# Teleolocation--Remotely Operated Legged Robots
## 5 Discussion  
Teleoperation has been shown to be effective in task spaces too dangerous or otherwise non-ideal for human intervention. Furthermore, when combined with haptic feedback and real-time sensing, performance and safety can be improved. These teleoperated architectures have success in controlling robot manipulators, but are only useful if the robot is able to reach the task at hand. This becomes an issue for difficult or challenging terrain unsuitable for wheeled or tracked navigation systems. Such obstacles are frequent in disaster response or space exploration, for example.  
Articulated legs, as opposed to wheels and tracks, offer practical flexibility and maneuverability to potentially traverse such terrains. The problem is real-time automation of such an unstructured task, as evidenced by the trials at the DARPA Robotics Challenge [59, 60]. Instead, human-in-the-loop control may offer better performance and adaptability in uncertain task environments. This project provides a proof of concept implementation of such an architecture. Overcoming challenging obstacles in space exploration might be achieved with human-controlled legged walking. In another case, in lieu of risking the lives of human responders in dangerous tasks, human-controlled legged robot proxies may be able to traverse a variety of real-world terrains and execute real tasks. This potential to save lives is one that is particularly of interest to the authors, and the promising preliminary results show that haptic feedback has the potential to be useful in future interfaces for control of robot gait execution. In particular, Experiment I showed that for remote control of legged locomotion, haptic joysticks with the proposed virtual fixtures resulted in significantly better performance for traversing complex terrain compared to widely used interface types. Furthermore, Experiment II produced preliminary results  
Figure 14: Performance comparisons between modes **D** and **E** for Experiment II, showing (**a**) distribution for time required and (**b**) number of steps needed to complete the navigation task.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Operator** \\ **Mode** \\ \end{tabular} & \begin{tabular}{c} **D** \\ **Virtual Fixtures** \\ **Disabled** \\ \end{tabular} & \
\begin{tabular}{c} **E** \\ **Virtual Fixtures** \\ **Enabled** \\ \end{tabular} &
\begin{tabular}{c} **D-E** \\ **Improvement with** \\ **Fixtures** \\ \end{tabular} \\ \hline Time [s] & 141.5 & 130 & 11.5 \\ \hline Steps & 127.7 & 124.95 & 2.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment II Mean Time to Completion and Mean Steps.  
that suggest performance enhancements using the proposed virtual fixtures in a physical implementation of telec locomotion to traverse a complex obstacle course. Performance was observed to be more consistently distributed across trials and users with the proposed method.  
Beyond life-critical tasks, robotic devices may need to navigate very unpredictable everyday traversal tasks. For example, navigating stairs, unfinished pathways, construction sites or otherwise loose terrain may be encountered. This work thus has the potential to extend telepresence to domains where wheeled or tracked robots cannot reach.