Resilient Navigation through Online Probabilistic Modality Reconfiguration

This paper proposes an approach to achieve resilient navigation in the context of indoor mobile robots. Resilient navigation seeks to mitigate the impact of control, localisation, or map errors on the safety of the platform while enforcing the robot’s ability to achieve its goal. We show that resilience to unpredictable errors can be achieved by combining the benefits of independent and complementary algorithmic approaches to navigation, or modalities, each tuned to a particular type of environment or situation. In this paper, the modalities comprise a path planning method and a reactive motion strategy. While the robot navigates, a Hidden Markov Model estimates the most appropriate modality online, or recommended modality, based on two types of information: context (using mainly information known a priori) and monitoring (evaluating unpredictable aspects of the current situation). The robot then uses the recommended modality, switching between one and another dynamically. The experimental validation shows that our approach can enable failure mitigation while maintaining the safety of the platform and making the most of the available modalities. In particular, the robot is shown to reach its goal successfully in the presence of: unpredicted control error, unexpected map errors and large injected localisation fault.