Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents
arXiv:2304.08932 · doi:10.1109/TSMC.2023.3240023
Abstract
In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.
arXiv commentIEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023
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Cited by in corpus (7)
- Unscented Optimal Control for 3D Coverage Planning with an Autonomous UAV Agent
- VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation
- Cooperative Receding Horizon 3D Coverage Control with a Team of Networked Aerial Agents
- Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response
- Jointly-optimized Trajectory Generation and Camera Control for 3D Coverage Planning
- Rolling Horizon Coverage Control with Collaborative Autonomous Agents
- Probabilistically Robust Trajectory Planning of Multiple Aerial Agents