Path Planning with Potential Field-Based Obstacle Avoidance in a 3D Environment by an Unmanned Aerial Vehicle

Batinović, Ana; Goričanec, Jurica; Marković, Lovro; Bogdan, Stjepan

doi:10.1109/icuas54217.2022.9836159

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…Approaches like Particle Swarm Optimization [137] and ant colony swarm optimization [138] have been introduced to address these issues. Rotational vector fields around obstacles have also been employed to prevent local minima during head-on collisions [131], [132]. Reference [132] employed the rotating component of the repulsive force to resolve local minima.…”

Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%

“…Rotational vector fields around obstacles have also been employed to prevent local minima during head-on collisions [131], [132]. Reference [132] employed the rotating component of the repulsive force to resolve local minima. The study [134] introduces an enhanced APF algorithm that incorporates the Fibonacci Sphere technique for route planning of UAVs.…”

Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%

“…Several notable contributions propose aviation dispute resolution strategies. Geometric [135], [143], force field [131], [132], optimum trajectory [119], and Markov Decision Process (MDP) [108], [110] approaches are the most used [144]. Numerous suggested algorithms using mathematical approaches [5], [72], [89], [105], [129] and ML [100], [128] need a strong processor and memory space, which cannot accommodate most UAVs operating in the real world.…”

Section: B Algorithm's Typementioning

confidence: 99%

See 2 more Smart Citations

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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In the contemporary landscape, the escalating deployment of drones across diverse industries has ushered in a consequential concern, including ensuring the security of drone operations. This concern extends to a spectrum of challenges, encompassing collisions with stationary and mobile obstacles and encounters with other drones. Moreover, the inherent limitations of drones, namely constraints on energy consumption, data storage capacity, and processing power, present formidable obstacles in developing collision avoidance algorithms. This review paper explores the challenges of ensuring safe drone operations, focusing on collision avoidance. We explore collision avoidance methods for UAVs from various perspectives, categorizing them into four main groups: obstacle detection and avoidance, collision avoidance algorithms, drone swarm, and path optimization. Additionally, our analysis delves into machine learning techniques, discusses metrics and simulation tools to validate collision avoidance systems, and delineates local and global algorithmic perspectives. Our evaluation reveals significant challenges in current drone collision prevention algorithms. Despite advancements, critical UAV network and communication challenges are often overlooked, prompting a reliance on simulation-based research due to cost and safety concerns. Challenges encompass precise detection of small and moving obstacles, minimizing path deviations at minimal cost, high machine learning and automation expenses, prohibitive costs of real testbeds, limited environmental comprehension, and security apprehensions. By addressing these key areas, future research can advance the field of drone collision avoidance and pave the way for safer and more efficient UAV operations.

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Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%

Section: B Uavs Collision Avoidance Algorithmsmentioning

confidence: 99%

Section: B Algorithm's Typementioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Rezaee,

Hamid,

Hussin

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

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“…During the exploration, the number of frontiers is changing and once the entire environment is explored and a complete map of the environment is created, the exploration process is The path execution and UAV control are achieved using an MPC-based tracking method. The original implementation is presented in [54] while an adapted version of their work is presented in [55] and used in this paper. The main motivation for using this tracking method is that it allows the UAV to smoothly follow and quickly change the UAV trajectory based on the current system state and model dynamics.…”

Section: Path Planning and Navigationmentioning

confidence: 99%

ASEP: An Autonomous Semantic Exploration Planner With Object Labeling

Milas,

Ivanovic,

Petrovic

2023

IEEE Access

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In this paper, we present a novel 3D autonomous exploration planner called the Autonomous Semantic Exploration Planner (ASEP), designed for GPS-denied indoor environments. ASEP combines real-time mapping, exploration, navigation, object detection, and object labeling onboard an Unmanned Aerial Vehicle (UAV) with limited resources. The planner is based on a frontier exploration strategy that utilizes semantic information about the environment in the exploration policy. The policy is extended to incorporate both geometric and semantic information provided by a deep convolutional neural network (DCNN) for semantic segmentation. This semantically-enhanced exploration algorithm directs the exploration toward the quick labeling of all objects of interest in the environment. An extended path planning algorithm continuously checks for path validity, enabling safe navigation in challenging environments. The overall system is designed to be modular and easily extended or replaced with custom modules. The proposed planner is evaluated and analyzed in both simulation and real-world environments using a UAV. Experimental studies demonstrate the effectiveness of the ASEP strategy compared to state-of-the-art methods. Results show that the objects in the environment are explored faster and total exploration time is reduced while the computational time remains consistent regardless of the semantic segmentation processing involved.

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“…Results are obtained from a simulation in Gazebo Simulator by Robot Operating System (ROS). Path planning in unknown environments with an aerial robot is presented in [ 10 ]. The conventional artificial potential field is used, but flight paths are modified in real time depending on a map of the surroundings gathered by a 3D LiDAR sensor.…”

Section: Introductionmentioning

confidence: 99%

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

Kownacki

2024

Sensors

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The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven. Tracking moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns. This allows them and the onboard camera to be oriented toward the tracked target. In simulations, the AR Drone 2.0 model of the Parrot quadcopter is used, which will make it possible to easily verify the method in real flights in future research.

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Path Planning with Potential Field-Based Obstacle Avoidance in a 3D Environment by an Unmanned Aerial Vehicle

Cited by 9 publications

References 30 publications

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues

ASEP: An Autonomous Semantic Exploration Planner With Object Labeling

Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets

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