Swarm robotic systems comprise many individual robots, and exhibit a degree of innate fault tolerance due to this built-in redundancy. They are robust in the sense that the complete failure of individual robots will have little detrimental effect on a swarm's overall collective behaviour. However, it has recently been shown that partially failed individuals may be harmful, and cause problems that cannot be solved by simply adding more robots to the swarm. Instead, an active approach to dealing with failed individuals is required for a swarm to continue operation in the face of partial failures. This thesis presents a novel method of exogenous fault detection that allows robots to detect the presence of faults in each other, via the comparison of expected and observed behaviour. Each robot predicts the expected behaviour of its neighbours by simulating them online in an internal replica of the real world. This expected behaviour is then compared against observations of their true behaviour, and any significant discrepancy is detected as a fault. This work represents the first step towards a distributed fault detection, diagnosis, and recovery process that would afford robot swarms a high degree of fault tolerance, and facilitate long-term autonomy.
|Qualification||Doctor of Philosophy|
|Award date||20 Jan 2017|
|Publication status||Published - Mar 2016|
- Swarm robotics
- Fault detection