# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 8 Conclusions and Future Work  
In this paper, we presented two main contributions: a novel algorithm for localising a single emitter using Received Signal Strength (RSS) from multiple directional antennae to derive direction estimates that improve upon existing localisation methods based only on distance estimates, and a system integration that demonstrates the effectiveness of this approach on a mobile robot. We developed a low-cost omnidirectional antennae setup with cylindrical parabolic reflector dishes that enabled the calculation of the direction of a signal source based on the angle of arrival. The received RSS values were processed with a particle filter in order to enable accurate target localisation, reducing the effects of interference and variations in RSS. This was combined with a simple motion planning system that allowed for basic obstacle avoidance while still progressing towards the end goal in some cases. This preliminary system was able to localise and navigate to the wireless emitter in open spaces and environments where there are minimal obstacles.  
With the addition of a Simultaneous Localisation and Mapping (SLAM) module, a map of the environment was generated that enabled long-term path planning using the A* algorithm. This enabled the robot to perform autonomous navigation in much more complex environments with multiple obstacles, including scenarios where the robot has to drive away from the target position in order to move around obstacles. The improved performance is validated through a number of experimental tests designed according to factory automation scenarios that demonstrate the superiority of the full system when there is no straight-line path available.  
The techniques described in this paper are applicable to a wide range of scenarios beyond industrial automation, such as scenarios with frequent changes and those that require the use of ad-hoc robotic systems. In emergency situations where successful deployment is time-critical, approaches that reduce the setup costs and times such as ours will be preferable to those that require significant infrastructure. Future work includes implementing more advanced forms of path planning to reduce the stringency of the current movement grid constraints to allow for more optimal navigation,  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Mean Time (Sec) Taken in** & **Mean Time (Sec) Taken in** \\  & **Preliminary System** & **Full System** \\ \hline (A) 4 m straight line & 89 & **90** \\ (B) Blocked 4 m straight line & 126 & 230 \\ (C) Partially blocked 4 m right angle & 252 & **225** \\ (D) Blocked 4 m right angle & 391 & **222** \\ (E) U-turn & Could not complete & **455** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark results, with the best result in each test bolded.  
improving the choice of obstacle avoidance sensors to allow for range finding in multiple planes, and potentially adapting the approach to allow for three-dimensional navigation by drones or underwater vehicles. Multipath effects could also be better estimated by identifying the shape and material of nearby obstacles, allowing for better correction of these effects at the receiver. This could also be achieved by incorporating other sensor data into the existing particle filter.  
This work was supported, in part, by the National Science Foundation, China (Project No. 61503247). [PERSON]-[PERSON] and [PERSON] developed the motivating scenario; [PERSON], [PERSON] and [PERSON] conceived and designed the research work; [PERSON] and [PERSON] developed the test platform and performed the experiments; [PERSON]-[PERSON], [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.