Fault tolerance is increasingly important in modern autonomous or industrial robots. The ability to detect and tolerate failures allows robots to effectively cope with internal failures and continue performing designated tasks without the need for immediate human intervention. To support these fault tolerant capabilities, methods of detecting and isolating failures must be perfected. This thesis presents new fault detection algorithms which detect failures in robot components using analytical redundancy relations. The robot components critical to fault detection are revealed using an extended fault tree analysis. The thesis validates the algorithms using a simulated robot failure testbed. An intelligent fault tolerance framework is proposed in which a fault tree database and the detection algorithms work together to detect and tolerate sensor or motor failures in a robot system. Future work will expand the detection and tolerance routines and embed the framework into a more flexible expert system package.