An Active Sense and Avoid System for Flying Robots in Dynamic Environments

Chen, Gang; Dong, Wei; Sheng, Xinjun; Zhu, Xiangyang; Ding, Han

doi:10.1109/tmech.2021.3060511

Cited by 29 publications

(23 citation statements)

References 29 publications

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“…As the robotic systems were deployed in dynamic environments, improvements have to be made to instantly represent the occupancy position of dynamic obstacles, such as pedestrians and other robots, and, even further, to predict the future trajectories of dynamic obstacles. An intuitive approach is to leverage independent detection and tracking of moving objects (DATMO) [14], [15], [16] to model the dynamic obstacles and utilize static occupancy maps still to represent the other objects. A prerequisite of DATMO is that the detection model and shape model of the dynamic obstacles are well-trained [3], which conflicts with the unknown environment characters in many tasks.…”

Section: B Occupancy Maps In Dynamic Environmentsmentioning

confidence: 99%

“…A prerequisite of DATMO is that the detection model and shape model of the dynamic obstacles are well-trained [3], which conflicts with the unknown environment characters in many tasks. In addition, difficulties in data association [3] and the trail noise caused by obstacles movements in the static map [15] are intractable. Therefore, improving the map itself directly by considering the dynamic obstacle assumption is required, and the dynamic occupancy map [17], [18] emerges accordingly.…”

Section: B Occupancy Maps In Dynamic Environmentsmentioning

confidence: 99%

“…The first model is the CV model, where the zero velocity situation is excluded, and the second model is a static model. Equation (15) can then be rewritten as:…”

Section: B Mixture Modelmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: B Occupancy Maps In Dynamic Environmentsmentioning

confidence: 99%

Section: B Occupancy Maps In Dynamic Environmentsmentioning

confidence: 99%

See 1 more Smart Citation

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Chen¹,

Wang²,

Peng³

et al. 2022

Preprint

Self Cite

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show abstract

“…Although active vision can also be implemented by moving the yaw angle of the drone, our approach benefits the control of the quadrotor. Due to the lightweight of the camera compared with the body of the quadrotor, this implementation brings negligible influence to the control of the original system while provides a flexible view field [27].…”

Section: A Graph-based Attention Planningmentioning

confidence: 99%

Agile Formation Control of Drone Flocking Enhanced with Active Vision-based Relative Localization

Zhang¹,

Chen²,

Li³

et al. 2021

Preprint

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Relative localization is a prerequisite for the cooperation of aerial swarms. The vision-based approach has been investigated owing to its scalability and independence on communication. However, the limited field of view (FOV) inherently restricts the performance of vision-based relative localization. Inspired by bird flocks in nature, this letter proposes a novel distributed active vision-based relative localization framework for formation control in aerial swarms. Aiming at improving observation quality and formation accuracy, we devise graphbased attention planning (GAP) to determine the active observation scheme in the swarm. Then active detection results are fused with onboard measurements from UWB and VIO to obtain real-time relative positions, which further improve the formation control performance. Real-world experiments show that the proposed active vision system enables the swarm agents to achieve agile flocking movements with an acceleration of 4 m/s 2 in circular formation tasks. A 45.3% improvement of formation accuracy has been achieved compared with the fixed vision system.

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“…For such a targeting task, it requires accurate control simultaneously on the position and attitude of the underactuated quadrotor in a continuous-time horizon. Therefore, smooth feedback control [5]- [7] is not applicable for such a targeting task, and an effective optimal tracking approach should be formulated. Considering the quadrotor's underactuated characteristics and its coupled kinematics with the moving animal, optimal control of this continuous targeting problem involves severe nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Egocentric Regulator for Continuous Targeting Problems of the Underactuated Quadrotor

Lin

Dong

Liu

et al. 2021

Preprint

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Flying robots such as the quadrotor could provide an efficient approach for medical treatment or sensor placing of wild animals. In these applications, continuously targeting the moving animal is a crucial requirement. Due to the underactuated characteristics of the quadrotor and the coupled kinematics with the animal, nonlinear optimal tracking approaches, other than smooth feedback control, are required. However, with severe nonlinearities, it would be time-consuming to evaluate control inputs, and real-time tracking may not be achieved with generic optimizers onboard. To tackle this problem, a novel efficient egocentric regulation approach with high computational efficiency is proposed in this paper. Specifically, it directly formulates the optimal tracking problem in an egocentric manner regarding the quadrotor's body coordinates. Meanwhile, the nonlinearities of the system are peeled off through a mapping of the feedback states as well as control inputs, between the inertial and body coordinates. In this way, the proposed efficient egocentric regulator only requires solving a quadratic performance objective with linear constraints and then generate control inputs analytically. Comparative simulations and mimic biological experiment are carried out to verify the effectiveness and computational efficiency. Results demonstrate that the proposed control approach presents the highest and stablest computational efficiency than generic optimizers on different platforms. Particularly, on a commonly utilized onboard computer, our method can compute the control action in approximately 0.3 ms, which is on the order of 350 times faster than that of generic nonlinear optimizers, establishing a control frequency around 3000 Hz.

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An Active Sense and Avoid System for Flying Robots in Dynamic Environments

Cited by 29 publications

References 29 publications

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Agile Formation Control of Drone Flocking Enhanced with Active Vision-based Relative Localization

An Efficient Egocentric Regulator for Continuous Targeting Problems of the Underactuated Quadrotor

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