EGO-Planner: An ESDF-Free Gradient-Based Local Planner for Quadrotors

Zhou, Xin; Wang, Zhepei; Ye, Hongkai; Xu, Chao; Gao, Fei

doi:10.1109/lra.2020.3047728

Cited by 196 publications

(113 citation statements)

References 19 publications

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“…For UAVs, generating safe flight trajectories to assist human operations is the key to develop an assistive teleoperation system. Most existing planning methods [20], [21], [22] do not consider environmental awareness, which is fatal while the drone navigating fast in complex and unknown environments. Richter et al [23] propose a learning-based method to plan for better visibility to unknown areas.…”

Section: Perception-aware Navigation In Unknown Environmentsmentioning

confidence: 99%

“…5) Collision Avoidance Penalty J c : We adopt the collision evaluation of our previous work EGO-Planner to guide the trajectory with a collision-free path. Readers can refer to [27] and [28] for more detailed explanations.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Finally, they are asked to perform each experiment three times repeatedly and the relevant data is recorded. Our quadrotor system's hardware setups is based on our previous work [27]. The parameters of visibility metric are N = 20, ρ = 0.8.…”

Section: A Implementation Detailsmentioning

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: 99%

Section: Problem Formulationmentioning

confidence: 99%

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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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“…On the down side, trajectory optimization suffers from a lack of guarantee. Many works formulate constraints as soft objective functions [21,20] or use primal-dual interior point methods to handle non-convex constraints [22,19,23], which does not guarantee constraint satisfaction. On the other hand, some techniques [24,25] restrict the solution space to a disjoint convex subset so that efficient solvers are available, but these methods are limited to returning sub-optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier

Ni¹,

Pan²,

Gao³

2021

Preprint

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We propose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into small sub-problems, leading to a low iteration cost and decentralized parameter updates. Our method inherits the theoretical properties of primal interior point method (P-IPM), i.e., guaranteed collision avoidance and homotopy preservation, while being orders of magnitude faster. We have analyzed the convergence and evaluated our method for timeoptimal multi-UAV trajectory optimizations and simultaneous goal-reaching of multiple robot arms, where we take into consider kinematics-, dynamics-limits, and homotopy-preserving collision constraints. Our method highlights 10 − 100× speedup, while generating trajectories of comparable qualities as state-of-theart P-IPM solver.

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“…Inspired by [14][15], some works make the optimization problem unconstrained. [1] formulates unconstrained optimization with ESDF map to achieve aggressive flight with unknown static obstacles, and [2] furthermore saves computation by reducing the dependency on ESDF. Besides, [3] provides a search-based method to avoid computational complexity from the optimal control problem.…”

Section: Related Workmentioning

confidence: 99%

DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles

Xu¹,

Deng²,

Yang³

et al. 2021

Preprint

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Safe UAV navigation is challenging due to the complex environment structures, dynamic obstacles, and uncertainties from measurement noises and unpredictable moving obstacle behaviors. Although plenty of recent works achieve safe navigation in complex static environments with sophisticated mapping algorithms, such as occupancy map and ESDF map, these methods cannot reliably handle dynamic environments due to the mapping limitation from moving obstacles. To address the limitation, this paper proposes a trajectory planning framework to achieve safe navigation considering complex static environments with dynamic obstacles. To reliably handle dynamic obstacles, we divide the environment representation into static mapping and dynamic object representation, which can be obtained from computer vision methods. Our framework first generates a static trajectory based on the proposed iterative corridor shrinking algorithm. Then, reactive chance-constrained model predictive control with temporal goal tracking is applied to avoid dynamic obstacles with uncertainties. The simulation results in various environments demonstrate the ability of our algorithm to navigate safely in complex static environments with dynamic obstacles.

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EGO-Planner: An ESDF-Free Gradient-Based Local Planner for Quadrotors

Cited by 196 publications

References 19 publications

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

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

Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier

DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles

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