Confirmation Protocol

In the final set of experiments the hybrid system was combined with the Multi-Confirmation Transmission Protocol (MCTP) from Chapter 3. Incorporating a transmission protocol that sup- ports confirmations, such as MCTP, allows the hybrid system to further mitigate the random fluctuations in the wireless links. Another benefit of using MCTP, as previously detailed, is that it was designed specifically for the wireless ad-hoc networks created by the hybrid system. These two facts allow the team to achieve even more reliable end-to-end communication. An example of this is seen when the patrolling experiment from the previous section is repeated but with MCTP, instead of UDP. The results from these experiments, in which five laps instead of the twenty were executed, are shown in Fig. 4.12. Again a heat map showing the average success rate is plotted in Fig. 4.12a and the average success rate as a function of distance traveled with a one σbound is plotted in Fig. 4.12b. The benefit of incorporating a confirmation protocol is immediately ap- parent. The system maintains an average success rate greater than 0.85, even though the desired rate was only 0.3. This is achieved without changing any of the system requirements, therefore

Distance Traveled (meter) 0 10 20 30 40 50 60 70 80 90 Success Rate 0 0.2 0.4 0.6 0.8 1

Figure Eight Comparison

MCTP UDP

Figure 4.13: Average success rate as a function of distance for the the figure eight experiments in Sections 4.5.1 and 4.5.2.

demonstrating that even with lower input requirements the system will optimize the end-to-end data rate. This property allows the system to provide a minimum guarantee for the performance of the network, while not sacrificing the possibility of more optimal operation. A more direct comparison between the UDP and MCTP experiments is shown in Fig. 4.13. Notice that there are similar drops in the performance between the two, but the magnitude of the drops is much less in the MCTP experiments, indicating a much more reliable end-to-end transmission.

This experiment also highlights the value of the hybrid system’s design. Since the focus of the hybrid system is on motion control and network routing, it resides entirely in the network layer of the OSI networking model. This allows the hybrid system to be incorporated into any larger system that requires a networking layer that is also capable of motion control. As demonstrated in the previous experiments, the hybrid system can operate independently, or as demonstrated in this experiment, it can be incorporated with other OSI compliant components to construct a more complex system. This provides a system-level flexibility to incorporate advancements in other layers of the OSI model without requiring modification to the hybrid system.

4.6

Summary

In this chapter, we develop a hybrid system that is able to drive a team of robots through a complex environment while preserving network integrity. The system is composed of two main subsystems arranged in a feedback loop. The outer loop is responsible for generating trajectories for the team and the inner loop in responsible for using those trajectories as a roadmap to successfully complete the given task. This construction allows the team to complete the situational awareness task in a distributed manner while avoid local minima.

We demonstrate the abilities of the system extensively through simulations and experiments. In simulation, we highlight a specific task that a distributed controller is unable to complete, but is trivial for the hybrid system. Additionally, we verify the ability of the system to operate without a reduction in performance, even when the team consists of 25 robots. Through experiments, we compare the performance of the hybrid system to a centralized system and show the hybrid system’s ability to optimize the robot’s trajectories to achieve a higher end-to-end data rate with less coordination overhead. Then, we emphasize the dynamic nature of the hybrid system in experiments where one of the support robots has a temporary motor failure that results in the centralized system losing network integrity, but the hybrid system adapts and maintains the network.

We conclude with an application that is well suited to the hybrid system, patrolling a series of hallways. In this application a patrol robot must repeatedly visit a series of sensing locations while transmitting data back to an access point. The hybrid system is able to position the support robots such that there is never a break in the end-to-end link. We perform this task two times; first, using UDP to demonstrate the ability to maintain a minimum end-to-end link, and then using MCTP to demonstrate a near loss-less end-to-end link.

Chapter 5

Simultaneous

Communication-Aware

Localization and Mapping

The ability of a team of robots to move through a known environment while providing real- time situational awareness over a wireless ad-hoc network with minimal global coordination, as demonstrated in Chapter 4, is a major step towards completing our objective. While this an im- provement, there still remains one capability that is required for such teams to be useful in realistic scenarios. That capability is operation in unknown environments. As mentioned previously, the term unknown environment could be used to describe a variety of locations, such as a building that has experienced a partial collapse, a recent construction, or simply, a building that has not been mapped prior. In this chapter, we present a system that is capable of efficiently constructing a meaningful representation of the environment while supporting real-time transmission of timely sensor data back to a central location. Therefore, we say this system is preforming the task of

5.1

Unknown Environments

The existence of an accurate map of the environment is critical to the abilities of the hybrid system demonstrated in Chapter 4. The map is used throughout the system, specifically in the localization system to estimate the position of the robot, in the channel estimation system to identify if the link between two robots is obstructed by an obstacle, and in the global path planner to determine collision free trajectories. The impact of an inaccurate, or incomplete map, can be dramatic to each of these systems.