Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

Ma’arif, Alfian; Rahmaniar, Wahyu; Márquez-Vera, Marco Antonio; Nuryono, Aninditya Anggari; Majdoubi, Rania; Çakan, Abdullah

doi:10.1109/comnetsat53002.2021.9530803

Cited by 25 publications

(14 citation statements)

References 52 publications

(50 reference statements)

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“…However, conventional potential field methods used for obstacle avoidance developed for ground robots, are not suitable for aerial vehicles due to their fast movement and inherent instability [38]. We therefore utilize a modified repulsive potential with virtual force E whenever there is an obstacle in the path of the MUAVS, defined as [39];…”

Section: Obstacle Avoidancementioning

confidence: 99%

Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

Aliyu

El-Férik

2022

IEEE Access

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Quadrotor Unmanned Aerial Vehicles (UAVs) are generally underactuated systems and when load is attached for transportation purposes, the system complexity increases. Therefore, the need to appropriately control such systems becomes paramount as they usually navigate in cluttered environments. In this work, we conceptualize the problem of cooperative tracking control for a Multi-agent UAV load system (MUAVLs) whereby each UAV is divided into global position and local attitude subsystems. To ensure that formation is maintained in a desired path, Neural Network Graph-theoretic Distributed Adaptive Control (NNGDAC) is used for the position subsystem with a modified virtual force artificial potential field for obstacle avoidance. Another Adaptive Feedback Linearization (AFBL) controller is also designed for the attitude subsystem which is verified by simulation results.

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Section: Obstacle Avoidancementioning

confidence: 99%

Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

Aliyu

El-Férik

2022

IEEE Access

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“…RRTx [13] refines and repairs the same tree over the entire duration of navigation, while RRT*-FND [14] improves RRT*, making it usable for fast online re-planning when a moving obstacle blocks the path. Instead, Fisher et al [15] use a combination of reachability maps and RRT-connect, while Ma'Arif et al [16] use artificial potential fields to be reactive to dynamic obstacles. As in the case of RRTs, numerous methods following the PRM paradigm have also been proposed, targeting specific flaws of the original algorithm.…”

Section: Related Workmentioning

confidence: 99%

T-PRM: Temporal Probabilistic Roadmap for Path Planning in Dynamic Environments

Huppi¹,

Bartolomei²,

Mascaro³

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Sampling-based motion planners are widely used in robotics due to their simplicity, flexibility and computational efficiency. However, in their most basic form, these algorithms operate under the assumption of static scenes and lack the ability to avoid collisions with dynamic (i.e. moving) obstacles. This raises safety concerns, limiting the range of possible applications of mobile robots in the real world. Motivated by these challenges, in this work we present Temporal-PRM, a novel sampling-based path-planning algorithm that performs obstacle avoidance in dynamic environments. The proposed approach extends the original Probabilistic Roadmap (PRM) with the notion of time, generating an augmented graph-like structure that can be efficiently queried using a time-aware variant of the A* search algorithm, also introduced in this paper. Our design maintains all the properties of PRM, such as the ability to perform multiple queries and to find smooth paths, while circumventing its downside by enabling collision avoidance in highly dynamic scenes with a minor increase in the computational cost. Through a series of challenging experiments in highly cluttered and dynamic environments, we demonstrate that the proposed path planner outperforms other state-ofthe-art sampling-based solvers. Moreover, we show that our algorithm can run onboard a flying robot, performing obstacle avoidance in real time.

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“…In practice, the traditional APF algorithm fails to reach the target point owing to two main issues: the so-called local minima issue 22 and the GNRON issue 23 . In other words, the goal is not reachable when obstacles are nearby.…”

Section: Defects Of Apf Algorithmsmentioning

confidence: 99%

Obstacle avoidance of mobile robots using modified artificial potential field algorithm based on vortex and PID adjustment

Sheng

Wang²

2022

International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022)

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In recent years, autonomous mobile robot has become a key research direction in academia due to their good application prospects. The ability to autonomously avoid obstacles and reach a preset target point is the basic requirement of an intelligent mobile robot. As a common obstacle avoidance algorithm for mobile robots, artificial potential field algorithm (APF) has defects such as local minima problem and GNRON problem. Aiming at the shortcomings of the traditional APF algorithm, this paper proposes an improved artificial potential field algorithm based on point vortex and PID adjustment. The implementation of the improved algorithm is based on the two following main points. Firstly, an irrotational point vortex flow field is added to the original repulsion field of the obstacle to form a composite potential field. Properties of point vortex are used to provide additional virtual deflection force to the robot to avoid getting stuck. Secondly, aiming at GNRON problem, the strength of vortex and the range of action of the vortex repulsive composite potential field are adjusted in real time by means of PID adjustment. Finally, in order to verify the feasibility of the improved algorithm proposed in this paper, a comparison with the traditional APF algorithm was performed in seven environments with different obstacle layouts. The results show that the improved algorithm can be effectively applied to a variety of obstacle environments. Moreover, the local minimal value problem and GNRON problem of the traditional APF algorithm are successfully solved. The mobile robot can flexibly and efficiently avoid obstacles and reach the end point.

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Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

Cited by 25 publications

References 52 publications

Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

Control of Multiple-UAV Conveying Slung Load With Obstacle Avoidance

T-PRM: Temporal Probabilistic Roadmap for Path Planning in Dynamic Environments

Obstacle avoidance of mobile robots using modified artificial potential field algorithm based on vortex and PID adjustment

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