arXiv subjects

Litong Meng

Publications and source records attributed to Litong Meng.

4 recordsLinked to original sources

arXiv2024

Efficient Collaborative Navigation through Perception Fusion for Multi-Robots in Unknown Environments

For tasks conducted in unknown environments with efficiency requirements, real-time navigation of multi-robot systems remains challenging due to unfamiliarity with surroundings.In this paper, we propose a novel multi-robot collaborative planning method that leverages the perception of different robots to intelligently select search directions and improve planning efficiency. Specifically, a foundational planner is employed to ensure reliable exploration towards targets in unknown environments and we introduce Graph Attention Architecture with Information Gain Weight(GIWT) to synthesizes the information from the target robot and its teammates to facilitate effective navigation around obstacles.In GIWT, after regionally encoding the relative positions of the robots along with their perceptual features, we compute the shared attention scores and incorporate the information gain obtained from neighboring robots as a supplementary weight. We design a corresponding expert data generation scheme to simulate real-world decision-making conditions for network training. Simulation experiments and real robot tests demonstrates that the proposed method significantly improves efficiency and enables collaborative planning for multiple robots. Our method achieves approximately 82% accuracy on the expert dataset and reduces the average path length by about 8% and 6% across two types of tasks compared to the fundamental planner in ROS tests, and a path length reduction of over 6% in real-world experiments.

cs.RO↗

arXiv2024

Nav-SCOPE: Swarm Robot Cooperative Perception and Coordinated Navigation

This paper proposes a lightweight systematic solution for multi-robot coordinated navigation with decentralized cooperative perception. An information flow is first created to facilitate real-time observation sharing over unreliable ad-hoc networks. Then, the environmental uncertainties of each robot are reduced by interaction fields that deliver complementary information. Finally, path optimization is achieved, enabling self-organized coordination with effective convergence, divergence, and collision avoidance. Our method is fully interpretable and ready for deployment without gaps. Comprehensive simulations and real-world experiments demonstrate reduced path redundancy, robust performance across various tasks, and minimal demands on computation and communication.

cs.RO↗

arXiv2024

Highly Efficient Observation Process based on FFT Filtering for Robot Swarm Collaborative Navigation in Unknown Environments

Collaborative path planning for robot swarms in complex, unknown environments without external positioning is a challenging problem. This requires robots to find safe directions based on real-time environmental observations, and to efficiently transfer and fuse these observations within the swarm. This study presents a filtering method based on Fast Fourier Transform (FFT) to address these two issues. We treat sensors' environmental observations as a digital sampling process. Then, we design two different types of filters for safe direction extraction, as well as for the compression and reconstruction of environmental data. The reconstructed data is mapped to probabilistic domain, achieving efficient fusion of swarm observations and planning decision. The computation time is only on the order of microseconds, and the transmission data in communication systems is in bit-level. The performance of our algorithm in sensor data processing was validated in real world experiments, and the effectiveness in swarm path optimization was demonstrated through extensive simulations.

cs.RO↗

arXiv2023

Collaborative Goal Tracking of Multiple Mobile Robots Based on Geometric Graph Neural Network

Multiple mobile robots play a significant role in various spatially distributed tasks.In unfamiliar and non-repetitive scenarios, reconstructing the global map is time-inefficient and sometimes unrealistic. Hence, research has focused on achieving real-time collaborative planning by utilizing sensor data from multiple robots located at different positions, all without relying on a global map.This paper introduces a Multi-Robot collaborative Path Planning method based on Geometric Graph Neural Network (MRPP-GeoGNN). We extract the features of each neighboring robot's sensory data and integrate the relative positions of neighboring robots into each interaction layer to incorporate obstacle information along with location details using geometric feature encoders. After that, a MLP layer is used to map the amalgamated local features to multiple forward directions for the robot's actual movement. We generated expert data in ROS to train the network and carried out both simulations and physical experiments to validate the effectiveness of the proposed method. Simulation results demonstrate an approximate 5% improvement in accuracy compared to the model based solely on CNN on expert datasets. The success rate is enhanced by about 4% compared to CNN, and the flowtime increase is reduced by approximately 18% in the ROS test, surpassing other GNN models. Besides, the proposed method is able to leverage neighbor's information and greatly improves path efficiency in real-world scenarios.

cs.RO↗