Cooperative obstacle avoidance using bidirectional artificial potential fields

McIntyre, David; Naeem, Wasif; Xu, Xiandong

doi:10.1109/control.2016.7737540

Cited by 9 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resultant force of the potential field can be obtained from the states of every aircraft and the environment condition at the current time: (22) Considering the upper bounds of the speed, acceleration and turning rate of the UAV, not all collision avoidance paths are flyable. The resultant force must be controlled within the capacity of the UAV.…”

Section: Resultant Force Generation Under Capacity Constraintsmentioning

confidence: 99%

“…Sun et al [21] improved the APF method to generate collision-free paths cooperatively in a 3D dynamic space with multiple UAV. McIntyre et al [22] introduced the bidirectional APF method to avoid the collisions between multiple unmanned ships moving at the same time to the same destination. Based on the APF and the bacterial evolution method, Oscar Montiel et al [23] put forward a path planning approach to prevent collision between moving and static obstacles in a complex, dynamic environment, which can determine the optimal paths in a flexible and efficient manner.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

Zhang

Nie

2019

IEEE Access

View full text Add to dashboard Cite

The key to the integration of unmanned aircrafts in the national airspace is to prevent them from colliding with the other traffics in the airspace. However, it is a great challenge to generate a safe, stable and robust collision-free path for unmanned aircraft system (UAS), a.k.a. unmanned aircraft vehicle (UAV), in real time, if the airspace is highly dynamics and heterogenous (i.e. thronged with aircrafts with different motion states). Based on the dynamic artificial potential field (DAPF) algorithm, this paper provides advisories on how to generate a real-time reactive collision-free path for unmanned aircraft vehicles flying in a dynamic airspace, aiming to ensure the flight safety and minimize the impact on surrounding traffic. Firstly, the safety distance was defined as a variable threshold, which scaled adaptively according to the relative motion states of the surrounding obstacles and the performance of the own UAV. Moreover, the forces of the potential field were improved, such that their magnitudes could be adjusted automatically according to the threat levels of the surrounding obstacles. The threat level of an obstacle depends on the relative position, speed and flight trend between the UAV and the obstacle. In addition, the repulsive force along the relative position of the traditional artificial potential field (APF) was retained, and a steering force was added to change the flight direction of the UAV, aiming to speed up the collision avoidance. Furthermore, the attractive force was modified to help the UAV return to the planned path quickly and stably. After that, the capacity were determined to ensure the feasible and practical path planning for the UAV. Finally, the proposed UAV path-planning method was proved effective, safe, stable and adaptive through simulations. INDEX TERMS Unmanned aircraft system (UAS), unmanned aircraft vehicle (UAV), path planning, collision avoidance, moving obstacle, artificial potential field (APF).

show abstract

Section: Resultant Force Generation Under Capacity Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

Zhang

Nie

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Assuming that the position of the UAV is P UAV , and the target position is P goal , then the gravitational potential field function is [28]:…”

Section: Artificial Potential Field Methodsmentioning

confidence: 99%

“…The literature [22] combines the Lyapunov theorem with the artificial potential field method to solve the local minimum problem. [28] proposes a two-way concept and provides spacing information between UAVs so that they can avoid conflict in order to reach the target point. In [29], a new control force is proposed to transform the constrained UAV trajectory planning problem into an unconstrained UAV trajectory planning problem.…”

Section: Introductionmentioning

confidence: 99%

UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field

2019

View full text Add to dashboard Cite

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the “leader–follower” strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method.

show abstract

“…The second improvement aims to increase the completeness of the algorithm, especially for complex environments. This improvement is based on a bidirectional APF as presented by [McIntyre et al 2016]. However, the initial and goal node exchange is made only when the algorithm enters a local minimum, unlike the original algorithm, which is done at each iteration.…”

Section: Path Planning Improvementsmentioning

confidence: 99%

Path Planning Algorithms in Unknown and Unstructured Environments for UAVs

Rocha

Vivaldini

2022

Anais Estendidos Do XIV Simpósio Brasileiro De Robótica E XIX Simpósio Latino-Americano De Robótica (SBR/LARS Estendido 2022)

View full text Add to dashboard Cite

For an Unmanned Aerial Vehicle to become autonomous, it must perform actions without human interference. Regardless of its application area, path planning is required to carry out a mission. Nowadays, several applications require the UAV to operate in an unknown, 3D, and unstructured environment. Another essential point is considering the movement restrictions in the execution of the movements, where achieving smooth curves reduces the number of stops on 90 degrees curves. One observable aspect among the existing and most used techniques is ”which would be the best technique to work in each of these environments”. This work aims to answer this question with a deeper analysis of all path planning categories: classic, metaheuristic, and machine learning. We develop our planner to analyze these techniques considering completeness, distance, time, CPU usage, memory usage, collision prevention, and robustness. This planner is modular, so it is possible to add new techniques and scenarios to be studied. We also performed tests in simulated and real environments.

show abstract

Cooperative obstacle avoidance using bidirectional artificial potential fields

Cited by 9 publications

References 26 publications

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

A Real-Time Collision Avoidance Strategy in Dynamic Airspace Based on Dynamic Artificial Potential Field Algorithm

UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field

Path Planning Algorithms in Unknown and Unstructured Environments for UAVs

Contact Info

Product

Resources

About