Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Chen, Gang; Peng, Peng; Zhang, Peihan; Wang, Dong

doi:10.48550/arxiv.2201.06645

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Then our method was compared with three recent works: the Faster method [31], the FDF method [6], and the RAS method [32]. In these works, Faster utilizes a corridor-based method assuming obstacles are static.…”

Section: A Simulation Testsmentioning

confidence: 99%

RAST: Risk-Aware Spatio-Temporal Safety Corridors for MAV Navigation in Dynamic Uncertain Environments

Chen

Shi

et al. 2023

IEEE Robot. Autom. Lett.

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Autonomous navigation of Micro Aerial Vehicles (MAVs) in dynamic and unknown environments is a complex and challenging task. Current works rely on assumptions to solve the problem. The MAV's pose is precisely known, the dynamic obstacles can be explicitly segmented from static ones, their number is known and fixed, or they can be modeled with given shapes. In this letter, we present a method for MAV navigation in dynamic uncertain environments without making any of these assumptions. The method employs a particle-based dynamic map to represent the local environment and predicts it to the near future. Collision risk is defined based on the predicted maps and a series of risk-aware spatio-temporal (RAST) safety corridors are constructed, which are finally used to optimize a dynamically-feasible collision-free trajectory for the MAV. We compared our method with several state-of-the-art works in 12000 simulation tests in Gazebo with the physical engine enabled. The results show that our method has the highest success rate at different uncertainty levels. Finally, we validated the proposed method in real experiments.

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Section: A Simulation Testsmentioning

confidence: 99%

RAST: Risk-Aware Spatio-Temporal Safety Corridors for MAV Navigation in Dynamic Uncertain Environments

Chen

Shi

et al. 2023

IEEE Robot. Autom. Lett.

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“…In the update step, the weight of the newborn particle is calculated separately [30]. The weight update Equations (33) and (34) are reformed to represent the survived particles and the newborn particles separately:…”

Section: Particle Birthmentioning

confidence: 99%

“…To further demonstrate the effectiveness and efficiency of our map in robotic systems. We deployed the DSP-Dynamic map on a mini quadrotor, named Mantis 1 , with a weight of only 320 grams, and utilized a sampling-based motion planning method [34] to realize obstacle avoidance in the environments with static and dynamic obstacles. The point cloud was collected from a Realsense d435 camera, and everything, including mapping and motion planning, was performed on the CPU of a low-cost Up core computing board.…”

Section: Applicationsmentioning

confidence: 99%

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Chen¹,

Wang²,

Peng³

et al. 2022

Preprint

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Dynamic occupancy maps were proposed in recent years to model the obstacles in dynamic environments. Among these maps, the particle-based map offers a solid theoretical basis and the ability to model complex-shaped obstacles. Current particle-based maps describe the occupancy status in discrete grid form and suffer from the grid size problem, namely: large grid size is unfavorable for path planning while small grid size lowers efficiency and causes gaps and inconsistencies. To tackle this problem, this paper generalizes the particle-based map into continuous space and builds an efficient 3D local map. A dual-structure subspace division paradigm, composed of a voxel subspace division and a novel pyramid-like subspace division, is proposed to propagate particles and update the map efficiently with the consideration of occlusions. The occupancy status of an arbitrary point can then be estimated with the cardinality expectation. To reduce the noise in modeling static and dynamic obstacles simultaneously, an initial velocity estimation approach and a mixture model are utilized. Experimental results show that our map can effectively and efficiently model both dynamic obstacles and static obstacles. Compared to the state-of-the-art grid-form particle-based map, our map enables continuous occupancy estimation and substantially improves the performance in different resolutions. We also deployed the map on a quadrotor to demonstrate the bright prospect of using this map in obstacle avoidance tasks of small-scale robotics systems.

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Risk-aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Cited by 2 publications

References 19 publications

RAST: Risk-Aware Spatio-Temporal Safety Corridors for MAV Navigation in Dynamic Uncertain Environments

RAST: Risk-Aware Spatio-Temporal Safety Corridors for MAV Navigation in Dynamic Uncertain Environments

Continuous Occupancy Mapping in Dynamic Environments Using Particles

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