# Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments

arXiv: https://arxiv.org/abs/2310.10863 · HTML: https://mlsanity.com/paper/2310.10863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Autonomous Exploration Development Environment and the Planning Algorithms](https://mlsanity.com/paper/2110.14573/) (2021)
- [Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments](https://mlsanity.com/paper/1904.02319/) (2019)
- [Efficient Informative Sensing using Multiple Robots](https://mlsanity.com/paper/1401.3462/) (2014)
- [Submodular Trajectory Optimization for Aerial 3D Scanning](https://mlsanity.com/paper/1705.00703/) (2017)
- [Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making](https://mlsanity.com/paper/1910.06988/) (2019)
- [AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.06343/) (2020)
- [Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization](https://mlsanity.com/paper/2007.02084/) (2020)
- [Resilient Active Information Acquisition with Teams of Robots](https://mlsanity.com/paper/2103.02733/) (2021)
- [Active Perception based Formation Control for Multiple Aerial Vehicles](https://mlsanity.com/paper/1901.07813/) (2019)
- [Scalable Distributed Planning for Multi-Robot, Multi-Target Tracking](https://mlsanity.com/paper/2107.08550/) (2021)
- [Do You See What I See? Coordinating Multiple Aerial Cameras for Robot Cinematography](https://mlsanity.com/paper/2011.05437/) (2020)
- [3D Human Reconstruction in the Wild with Collaborative Aerial Cameras](https://mlsanity.com/paper/2108.03936/) (2021)
- [Autonomous Execution of Cinematographic Shots with Multiple Drones](https://mlsanity.com/paper/2006.12163/) (2020)
- [Image-Based Multi-UAV Tracking System in a Cluttered Environment](https://mlsanity.com/paper/2109.07079/) (2021)
- [Autonomous Planning for Multiple Aerial Cinematographers](https://mlsanity.com/paper/2005.07237/) (2020)
- [Energy-Aware, Collision-Free Information Gathering for Heterogeneous Robot Teams](https://mlsanity.com/paper/2208.00262/) (2022)
- [CineTransfer: Controlling a Robot to Imitate Cinematographic Style from a Single Example](https://mlsanity.com/paper/2310.03953/) (2023)
- [Volumetric Objectives for Multi-Robot Exploration of Three-Dimensional Environments](https://mlsanity.com/paper/2103.11625/) (2021)
- [Multi-robot Task Assignment for Aerial Tracking with Viewpoint Constraints](https://mlsanity.com/paper/2205.15483/) (2022)
- [Onboard View Planning of a Flying Camera for High Fidelity 3D Reconstruction of a Moving Actor](https://mlsanity.com/paper/2308.00134/) (2023)

## Built on by (most cited first, 2 of 2)

- [CoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perception](https://mlsanity.com/paper/2509.17107/) (2025)
- [EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception](https://mlsanity.com/paper/2610.00319/) (2026)

## Abstract (arXiv)

Deployment of teams of aerial robots could enable large-scale filming of dynamic groups of people (actors) in complex environments for applications in areas such as team sports and cinematography. Toward this end, methods for submodular maximization via sequential greedy planning can enable scalable optimization of camera views across teams of robots but face challenges with efficient coordination in cluttered environments. Obstacles can produce occlusions and increase chances of inter-robot collision which can violate requirements for near-optimality guarantees. To coordinate teams of aerial robots in filming groups of people in dense environments, a more general view-planning approach is required. We explore how collision and occlusion impact performance in filming applications through the development of a multi-robot multi-actor view planner with an occlusion-aware objective for filming groups of people and compare with a formation planner and a greedy planner that ignores inter-robot collisions. We evaluate our approach based on five test environments and complex multi-actor behaviors. Compared with a formation planner, our sequential planner generates 14% greater view reward for filming the actors in three scenarios and comparable performance to formation planning on two others. We also observe near identical view rewards for sequential planning both with and without inter-robot collision constraints which indicates that robots are able to avoid collisions without impairing performance in the perception task. Overall, we demonstrate effective coordination of teams of aerial robots in environments cluttered with obstacles that may cause collisions or occlusions and for filming groups that may split, merge, or spread apart.