3,132 research outputs found
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
Collective cluster-based map merging in multi robot SLAM
New challenges arise with multi-robotics, while information integration is among the most important problems need to be solved in this field. For mobile robots, information integration usually refers to map merging . Map merging is the process of combining partial maps constructed by individual robots in order to build a global map of the environment.
Different approaches have been made toward solving map merging problem. Our method is based on transformational approach, in which the idea is to find regions of overlap between local maps and fuse them together using a set of transformations and similarity heuristic algorithms. The contribution of this work is an improvement made in the search space of candidate transformations. This was achieved by enforcing pair-wise partial localization technique over the local maps prior to any attempt to transform them. The experimental results show a noticeable improvement (15-20%) made in the overall mapping time using our technique
An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards
In many domains such as transportation and logistics, search and rescue, or
cooperative surveillance, tasks are pending to be allocated with the
consideration of possible execution uncertainties. Existing task coordination
algorithms either ignore the stochastic process or suffer from the
computational intensity. Taking advantage of the weakly coupled feature of the
problem and the opportunity for coordination in advance, we propose a
decentralized auction-based coordination strategy using a newly formulated
score function which is generated by forming the problem into task-constrained
Markov decision processes (MDPs). The proposed method guarantees convergence
and at least 50% optimality in the premise of a submodular reward function.
Furthermore, for the implementation on large-scale applications, an approximate
variant of the proposed method, namely Deep Auction, is also suggested with the
use of neural networks, which is evasive of the troublesome for constructing
MDPs. Inspired by the well-known actor-critic architecture, two Transformers
are used to map observations to action probabilities and cumulative rewards
respectively. Finally, we demonstrate the performance of the two proposed
approaches in the context of drone deliveries, where the stochastic planning
for the drone league is cast into a stochastic price-collecting Vehicle Routing
Problem (VRP) with time windows. Simulation results are compared with
state-of-the-art methods in terms of solution quality, planning efficiency and
scalability.Comment: 17 pages, 5 figure
Search and Rescue under the Forest Canopy using Multiple UAVs
We present a multi-robot system for GPS-denied search and rescue under the
forest canopy. Forests are particularly challenging environments for
collaborative exploration and mapping, in large part due to the existence of
severe perceptual aliasing which hinders reliable loop closure detection for
mutual localization and map fusion. Our proposed system features unmanned
aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning.
When communication is available, each UAV transmits compressed tree-based
submaps to a central ground station for collaborative simultaneous localization
and mapping (CSLAM). To overcome high measurement noise and perceptual
aliasing, we use the local configuration of a group of trees as a distinctive
feature for robust loop closure detection. Furthermore, we propose a novel
procedure based on cycle consistent multiway matching to recover from incorrect
pairwise data associations. The returned global data association is guaranteed
to be cycle consistent, and is shown to improve both precision and recall
compared to the input pairwise associations. The proposed multi-UAV system is
validated both in simulation and during real-world collaborative exploration
missions at NASA Langley Research Center.Comment: IJRR revisio
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