Enabling mobile robots to assemble large structures in constrained environments requires planning systems that are both capable of dealing with high complexity and can provide robust execution in the face of run-time failures. We achieve execution robustness through exception handling capabilities that are seamlessly integrated throughout the planning system. Having these recovery mechanisms in place allows us to leverage their capabilities to compensate for problems introduced by approximations made during planning. Turning an apparent problem into an opportunity, we are able to plan complex assembly tasks and execute them robustly without the computational cost associated with more sophisticated planners and apply some of the savings toward recovering from unforeseen run-time errors. We show results where simple planning strategies paired with exception-handling are able to achieve the same outcomes (and in less time) as more elaborate methods would.

In the future, teams of robots will construct outposts on Mars and orbital structures in space. Such tasks will require assembly of a large number of components into structures. Automatic generation of assembly sequences is a difficult and well-studied problem in structured factory environments that are specifically engineered for the assembly task at hand, but it is much less understood in less constrained settings. Instead of representing the problem in the space of the many degrees of freedom of the robots and components involved in the assembly, we approach the problem in the space of valid configurations of the structure to be assembled. We use a graph-based framework to describe valid assembly configurations and feasible assembly steps. In addition to reasoning about kinematic feasibility of assembly steps, we consider the quality of potential configurations with respect to actions for the mobile robots. This method automatically repositions the structure in the workspace so that components to be assembled are most approachable. That is, the sequence of assembly and the position of the structure as it is assembled is chosen so as to maximize the area in which mobile robots can operate to perform their tasks. We present simulation results from a simple five-component assembly with and without the constraints of a narrow confined environment. Results show that our method allows over twice as much space available for robots during assembly. In addition, the plans preserve most of their free-space flexibility in tight workspaces where other planning approaches are left with only a few candidate solutions.

The capability to assemble structures is fundamental to the use of robotics in precursor missions in orbit and on planetary surfaces. We have performed autonomous assembly in neutral buoyancy of elements of a space truss whose mating components require positioning tolerances of the same order of magnitude as the noise in the sensor systems used for the docking. Numerous trade-offs, design decisions, and innovations were made during the development of the assembly system in order to both reduce and compensate for the sensor noise. By using relative positioning, decoupling sensing and manipulation, caching high-quality position estimates, and developing a new waypoint-completion metric, we were able to reduce sensor noise to the sub-millimeter level and autonomously assemble components with millimeter tolerances. In this paper, we discuss our approaches to the problem and report the results of a series of autonomous assembly operations.

Recent research in human-robot interaction has investigated the concept of Sliding, or Adjustable, Autonomy, a mode of operation bridging the gap between explicit teleoperation and complete robot autonomy. This work has largely been in single-agent domains-involving only one human and one robot-and has not examined the issues that arise in multiagent domains. We discuss the issues involved in adapting Sliding Autonomy concepts to coordinated multiagent teams. In our approach, remote human operators have the ability to join, or leave, the team at will to assist the autonomous agents with their tasks (or aspects of their tasks) while not disrupting the team's coordination. Agents model their own and the human operator's performance on subtasks to enable them to determine when to request help from the operator. To validate our approach, we present the results of two experiments. The first evaluates the human/multirobot team's performance under four different collaboration strategies including complete teleoperation, pure autonomy, and two distinct versions of Sliding Autonomy. The second experiment compares a variety of user interface configurations to investigate how quickly a human operator can attain situational awareness when asked to help. The results of these studies support our belief that by incorporating a remote human operator into multiagent teams, the team as a whole becomes more robust and efficient

We present the design and gait generation for an experimental ROLLERBLADER. The ROLLERBLADER is a robot with a central platform mounted on omnidirectional casters and two 3 degree-of-freedom legs. A passive rollerblading wheel is attached to the end of each leg. The wheels give rise to nonholonomic constraints acting on the robot. The legs can be picked up and placed back on the ground allowing a combination of skating and walking gaits. We present two types of gaits for the robot. In the first gait, we allow the legs to be picked up and placed back on the ground while in the second, the wheels are constrained to stay on the ground at all tunes. Experimental gait results for a prototype robot are also presented.