Prasanna Velagapudi

Choosing Autonomy Modes for Multirobot Search

Objective: The number of robots an operator can supervise increases with the robots’ level of autonomy. The reported study investigates multirobot foraging to identify aspects of the task most suitable for automation. Background: Many envisioned applications of robotics involve multirobot teams. One of the simplest of these applications is foraging, in which robots are operated independently to explore and discover targets. Depending on levels of autonomy and task, operators have been found able to manage 3 to 12 robots. Method: The foraging task can be functionally subdivided into visiting new regions and identifying targets. In the reported experiment, full-task foraging performance was compared with exploration and perceptual search performance for 4-, 8-, and 12-robot teams in a between-groups repeated measures design. Results: Operators in the full-task condition could not successfully manage 12 robots, finding only half as many victims as perceptual search operators. Exploration performance was roughly the same in the full-task and exploration conditions, suggesting that performance of this subtask was limiting the number of robots that could be controlled. Conclusion: Performance and workload measures indicate that exploration (navigation) tasks are the limiting factor in multirobot foraging. This finding suggests that robot navigation is the best candidate for automation. Application: Search tasks, such as foraging or perimeter control, account for many of the near-term applications envisioned for multirobot teams. The results support the choice of task-centered architectures in which the control and coordination of robotic platforms is automated, leaving search and identification of targets to human operators.

Development of a Low Cost Multi-Robot Autonomous Marine Surface Platform

In this paper, we outline a low cost multi-robot autonomous platform for a broad set of applications including water quality monitoring, flood disaster mitigation and depth buoy verification. By working cooperatively, fleets of vessels can cover large areas that would otherwise be impractical, time consuming and prohibitively expensive to traverse by a single vessel. We describe the hardware design, control infrastructure, and software architecture of the system, while additionally presenting experimental results from several field trials. Further, we discuss our initial efforts towards developing our system for water quality monitoring, in which a team of watercraft equipped with specialized sensors autonomously samples the physical quantity being measured and provides online situational awareness to the operator regarding water quality in the observed area. From canals in New York to volcanic lakes in the Philippines, our vessels have been tested in diverse marine environments and the results obtained from initial experiments in these domains are also discussed.

Maintaining shared belief in a large multiagent team

A cooperative teams performance strongly depends on the view that the team has of the environment in which it operates. In a team with many autonomous vehicles and many sensors, there is a large volume of information available from which to create that view. However, typically communication bandwidth limitations prevent all sensor readings being shared with all other team members. This paper presents three policies for sharing information in a large team that balance the value of information against communication costs. Analytical and empirical evidence of their effectiveness is provided. The results show that using some easily obtainable probabilistic information about the team dramatically improves overall belief sharing performance. Specifically, by collectively estimating the value of a piece of information, the team can make most efficient use of its communication resources.

Scaling effects in multi-robot control

The present study investigates the effect of the number of controlled robots on performance of an urban search and rescue (USAR) task using a realistic simulation. Task performance increased in going from four to eight controlled robots but deteriorated in moving from eight to twelve. Workload increased monotonically with number of robots. Performance per robot decreased with increases in team size. Results are consistent with earlier studies suggesting a limit of between 8-12 robots for direct human control. This study demonstrates that these findings generalize to a more realistic setting and complex task.

Process and Performance in Human-Robot Teams:

The authors are developing a theory for human control of robot teams based on considering how control difficulty grows with team size. Current work focuses on domains, such as foraging, in which robots perform largely independent tasks. Such tasks are particularly amenable to analysis because effects on performance and cognitive resources are predicted to be additive, and tasks can safely be allocated across operators because of their independence. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an urban search-and-rescue (USAR) task. Two possible ways to organize operators were identified: as individual assignments of robots to operators, assigned robots, or as a shared pool in which operators service robots from the population as needed. The experiment compares two-person teams of operators controlling teams of 12 robots each in the assigned-robots condition or sharing control of 24 robots in the shared-pool condition using either waypoint control in the manual condition or autonomous path planning in the autonomy condition. Automating path planning improved system performance, but process measures suggest it may weaken situation awareness.

Teams organization and performance in multi-human/multi-robot teams

We are developing a theory for human control of robot teams based on considering how control varies across different task allocations. Our current work focuses on domains such as foraging in which robots perform largely independent tasks. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an Urban Search and Rescue (USAR) task. We identify three subtasks: perceptual search-visual search for victims, assistance-teleoperation to assist robot, and navigation-path planning and coordination. For the studies reported here, navigation was selected for automation because it involves weak dependencies among robots making it more complex and because it was shown in an earlier experiment to be the most difficult. Two possible ways to organize operators were identified as assignment of robots to particular operators or as a shared pool in which operators service robots from the population as needed. The experiment compares two member teams of operators controlling teams of 12 robots each in the assigned robots conditions or sharing control of 24 robots in the shared pool conditions using either waypoint control or autonomous path planning. We identify three self organizing team strategies in the shared pool condition: joint control operators share full authority over robots, mixed control in which one operator takes primary control while the other acts as an assistant, and split control in which operators divide the robots with each controlling a subteam. Automating path planning improved system performance. Effects of team organization favored operator teams who shared authority for the pool of robots.

Human teams for large scale multirobot control

We are developing an architecture for controlling robot teams based on considering how control difficulty for different tasks grows with increases in team size. Our analysis suggests that assignments of persons to commander (single commands to entire robot team), operator (commands to individual robots), and coordinator (control of interdependent robots) roles can lead to the most efficient organization. The ability to assign tasks within or between operators makes scheduling these interactions an important factor in team performance. Two possible ways to organize operators are through Individual Assignments of robots or as a Call Center in which operators service robots from the population as needed. In recent experiments we have found that participants performing an Urban Search And Rescue (USAR) foraging task using waypoint control were at or over their limits when controlling 12 robots. The present study uses the same robots, environment, and level of autonomy but with teams of two operators assigned to control 24 robots. These operators controlled teams of 12 robots in the Individual Assignment condition. In the Call Center condition operators shared control of the 24 robots. For this task and level of robot autonomy Individual Assignment participants performed marginally better searching larger regions but without finding more victims.

Synchronous vs. Asynchronous Video in Multi-robot Search

Camera guided teleoperation has long been the preferred mode for controlling remote robots, with other modes such as asynchronous control only used when unavoidable. In this experiment we evaluate the usefulness of asynchronous operation for a multirobot search task. Because controlling multiple robots places additional demands on the operator, removing the forced pace for reviewing camera video might reduce workload and improve performance. In the reported experiment participants operated four robot teams performing a simulated urban search and rescue (USAR) task using either conventional streaming video plus a map interface or an experimental interface without streaming video but with the ability to store panoramic images on the map to be viewed at leisure. Search performance was somewhat better using the conventional interface, however, ancillary measures suggest that the asynchronous interface succeeded in reducing temporal demands for switching between robots.

Flood disaster mitigation: a real-world challenge problem for multi-agent unmanned surface vehicles

As we advance the state of technology for robotic systems, there is a need for defining complex real-world challenge problems for the multi-agent/robot community to address. A well-defined challenge problem can motivate researchers to aggressively address and overcome core domain challenges that might otherwise take years to solve. As the focus of multi-agent research shifts from the mature domains of UGV and UAVs to USVs, there is a need for outlining well-defined and realistic challenge problems. In this position paper, we define one such problem, flood disaster mitigation. The ability to respond quickly and effectively to disasters is essential to saving lives and limiting the scope of damage. The nature of floods dictates the need for a fast-deployable fleet of low-cost and small autonomous boats that can provide situational awareness (SA), damage assessment and deliver supplies before more traditional emergency response assets can access affected areas. In addition to addressing an essential need, the outlined application provides an interesting challenge problem for advancing fundamental research in multi-agent systems (MAS) specific to the USV domain. In this paper, we define a technical statement of this MAS challenge problem based and outline MAS specific technical constraints based on the associated real-world issues. Core MAS sub-problems that must be solved for this application include coordination, control, human interaction, autonomy, task allocation, and communication. This problem provides a concrete and real-world MAS application that will bring together researchers with a diverse range of expertise to develop and implement the necessary algorithms and mechanisms.

Scaling effects for streaming video vs. static panorama in multirobot search

Camera guided teleoperation has long been the preferred mode for controlling remote robots with other modes such as asynchronous control only used when unavoidable. Because controlling multiple robots places additional demands on the operator we hypothesized that removing the forced pace for reviewing camera video might reduce workload and improve performance. In an earlier experiment participants operated four teams performing a simulated urban search and rescue (USAR) task using a conventional streaming video plus map interface or an experimental interface without streaming video but with the ability to store panoramic images on the map to be viewed at leisure. Operators were more accurate in marking victims on maps using the conventional interface; however, ancillary measures suggested that the asynchronous interface succeeded in reducing temporal demands for switching between robots. This raised the possibility that the asynchronous interface might perform better if teams were larger. In this experiment we evaluate the usefulness of asynchronous video for teams of 4, 8, or 12 robots. Operators in the two conditions were equally successful in finding victims, however, the streaming video maintained its advantage for accuracy in locating victims.