This thesis project has been done with Crownstone, a subsidiary company of Almende. One of their project goals was to develop indoor localization algorithms to determine room-level location of an asset within the Crownstone network for smart-building, home automation and healthcare applications. An asset is a wireless device transmitting Bluetooth messages that are heard by Crownstones(sensors) and measure the strength of the received signal(RSSI). The terms position and location are used to denote two different concepts in this thesis. Position refers to estimating the specific coordinates, whereas a location refers to a much wider space like a room. In this thesis, we are interested in getting a location estimate.
There are two most widely used and researched localization techniques to determine the location of the asset. First, a model based(MB) method (e.g. Trilateration algorithm) which uses a mathematical model based on distance and second is a data-driven(DD) method (e.g. Fingerprinting algorithm) that relies on existing data, like RSSI to directly get the location. Algorithms are tested on real data collected by the Crownstones at the Almende office(test environment) divided into a finite number of locations or rooms. Metrics are defined based on the requirements of Almende to compare the MB algorithms with the DD algorithms. In this thesis, firstly, a centralized multilateration(MB-C) algorithm is implemented taking into account distances from N Crownstones at the office. Since one of the requirements was to perform in-network localization, a simple averaging consensus based distributed(MB-D) algorithm was selected and compared against the MB-C algorithm. Results show that the MB-D algorithm is faster, scalable and robust against single-point of failure than the MB-C but is less accurate and does not converge to the centralized solution for a noise variance greater than 10dB.
The MB algorithms have limitations in terms of selecting a model, learning the
model parameters and an additional step of mapping the position output of the implemented MB algorithms to a location is also required. To deal with these challenges, a Machine-learning(ML) based data-driven algorithm is proposed. In this, training datasets were iteratively improved with different features. Then, an Ensemble based centralized ML algorithm (DD-C) is implemented, giving a classification accuracy of 65%. Algorithm is further improved by distributed data handling leading to a classification accuracy of 77%. There has been very little to no study on finding the room-level location of an asset in an indoor setting using a distributed ML based data-driven algorithm. A consensus based distributed ML algorithm (DD-D) is proposed that performs local predictions within the Crownstone network using the same globally trained model giving a classification accuracy of 73%.
The results show that the proposed DD algorithms perform better than the MB
algorithms in terms of accuracy and are comparable in terms of prediction time. Results also indicate the proposed DD algorithms are more scalable, robust against noise but are computationally expensive.

Distributed cooperative robots can be highly beneficial in mapping disaster environments and assisting with search and rescue operations. In most situations such environments only allow for only limited communication between robots. This thesis reports on simulation experiments conducted to test the impact of having only partial communication capabilities between cooperative agents on area exploration strategies. The Monte Carlo Tree Search (MCTS) planning algorithm has been utilised by multi-robot teams to cooperate and explore an environment effectively. On top of this base case, other communication scenarios are applied: No communication at all, and near-neighbor communication at various ranges. In addition to these communication strategies, robots are also given the ability to predict the paths of peers. From extensive simulation tests, it is shown that partial communication can recover a significant amount of performance in a limited communication environment. Giving agents a peer prediction ability is shown to have a positive effect in very specific situations. It is also shown that providing prior information of the environment obstacle locations to agents is not useful. Instead, increasing the number of chances of agents sharing information, positively effects the exploration performance.