Visibility-aware Trajectory Optimization with Application to Aerial Tracking

Wang, Qianhao; Gao, Yuman; Ji, Jialin; Chen, Xu; Gao, Fei

doi:10.48550/arxiv.2103.06742

Cited by 6 publications

(17 citation statements)

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“…However, when operators of different levels perform various teleoperation tasks, it is difficult to accurately identify which areas are unknown to them, since it is scarcely possible for them to perceive every detail in the field of view (FOV). In this paper, we extend our previous work [9] to equip our assistive teleoperation system with environmental awareness. Instead of identifying unknown regions and defining task-specific objectives, our method successfully increases confidence and safety when operators remotely teleoperate the drone in complex environments.…”

Section: Perception-aware Navigation In Unknown Environmentsmentioning

confidence: 90%

“…For all clustered objects afterward, we merge it with the margin object as M, like Fig. 3(d In this section, based on our previous work [9], we describe an analytical visibility metric that can be easily formulated into a in cost function. Then we propose a trajectory optimizer that refines the path obtained from Sec.…”

Section: Gaze Enhanced Intention Guiding Topological Path Generationmentioning

confidence: 99%

“…( Based on previous work [9], we improve penalty function of visibility with Eq.2, which will be detailed in V-C. As shown in Fig. 4(b), by squeezing out obstacles inside the spherical areas, we effectively increase the visibility of the target.…”

Section: A Analytical Visibility Metricmentioning

confidence: 99%

“…Therefore, a robust flying assistance system should not only generate safe motions using operator's intention, but also assist operators to do timely decisions with environmental awareness. Recently, many works [7], [8], including our previous research [9] start to consider perception awareness by planning the drone to observe and avoid unknown obstacles actively. Nevertheless, these works cannot be directly applied to assistive aerial teleoperation (details in Sec.II-C).…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, these works cannot be directly applied to assistive aerial teleoperation (details in Sec.II-C). To address this issue, we propose a novel perception-aware planner by improving our previous work [9] to enhance the visibility of where the operator's intention refers. Further-more, as Fig.…”

Section: Introductionmentioning

confidence: 99%

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GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

Wang¹,

He²,

Zhiren³

et al. 2021

Preprint

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Gaze is an intuitive and direct way to represent the intentions of an individual. However, when it comes to assistive aerial teleoperation which aims to perform operators' intention, rare attention has been paid to gaze. Existing methods obtain intention directly from the remote controller (RC) input, which is inaccurate, unstable, and unfriendly to nonprofessional operators. Further, most teleoperation works do not consider environment perception which is vital to guarantee safety. In this paper, we present GPA-Teleoperation, a gaze enhanced perception-aware assistive teleoperation framework, which addresses the above issues systematically. We capture the intention utilizing gaze information, and generate a topological path matching it. Then we refine the path into a safe and feasible trajectory which simultaneously enhances the perception awareness to the environment operators are interested in. Additionally, the proposed method is integrated into a customized quadrotor system. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons verify that the proposed system is reliable, robust and applicable to even unskilled users. We will release the source code 1 of our system to benefit related researches. † Equal contribution.

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Section: Perception-aware Navigation In Unknown Environmentsmentioning

confidence: 90%

Section: Gaze Enhanced Intention Guiding Topological Path Generationmentioning

confidence: 99%

Section: A Analytical Visibility Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

Wang¹,

He²,

Zhiren³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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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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Visibility-Aware Navigation With Batch Projection Augmented Cross-Entropy Method Over a Learned Occlusion Cost

Masnavi

Shrestha

Mishra

et al. 2022

IEEE Robot. Autom. Lett.

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We present two real-time trajectory optimizers based on the Cross-Entropy Method for visibility-aware navigation. The two approaches differ in handling inequality constraints stemming from bounds on motion derivatives, collision avoidance, tracking error, etc. Our first optimizer augments the inequalities into the cost function, while the second one relies on a novel GPU accelerated batch projection algorithm. We adopt a learningbased approach to ensure a fast query of the occlusion cost arising from the environment. Specifically, we train a neural network to compute the occlusion directly from the point obstacles generated from LiDAR or RGB-D sensors. Our learned occlusion model can be queried up to 3x faster than the approaches based on distance computation from occupancy or voxel maps. We improve the state-of-the-art in the following aspects. First, our optimizers do not require any explicit map building and can thus adapt on the fly to the changes in the environment. Second, we outperform existing approaches in target tracking applications in maintaining target visibility and success rate while being competitive in acceleration effort and computation time.

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Visibility-aware Trajectory Optimization with Application to Aerial Tracking

Cited by 6 publications

References 0 publications

GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

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

Visibility-Aware Navigation With Batch Projection Augmented Cross-Entropy Method Over a Learned Occlusion Cost

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