Integrated Motion Planner for Real-time Aerial Videography with a Drone in a Dense Environment

Jeon, Boseong Felipe; Lee, Yunwoo; Kim, H. Jin

doi:10.1109/icra40945.2020.9196703

Cited by 43 publications

(104 citation statements)

References 16 publications

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“…As discussed in our previous work [8], a larger value of ψ(x p ; x q ) is interpreted to be more robust against the unknown movement of x q . Also, ψ(x p ; x q ) = 0 indicates the occlusion of x q .…”

Section: A Scoring Fields For Safety and Visibilitymentioning

confidence: 65%

“…The whole system including target estimation, mapping, planning, and control runs fully onboard. The code implementations of sensor data processing, prediction, and motion planning can be found https://github.com/icsl-Jeon/zed2_client, https://github.com/icsl-Jeon/chasing_utils, and https://github.com/icsl-Jeon/dual_chaser, respectively putation than our previous work [8]. Based on the score field, Section.…”

Section: B Contributionsmentioning

confidence: 99%

“…In this section, we overview the measures which quantify the safety of a location and its visibility toward a target against obstacles, based on our previous work [8]. Next, as a newly introduced concept in this paper, we define the visibility of a chaser position toward two targets considering the interocclusion and the FOV limit.…”

Section: Preliminarymentioning

confidence: 99%

“…Most of the recent works on the chasing task [5]- [7], [9], [10] adopted a prediction algorithm which considers only minimization of the past observation error without reflecting the presence of obstacles. Although our previous work [8] introduced the prediction algorithm considering obstacles, via-points of the target and the obstacles should be known a priori, which has limited online applications. Also, the proposed prediction could not guarantee the non-intersection with obstacles because the collision avoidance was included as a cost of a non-convex optimization.…”

Section: Forecasting Target Movementmentioning

confidence: 99%

“…They considered the key motion objectives such as the actuation efficiency of the chaser drone and the desired relative distance between the drone and the target. Safety of the chaser drone and visibility of the target were also handled against obstacles based on non-convex optimization [5], [6] or search-based methods [8], [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Autonomous Aerial Dual-Target Following Among Obstacles

et al. 2021

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In contrast to recent developments in online motion planning to follow a single target with a drone among obstacles, a multi-target case with a single chaser drone has been hardly discussed in similar settings. Following more than one target is challenged by multiple visibility issues due to the inter-target occlusion and the limited field-of-view in addition to the possible occlusion and collision with obstacles. Also, reflecting multiple targets into planning objectives or constraints increases the computation load and numerical issues in the optimization compared to the single target case. To resolve the issues, we first develop a visibility score field for multiple targets incorporating the field-of-view limit and inter-occlusion between targets. Next, we develop a fast sweeping algorithm used to compute the field for the suitability of real-time applications. Last, we build an efficient hierarchical planning pipeline to output a chasing motion for multiple targets ensuring key objectives and constraints. For reliable chasing, we also present a prediction algorithm to forecast the movement of targets considering obstacles. The online performance of the proposed algorithm is extensively validated in challenging scenarios, including a large-scale simulation, and multiple real-world experiments in indoor and outdoor scenes. The full code implementation of the proposed method is released here: https://github.com/icsl-Jeon/dual_chaser.

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Section: A Scoring Fields For Safety and Visibilitymentioning

confidence: 65%

Section: B Contributionsmentioning

confidence: 99%

Section: Preliminarymentioning

confidence: 99%

Section: Forecasting Target Movementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Autonomous Aerial Dual-Target Following Among Obstacles

et al. 2021

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Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression

Pan

Zhang

Yang

et al. 2021

IET Cyber-Syst and Robotics

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In recent years, several progressive studies promote the development of aerial tracking. One of the representative studies is our previous work Fast‐Tracker which is applicable to various challenging tracking scenarios. However, it suffers from two main drawbacks: (1) the oversimplification in target detection by using artificial markers and (2) the contradiction between simultaneous target and environment perception with limited onboard vision. In this study, we upgrade the target detection in Fast‐Tracker to detect and localise a human target based on deep learning and non‐linear regression to solve the former problem. For the latter one, we equip the quadrotor system with 360° active vision on a customised gimbal camera. Furthermore, we improve the tracking trajectory planning in Fast‐Tracker by incorporating an occlusion‐aware mechanism that generates observable tracking trajectories. Comprehensive real‐world tests confirm the proposed system's robustness and real‐time capability. Benchmark comparisons with Fast‐Tracker validate that the proposed system presents better tracking performance even when performing more difficult tracking tasks. The cover image is based on the Original Article Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression by Can Cui et al., https://doi.org/10.1049/csy2.12033.

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Real-Time Multi-Convex Model Predictive Control for Occlusion-Free Target Tracking With Quadrotors

et al. 2022

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This paper proposes a Model Predictive Control (MPC) algorithm for target tracking amongst static and dynamic obstacles. Our main contribution lies in improving the computational tractability and reliability of the underlying non-convex trajectory optimization. The result is an MPC algorithm that runs real-time on laptops and embedded hardware devices such as Jetson TX2. Our approach relies on novel reformulations for the tracking, collision, and occlusion constraints that induce a multi-convex structure in the resulting trajectory optimization. We exploit these mathematical structures using the split Bregman Iteration technique, eventually reducing our MPC to a series of convex Quadratic Programs solvable in a few milliseconds. The fast re-planning of our MPC allows for occlusion and collision-free tracking in complex environments even while considering a simple constant-velocity prediction for the target trajectory and dynamic obstacles. We perform extensive bench-marking in a realistic physics engine and show that our MPC outperforms the state-of-the-art algorithms in visibility, smoothness, and computation-time metrics.

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Integrated Motion Planner for Real-time Aerial Videography with a Drone in a Dense Environment

Cited by 43 publications

References 16 publications

Autonomous Aerial Dual-Target Following Among Obstacles

Autonomous Aerial Dual-Target Following Among Obstacles

Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression

Real-Time Multi-Convex Model Predictive Control for Occlusion-Free Target Tracking With Quadrotors

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