Abstract-In this paper, we address the problem of distributed motion planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complete method which is shown to converge to the centralized prioritized planner solution, and a sparse method in which robots discover collisions probabilistically. Planning is divided into a number of iterations, during which every robot simultaneously and independently computes a planning solution based on other robots' path information from the previous iteration. Paths are exchanged in ways that exploit the cooperative nature of the team and a statistical phenomenon known as the "birthday paradox". Performance is measured in simulated 2D environments with teams of up to 240 robots. We find that in moderately constrained environments, these methods generate solutions of similar quality to a centralized prioritized planner, but display interesting communication and planning time characteristics.I. INTRODUCTION Much previous research has been done on the problem of cooperative path planning. Unfortunately many proposed approaches do not scale to teams of hundreds of robots in constrained environments. One approach which has been shown to be effective for reasonably large teams is prioritized planning [1]. In this approach, robots sequentially plan paths according to a prioritization function. However, this means robots must plan paths in order, resulting in a linear increase in overall planning time with the number of robots. Prioritized planning is also centralized, creating a potential computational and communication bottleneck, as well as a single point of failure.However, in many domains, the strict ordering of sequential planning is likely to be unnecessarily expensive because not all robots need to avoid all other robots. Online prioritized approaches, such as [2] and [3], take advantage of this property by determining which sets of robots need to be planned sequentially by detecting interactions via local observations. In this paper, we describe an approach that distributes prioritized planning, allowing each robot to plan at the same time, then look for collisions between paths and require lower priority robots to replan. The intuition behind this is that if robot paths are not dependent on all other paths, the number of replanning iterations might be quite low and overall planning time will be reduced because no single node needs to plan for each robot in sequence. We prove that distributing the planning in this way still converges to the same result as the centralized planner. Experimental results support the intuition, showing a dramatic reduction in the number of planning iterations. However, because the length

This paper details the design and architecture of a series elastic actuated snake robot, the SEA Snake. The robot consists of a series chain of 1-DOF modules that are capable of torque, velocity and position control. Additionally, each module includes a high-speed Ethernet communications bus, internal IMU, modular electro-mechanical interface, and ARM based on-board control electronics.

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. Participants controlled either 4, 8, or 12 robots. In the fulltask control condition participants both dictated the robots' paths and controlled their cameras to search for victims. In the exploration condition, participants directed the team of robots in order to explore as wide an area as possible. In the perceptual search condition, participants searched for victims by controlling cameras mounted on robots following predetermined paths selected to match characteristics of paths generated under the other two conditions. By decomposing the search and rescue task into exploration and perceptual search subtasks the experiment allows the determination of their scaling characteristics in order to provide a basis for tentative task allocations among humans and automation for controlling larger robot teams. In the fulltask control condition 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.

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 fulltask 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.

No abstract

Abstract-We explore the combined planning of pregrasp manipulation and transport tasks. We formulate this problem as a simultaneous optimization of pregrasp and transport trajectories to minimize overall cost. Next, we reduce this simultaneous optimization problem to an optimization of the transport trajectory with start-point costs and demonstrate how to use physically realistic planners to compute the cost of bringing the object to these start-points. We show how to solve this optimization problem by extending functional gradient-descent methods and demonstrate our planner on two bimanual manipulation platforms.

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.

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.