Combining Blockchain and Swarm Robotics to Deploy Surveillance Missions

Current swarm robotics systems are not utilized as frequently in surveillance missions due to the limitations of the existing distributed systems\u27 designs. The main limitation of swarm robotics is the absence of a framework for robots to be self-governing, secure, and scalable. As of today, a swarm of robots is not able to communicate and perform tasks in transparent and autonomous ways. Many believe blockchain is the imminent future of distributed autonomous systems. A blockchain is a system of computers that stores and distributes data among all participants. Every single participant is a validator and protector of the data in the blockchain system. The data cannot be modified since all participants are storing and watching the same records. In this thesis, we will focus on blockchain applications in swarm robotics using Ethereum smart contracts because blockchain can make a swarm globally connected and secure. A decentralized application (DApp) is used to deploy surveillance missions. After mission deployment, the swarm uses blockchain to communicate and make decisions on appropriate tasks within Ethereum private networks. We set a test swarm robotics system and evaluate the blockchain for its performance, scalability, recoverability, and responsiveness. We conclude that, although blockchain enables a swarm to be globally connected and secure, there are performance limitations that can become a critical issue

Measuring progress in robotics: Benchmarking and the ‘measure-target confusion’

While it is often said that robotics should aspire to reproducible and measurable results that allow benchmarking, I argue that a focus on benchmarking can be a hindrance for progress in robotics. The reason is what I call the ‘measure-target confusion’, the confusion between a measure of progress and the target of progress. Progress on a benchmark (the measure) is not identical to scientific or technological progress (the target). In the past, several academic disciplines have been led into pursuing only reproducible and measurable ‘scientific’ results – robotics should be careful to follow that line because results that can be benchmarked must be specific and context-dependent, but robotics targets whole complex systems for a broad variety of contexts. While it is extremely valuable to improve benchmarks to reduce the distance be- tween measure and target, the general problem to measure progress towards more intelligent machines (the target) will not be solved by benchmarks alone; we need a balanced approach with sophisticated benchmarks, plus real-life testing, plus qualitative judgment