Autonomous robots offer alluring perspectives in numerous application domains: space rovers, satellites, medical assistants, tour guides, etc. However, a severe lack of trust in their dependability greatly reduces their possible usage. In particular, autonomous systems make extensive use of decisional mechanisms that are able to take complex and adaptative decisions, but are very hard to validate. This paper proposes a fault tolerance approach for decisional planning components, which are almost mandatory in complex autonomous systems. The proposed mechanisms focus on development faults in planning models and heuristics, through the use of diversification. The paper presents an implementation of these mechanisms on an existing autonomous robot architecture, and evaluates their impact on performance and reliability through the use of fault injection.