Dr Junyan Hu's Outputs (8)

Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics ⋆ (2024)
Presentation / Conference Contribution
Watson, M., Ren, H., Arvin, F., & Hu, J. (2024, August). Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics ⋆. Presented at 2024 Annual Conference Towards Autonomous Robotic Systems (TAROS), London

Coverage Path Planning (CPP) is an effective approach to let intelligent robots cover an area by finding feasible paths through the environment. In this paper, we focus on using reinforcement learning to learn about a given environment and find the m... Read More about Predator-Prey Q-Learning Based Collaborative Coverage Path Planning for Swarm Robotics ⋆.

A Multi-Agent Path Planning Strategy with Reconfigurable Topology in Unknown Environments (2024)
Presentation / Conference Contribution
Sun, H., Hu, J., Dai, L., & Chen, B. (2024, August). A Multi-Agent Path Planning Strategy with Reconfigurable Topology in Unknown Environments. Presented at 2024 IEEE International Conference on Automation Science and Engineering (CASE), Bari, Italy

Safety-guaranteed trajectories are important for multi-agent systems to work in an unknown constrained environment. To address this issue, this paper proposes a cooperative path planning strategy for a swarm of agents such that they can achieve a tar... Read More about A Multi-Agent Path Planning Strategy with Reconfigurable Topology in Unknown Environments.

Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning (2024)
Presentation / Conference Contribution
Champagnie, K., Chen, B., Arvin, F., & Hu, J. (2024, August). Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning. Presented at 2024 IEEE International Conference on Automation Science and Engineering (CASE), Bari, Italy

Two promising approaches to coverage path planning are reward-based and pheromone-based methods. Reward-based methods allow heuristics to be learned automatically, often yielding a superior performance to hand-crafted rules. On the other hand, pherom... Read More about Online Multi-Robot Coverage Path Planning in Dynamic Environments Through Pheromone-Based Reinforcement Learning.

RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems (2024)
Presentation / Conference Contribution
Agachi, C., Arvin, F., & Hu, J. (2024, August). RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems. Presented at 2024 IEEE International Conference on Intelligent Systems (IS), Varna, Bulgaria

Coordination of multi-agent systems has received significant attention during the past few years owing to its wide real-world applications, such as cooperative exploration, aircraft formation, and autonomous vehicle platooning. To address this issue,... Read More about RRT*-Based Leader-Follower Trajectory Planning and Tracking in Multi-Agent Systems.

Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization (2024)
Presentation / Conference Contribution
Champagnie, K., Arvin, F., & Hu, J. (2024, March). Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization. Presented at 2024 IEEE International Conference on Industrial Technology (ICIT), Bristol, UK

In this paper, we present GEM, a novel approach to online coverage path planning in which a swarm of homogeneous agents act to maximize the entropy of pheromone deposited within their environment. We show that entropy maximization (EM) coincides with... Read More about Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization.

Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems (2023)
Presentation / Conference Contribution
Wu, K., Hu, J., Ding, Z., & Arvin, F. (2023, July). Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems

This paper addresses the bearing-only formation tracking problem for heterogeneous nonlinear multi-robot systems. In contrast to position and distance-based formation algorithms, the robots can only measure the bearing information from their neighbor... Read More about Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems.

Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements (2022)
Presentation / Conference Contribution
Wu, K., Hu, J., Lennox, B., & Arvin, F. (2022, July). Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements. Presented at 2022 European Control Conference (ECC), London, United Kingdom

Inspired by natural swarm collective behaviors such as colonies of bees and schools of fish, coordination strategies in swarm robotics have received significant attention in recent years. In this paper, a mixed formation control design based on edge... Read More about Mixed Controller Design for Multi-Vehicle Formation Based on Edge and Bearing Measurements.

Omnipotent Virtual Giant for Remote Human–Swarm Interaction (2021)
Presentation / Conference Contribution
Jang, I., Hu, J., Arvin, F., Carrasco, J., & Lennox, B. (2021, August). Omnipotent Virtual Giant for Remote Human–Swarm Interaction. Presented at 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), Vancouver, BC, Canada

This paper proposes an intuitive human-swarm interaction framework inspired by our childhood memory in which we interacted with living ants by changing their positions and environments as if we were omnipotent relative to the ants. In virtual reality... Read More about Omnipotent Virtual Giant for Remote Human–Swarm Interaction.