Survey of UAV motion planning

Quan, Lun; Han, Luxin; Zhou, Boyu; Shen, Shaojie; Gao, Fei

doi:10.1049/iet-csr.2020.0004

Cited by 92 publications

(41 citation statements)

References 54 publications

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“…Because of its scalability, limited computational complexity, and adaptability, it has been used previously in the course of realtime adaptive path planning for obstacle avoidance (48) and target detection and tracking (49,50). The interested reader is referred to summaries on path planning for UAVs (46,47,(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61).…”

Section: Classification-driven Adaptive Searchmentioning

confidence: 99%

An autonomous drone for search and rescue in forests using airborne optical sectioning

2021

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Autonomous drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found, in total, 38 of 42 hidden persons. For experiments with predefined flight paths, the average precision was 86%, and we found 30 of 34 cases. For adaptive sampling experiments (where potential findings are double-checked on the basis of initial classification confidences), all eight hidden persons were found, leading to an average precision of 100%, whereas classification confidence was increased on average by 15%. Thermal image processing, classification, and dynamic flight path adaptation are computed on-board in real time and while flying. We show that deep learning–based person classification is unaffected by sparse and error-prone sampling within straight flight path segments. This finding allows search missions to be substantially shortened and reduces the image complexity to 1/10th when compared with previous approaches. The goal of our adaptive online sampling technique is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, because it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.

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Section: Classification-driven Adaptive Searchmentioning

confidence: 99%

An autonomous drone for search and rescue in forests using airborne optical sectioning

2021

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“…The aerial vehicle has a total mass of m = 2.1 kg. The inertial tensor of the vehicle can be obtained via a CAD model, and J = diag(0.12, 0.11, 0.18) kgm 2 . The vehicle equipped with four ESC to drive BLDC motors.…”

Section: Prototype Platformmentioning

confidence: 99%

Driving and tilt‐hovering – An agile and manoeuvrable aerial vehicle with tiltable rotors

Wang

et al. 2021

IET Cyber-Syst and Robotics

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Driving and tilt‐hovering help unmanned aerial vehicles extend their time of operation and the visible range of the equipped sensors. Standard quadrotors are underactuated, whose translational and rotational motions are strongly coupled. As a result, it is impossible to maintain the attitude invariably while changing position. Meanwhile, larger flying speed results in greater air resistance, which is also not desirable. A novel aerial platform with tiltable rotors is proposed. Compared with standard quadrotors, the aerial vehicle has three special capabilities: vector‐flying, tilt‐hovering, and driving. The platform consists of two tilting‐axes to change the direction of thrusts with respect to the aircraft body. To guarantee flexible manoeuvres, a geometric tracking control scheme that adapts on the fly to the thrust vectoring is exploited. Manoeuvrability of the proposed vehicle in different manoeuvres is demonstrated, such as flight with vectoring thrust that can reduce the flight resistance, tilt‐hovering expanding the reachable set of the equipped sensors. The prototype equipped with an electromechanical controller is constructed, and several associated preliminary experiments are performed. The feasibility of the mechanical design and the geometric control strategy are demonstrated.

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“…The search-based methods, a.k.a. grid-based, discretize the environment map into a graph of grids and use a search algorithm to find a collision-free path through these grids [ 6 ]. The two fundamental graph search algorithms are Breadth-First Search (BFS) and Depth-First Search (DFS) [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

“…It has many applications, such as robotic surgery [ 2 ], driverless cars [ 3 ], automation [ 4 ], and mining [ 5 ]. An extensive amount of research has been conducted in the field of path planning for autonomous vehicles [ 3 , 6 ]. However, most of the presented approaches provide non- or sub-optimal solutions and do not account for the dynamics of the vehicle, instead treating it as a kinematic model with velocity inputs [ 1 ], for instance a unicycle or kinematic car [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

Younes

Barczyk

2021

Sensors

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Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.

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Survey of UAV motion planning

Cited by 92 publications

References 54 publications

An autonomous drone for search and rescue in forests using airborne optical sectioning

An autonomous drone for search and rescue in forests using airborne optical sectioning

Driving and tilt‐hovering – An agile and manoeuvrable aerial vehicle with tiltable rotors

Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

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