Real-Time Efficient Trajectory Planning for Quadrotor Based on Hard Constraints

Chen, Peng; Jiang, Yongqi; Dang, Yuanjie; Yu, Tianwei; Liang, Ronghua

doi:10.1007/s10846-022-01662-9

Cited by 3 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, they can be divided into offline and online algorithms [8]. The offline algorithms plan the trajectory before the takeoff; hence, they need information about the environment (such as obstacles and fly-zones) in advance [9,10]. Even though they always guarantee a feasible trajectory, these algorithms find a limited range of application, since they cannot be applied in partially unknown and/or dynamic environments where moving vehicles or other obstacles can cause a change to the planned trajectory.…”

Section: Introductionmentioning

confidence: 99%

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Gelli,

Bigazzi,

Boni

et al. 2024

Drones

View full text Add to dashboard Cite

In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D localization using computer vision or advanced sensors. Consequently, much of the existing research focuses on semi-autonomous systems, which rely on ground assistance through the use of external sensors (motion capture systems) and remote computing power. This study addresses the challenge by proposing a fully autonomous trajectory planning solution. By introducing a real-time path planning algorithm based on the minimization of the snap, the optimal trajectory is dynamically recalculated as needed. Evaluation of the algorithm’s performance is conducted in an unknown real-world scenario, utilizing both simulations and experimental data. The algorithm was implemented in MATLAB and subsequently translated to C++ for onboard execution on the drone.

show abstract

Section: Introductionmentioning

confidence: 99%

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Gelli,

Bigazzi,

Boni

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…One is flight path planning performing at a global level. Some conventional and intelligent algorithms are used for global optimal path seeking according to selected criteria (e.g., energy consumption, flight time, or operational costs minimization) and the imposed UAV dynamic and kinematic characteristics constraints [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In this case, the main focus is economical path seeking.…”

mentioning

confidence: 99%

“…The A* algorithm is one of the most popular graph-searching methods, using discretized spatial nodes to calculate the cost value of each traversed node to obtain the feasible path, while a heuristic function is used to search for approximate optimal solutions based on empirical rules under acceptable computational costs to find solutions [9,10]. Moreover, there are some variants of A*, such as Anytime Repairing A* (ARA) [11], Jump Point Search (JPS) [12], and Theta* [13]. Most research efforts have focused on the trade balance between optimality and computational efficiency.…”

mentioning

confidence: 99%

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

Sun,

Wang,

et al. 2024

Drones

View full text Add to dashboard Cite

The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization.

show abstract

Optimal Event-Triggered Control of Quadrotor with Low Power Consumption and High Stability

Zou,

Zeng,

Huang

et al. 2024

Springer Aerospace Technology

View full text Add to dashboard Cite

Real-Time Efficient Trajectory Planning for Quadrotor Based on Hard Constraints

Cited by 3 publications

References 21 publications

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

Optimal Event-Triggered Control of Quadrotor with Low Power Consumption and High Stability

Contact Info

Product

Resources

About