Complex autonomous robots such as autonomous vehicles or robotic guides are critical systems because their failures could have catastrophic and costly consequences on themselves and their immediate environment, including users and bystanders. Moreover, verification and validation of these systems, that includes decisional software, is a difficult and complex task, requiring high expertise. In practice, despite recent advances in formal verification techniques and intensive testing for autonomous vehicles, it is still not possible to guarantee elimination of all residual development faults. Another way to enhance the confidence placed in such software, is to consider tolerance mechanisms with regards to these faults. This article proposes such an approach for temporal planners which are a major class of decisional software components in complex autonomous systems. The proposed fault tolerance mechanisms focus on residual development faults in planning models and heuristics. They use four complementary detection mechanisms to detect planning errors. Recovery from possible errors is achieved using redundant diversified planning models. We present an implementation of the proposed architecture on an existing autonomous robot software architecture. We also describe a validation framework used to evaluate the cost and efficacy of the fault tolerance mechanisms using real robot software on simulated robot hardware, and fault injection in the declarative planning models. In this framework, the proposed fault tolerant mechanisms are shown to greatly improve the system reliability with no significant impact on performance.