12 citations · 22 across the 8 of their papers we have counts for
5 papers · 1 filter
Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning
Zhehui Huang, Zhaojing Yang, Rahul Krupani +3
End-to-end deep reinforcement learning (DRL) for quadrotor control promises many benefits -- easy deployment, task generalization and real-time execution capability. Prior end-to-e…
MRNAV: Multi-Robot Aware Planning and Control Stack for Collision and Deadlock-free Navigation in Cluttered Environments
Baskın Şenbaşlar, Pilar Luiz, Wolfgang Hönig +1
Multi-robot collision-free and deadlock-free navigation in cluttered environments with static and dynamic obstacles is a fundamental problem for many applications. We introduce MRN…
DREAM: Decentralized Real-time Asynchronous Probabilistic Trajectory Planning for Collision-free Multi-Robot Navigation in Cluttered Environments
Baskın Şenbaşlar, Gaurav S. Sukhatme
Collision-free navigation in cluttered environments with static and dynamic obstacles is essential for many multi-robot tasks. Dynamic obstacles may also be interactive, i.e., thei…
RLSS: Real-time, Decentralized, Cooperative, Networkless Multi-Robot Trajectory Planning using Linear Spatial Separations
Baskın Şenbaşlar, Wolfgang Hönig, Nora Ayanian
Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this artic…
Probabilistic Trajectory Planning for Static and Interaction-aware Dynamic Obstacle Avoidance
Baskın Şenbaşlar, Gaurav S. Sukhatme
Collision-free mobile robot navigation is an important problem for many robotics applications, especially in cluttered environments. In such environments, obstacles can be static o…