unknown area, search and rescue, reconnaissance, security, military, rehabilitation, pick up and delivery, and cleaning. Most of the research work on mobile robots has been done in which the system in ideal. Experience with physical mobile robots has shown that robot failure is very common. Especially in complex and unknown environments, mechanism components and control systems of robots are possibility to become faulty, where human intervention is expensive, slow, unreliable, or impossible. If the faults can't be addressed, or processed in time, mobile robots will operate in an unpredictable and dangerous way. Faults will decrease the performance of the robots, and make the robots can't operate in normal manner, even will result in catastrophic failures. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner.According to the current international research situation of fault diagnosis technique for mobile robot, combined with new results obtained in relative disciplines, some kinds of fault diagnosis schemes for mobile robot in unknown environment are proposed in this thesis. The major work and the result of research are represented as follows:1. In this thesis, the current international research situation of fault diagnosis technique is summarized. The mathematics model, signal processing and knowledge based fault diagnosis method are studied.2. The fault diagnosis of mobile robot in unknown environment is studied. The subsystems of mobile robot are introduced. The fault modes of each subsystem are given. The fault diagnosis methods of each subsystem are summarized. Then, the kinematics model and dynamic model of mobile robot are constructed.3. Based on multiple model estimation and Cerebellar Model Articulation Controller (CMAC) neural network, a new fault diagnosis method for mobile robots is studied. Firstly, the fault models of mobile robots motion system are constructed. Then, using the sort approximation ability of the neural network, an exact mapping from space of fault symptom to space of fault modes is established. Finally, the proposed fault diagnosis method apply the CMAC neural network to decide which fault has occurred, This method has been implemented on a mobile robot and the simulation results show the effectiveness of the method. 4. An improved particle filter based approach is proposed to diagnosis and prediction the fault modes of mobile robots. Particle filter is a algorithm that uses swarms of weighted particles in state space to approximate the probability density function of the state. According to the probability distribution of the state, the probability of fault diagnosis and fault prediction could be obtained. The proposed approach has been implemented on a mobile robot and the simulation results show the effectiveness of the method.5. A new method is introduced to detect and diagnose the faults of mobile robots in different movement states. The movement states of mobile robot include static state, rectilinear movement state, and three kinds of turning states. Several modes of faults are discussed in the corresponding movement states. Then a bank of Kalman filters are used to process the mode probability of each fault mode occurring during the movement states. According to the values of mode probability, we can estimate which mode of faults occurred. Compared with other fault detection and diagnosis methods, the method proposed in this paper improves the capability of avoiding the appearance of misdiagnosis and failing to detection and diagnosis. This proposed method has been implemented on a mobile robot and the simulation results show the effectiveness of the method.Finally, the work of this thesis is summarized and the prospective of future research is discussed.