22,336 research outputs found
SkiROS2: A skill-based Robot Control Platform for ROS
The need for autonomous robot systems in both the service and the industrial
domain is larger than ever. In the latter, the transition to small batches or
even "batch size 1" in production created a need for robot control system
architectures that can provide the required flexibility. Such architectures
must not only have a sufficient knowledge integration framework. It must also
support autonomous mission execution and allow for interchangeability and
interoperability between different tasks and robot systems. We introduce
SkiROS2, a skill-based robot control platform on top of ROS. SkiROS2 proposes a
layered, hybrid control structure for automated task planning, and reactive
execution, supported by a knowledge base for reasoning about the world state
and entities. The scheduling formulation builds on the extended behavior tree
model that merges task-level planning and execution. This allows for a high
degree of modularity and a fast reaction to changes in the environment. The
skill formulation based on pre-, hold- and post-conditions allows to organize
robot programs and to compose diverse skills reaching from perception to
low-level control and the incorporation of external tools. We relate SkiROS2 to
the field and outline three example use cases that cover task planning,
reasoning, multisensory input, integration in a manufacturing execution system
and reinforcement learning.Comment: 8 pages, 3 figures. Accepted at 2023 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Teams organization and performance analysis in autonomous human-robot teams
This paper proposes a theory of human control of robot teams based on considering how people coordinate 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. This paper reports an extended analysis of the two conditions from a larger four condition study. In these two "shared pool" conditions Twenty four simulated robots were controlled by teams of 2 participants. Sixty paid participants (30 teams) were recruited to perform the shared pool tasks in which participants shared control of the 24 UGVs and viewed the same screens. Groups in the manual control condition issued waypoints to navigate their robots. In the autonomy condition robots generated their own waypoints using distributed 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 sub-team. Automating path planning improved system performance. Effects of team organization favored operator teams who shared authority for the pool of robots. © 2010 ACM
Home alone: autonomous extension and correction of spatial representations
In this paper we present an account
of the problems faced by a mobile robot given
an incomplete tour of an unknown environment,
and introduce a collection of techniques which can
generate successful behaviour even in the presence
of such problems. Underlying our approach is the
principle that an autonomous system must be motivated
to act to gather new knowledge, and to validate
and correct existing knowledge. This principle is
embodied in Dora, a mobile robot which features
the aforementioned techniques: shared representations,
non-monotonic reasoning, and goal generation
and management. To demonstrate how well this
collection of techniques work in real-world situations
we present a comprehensive analysis of the Dora
system’s performance over multiple tours in an indoor
environment. In this analysis Dora successfully
completed 18 of 21 attempted runs, with all but
3 of these successes requiring one or more of the
integrated techniques to recover from problems
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