Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field

Guo, Yicong; Liu, Xiaoxiong; Wei, Jiang; Zhang, Weiguo

doi:10.3390/drones7030180

Cited by 7 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From Figure 14, it can be observed that due to the different locations and numbers of UAVs, the agent may determine different avoidance actions when facing collision risks, leading to different trajectories. DDQN is a typical value-based DRL algorithm [18,19], while APF utilizes the repulsive force of obstacles and the gravitational force of target to guide the UAV motion, and is widely used in research on collision avoidance [30,31]. DDQN requires that the agent action space be discrete, which is set to ∆𝜑 ∈ 3°, 0, 3° , ∆𝑍 ∈ 1 m, 0 m, 1 m , ∆𝑉 ∈ 2 m/s, 0 m/s, 2 m/s .…”

Section: Different Numbers Of Uavsmentioning

confidence: 99%

“…DDQN is a typical value-based DRL algorithm [18,19], while APF utilizes the repulsive force of obstacles and the gravitational force of target to guide the UAV motion, and is widely used in research on collision avoidance [30,31]. DDQN requires that the agent action space be discrete, which is set to ∆ϕ ∈ {−3 • , 0, 3 • }, ∆Z ∈ {−1 m, 0 m, 1 m}, ∆V ∈ {−2 m/s, 0 m/s, 2 m/s}.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Zhang

Zhou

et al. 2023

Drones

View full text Add to dashboard Cite

The increasing number of unmanned aerial vehicles (UAVs) in low-altitude airspace is seriously threatening the safety of the urban environment. This paper proposes an adaptive collision avoidance method for multiple UAVs (mUAVs), aiming to provide a safe guidance for UAVs at risk of collision. The proposed method is formulated as a two−layer resolution framework with the considerations of speed adjustment and rerouting strategies. The first layer is established as a deep reinforcement learning (DRL) model with a continuous state space and action space that adaptively selects the most suitable resolution strategy for UAV pairs. The second layer is developed as a collaborative mUAV collision avoidance model, which combines a three-dimensional conflict detection and conflict resolution pool to perform resolution. To train the DRL model, in this paper, a deep deterministic policy gradient (DDPG) algorithm is introduced and improved upon. The results demonstrate that the average time required to calculate a strategy is 0.096 s, the success rate reaches 95.03%, and the extra flight distance is 26.8 m, which meets the real-time requirements and provides a reliable reference for human intervention. The proposed method can adapt to various scenarios, e.g., different numbers and positions of UAVs, with interference from random factors. The improved DDPG algorithm can also significantly improve convergence speed and save training time.

show abstract

Section: Different Numbers Of Uavsmentioning

confidence: 99%

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Zhang

Zhou

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…Potential field-based methods utilize attraction and repulsion forces to guide agents toward desired positions while avoiding collisions. Artificial potential functions [67] define a mathematical representation of the desired formation and drive the swarm toward it. Behavior-based methods focus on defining individual agent behaviors and interactions that collectively result in the desired formation.…”

Section: Swarm Formation Control Strategiesmentioning

confidence: 99%

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process

Phadke,

Medrano,

Sekharan

et al. 2023

Sensors

View full text Add to dashboard Cite

The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one’s research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.

show abstract

“…Path planning consists of planning an optimal path from an initial position to an end position, with quad-rotor UAVs flying in the presence of obstacles on the terrain and the required constraints being met at the same time [10]. At present, the most common algorithms for path planning include Dijkstra's algorithm [11], the A* algorithm [12][13][14], the Artificial Potential Field Method [15], the RRT (Rapid-exploring Random Trees) algorithm [16][17][18][19][20], and the RRT* algorithm [21][22][23][24][25], the last of which is progressively optimized for the RRT algorithm.…”

Section: Introductionmentioning

confidence: 99%

Quad-Rotor Unmanned Aerial Vehicle Path Planning Based on the Target Bias Extension and Dynamic Step Size RRT* Algorithm

Gao,

Hou,

et al. 2024

WEVJ

View full text Add to dashboard Cite

For the path planning of quad-rotor UAVs, the traditional RRT* algorithm has weak exploration ability, low planning efficiency, and a poor planning effect. A TD-RRT* algorithm based on target bias expansion and dynamic step size is proposed herein. First, random-tree expansion is combined with the target bias strategy to remove the blindness of the random tree, and we assign different weights to the sampling point and the target point so that the target point can be quickly approached and the search speed can be improved. Then, the dynamic step size is introduced to speed up the search speed, effectively solving the problem of invalid expansion in the process of trajectory generation. We then adjust the step length required for the expansion tree and obstacles in real time, solve the opposition between smoothness and real time in path planning, and improve the algorithm’s search efficiency. Finally, the cubic B-spline interpolation method is used to modify the local inflection point of the path of the improved RRT* algorithm to smooth the path. The simulation results show that compared with the traditional RRT* algorithm, the number of iterations of path planning of the TD-RRT* algorithm is reduced, the travel distance from the starting position to the end position is shortened, the time consumption is reduced, the path route is smoother, and the path optimization effect is better. The TD-RRT* algorithm based on target bias expansion and dynamic step size significantly improves the planning efficiency and planning effect of quad-rotor UAVs in a three-dimensional-space environment.

show abstract

Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field

Cited by 7 publications

References 46 publications

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process

Quad-Rotor Unmanned Aerial Vehicle Path Planning Based on the Target Bias Extension and Dynamic Step Size RRT* Algorithm

Contact Info

Product

Resources

About