314 research outputs found
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Conflict-Based Search is one of the most popular methods for multi-agent path
finding. Though it is complete and optimal, it does not scale well. Recent
works have been proposed to accelerate it by introducing various heuristics.
However, whether these heuristics can apply to non-grid-based problem settings
while maintaining their effectiveness remains an open question. In this work,
we find that the answer is prone to be no. To this end, we propose a
learning-based component, i.e., the Graph Transformer, as a heuristic function
to accelerate the planning. The proposed method is provably complete and
bounded-suboptimal with any desired factor. We conduct extensive experiments on
two environments with dense graphs. Results show that the proposed Graph
Transformer can be trained in problem instances with relatively few agents and
generalizes well to a larger number of agents, while achieving better
performance than state-of-the-art methods.Comment: Accepted by ICRA 202
Graph Neural Networks for Decentralized Multi-Robot Path Planning
Effective communication is key to successful, de- centralized, multi-robot path planning. Yet, it is far from obvious what information is crucial to the task at hand, and how and when it must be shared among robots. To side-step these issues and move beyond hand-crafted heuristics, we propose a combined model that automatically synthesizes local communication and decision-making policies for robots navigating in constrained workspaces. Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among robots. We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations. We evaluate our method in simulations by navigating teams of robots to their destinations in 2D cluttered workspaces. We measure the success rates and sum of costs over the planned paths. The results show a performance close to that of our expert algorithm, demonstrating the validity of our approach. In particular, we show our model’s capability to generalize to previously unseen cases (involving larger environments and larger robot teams).We gratefully acknowledge the support of ARL grant DCIST CRA W911NF-17-2-0181. A. Prorok was supported by the Engineering and Physical Sciences Research Council (grant EP/S015493/1). We gratefully acknowledge their support
See What the Robot Can't See: Learning Cooperative Perception for Visual Navigation
We consider the problem of navigating a mobile robot towards a target in an
unknown environment that is endowed with visual sensors, where neither the
robot nor the sensors have access to global positioning information and only
use first-person-view images. In order to overcome the need for positioning, we
train the sensors to encode and communicate relevant viewpoint information to
the mobile robot, whose objective it is to use this information to navigate as
efficiently as possible to the target. We overcome the challenge of enabling
all the sensors (even those that cannot directly see the target) to predict the
direction along the shortest path to the target by implementing a
neighborhood-based feature aggregation module using a Graph Neural Network
(GNN) architecture. In our experiments, we first demonstrate generalizability
to previously unseen environments with various sensor layouts. Our results show
that by using communication between the sensors and the robot, we achieve up to
2.0x improvement in SPL (Success weighted by Path Length) when compared to a
communication-free baseline. This is done without requiring a global map,
positioning data, nor pre-calibration of the sensor network. Second, we perform
a zero-shot transfer of our model from simulation to the real world. Laboratory
experiments demonstrate the feasibility of our approach in various cluttered
environments. Finally, we showcase examples of successful navigation to the
target while the sensor network layout is dynamically reconfigured.Comment: Reformatting for IROS with updated result
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