The UAV cooperative formation control design with collision avoidance based on improved artificial potential field

This paper addresses a local minima problem for multiple unmanned aerial vehicles (UAVs) in the process of collision avoidance by using the artificial potential field method, thereby enabling UAVs to avoid the obstacle effectively in 3-D space. The main contribution is to propose a collision avoidance control algorithm based on the virtual structure and the “leader–follower” control strategy in 3-D space that can avoid the obstacle effectively and then track the motion target. The three UAVs constitute the regular triangular formation as the control object, the virtual leader flight trajectory as the expected path, the obstacles as the simplified cylinders, and the artificial potential fields around them as approximately spherical surfaces. The attractive force of the artificial potential field can guide the virtual leader to track the target. At the same time, the follower tracks the leader to maintain the formation flight. The effect of the repulsive force can avoid the collision between the UAVs and arrange the followers such that they are evenly distributed on the spherical surface. Moreover, the follower’s specific order and position are not required. The collision path of the UAV formation depends on the artificial potential field with the two composite vectors, and every UAV may choose the optimal path to avoid the obstacle and reconfigure the regular triangular formation flight after passing the obstacle. The effectiveness of the proposed collision avoidance control algorithm is fully proved by simulation tests. Meanwhile, we also provide a new concept for multi-UAV formation avoidance of an obstacle.

It is a hot topic in the field of road landscape planning technology that a mobile robot can quickly and safely find an optimal path in a multi-obstacle environment. In path planning, in light of the problems of poor cooperation and slow convergence of ant colony algorithm in a known environment, the existing potential field method in the local path environment focuses on avoiding dynamic obstacles but cannot guarantee an optimal path. This study provides a new fusion algorithm for path planning optimization in both static and dynamic environments. Firstly, to prevent slipping into a local optimum, create a pheromone diffusion model and adaptively tweak the population information entropy factor to speed up the convergence speed of the Adaptive Ant Colony Optimization (AACO) algorithm. Secondly, on the basis of the globally planned path, by designing the local stability detection and escape functions, the Improved Artificial Potential Field (IAPF) method is utilized to solve the problem of unreachable destination. Finally, we conduct simulation experiments through MATLAB to compare the indicators for evaluating paths, it verifies that the fusion algorithm proposed in this research has obvious advantages in path planning in both static and dynamic environments.KeywordsPath planningAdaptive ant colony optimizationPopulation information entropyImproved artificial potential field

Forest fire is a natural disaster that causes huge damage to human life and property. Emergency rescue and evacuation actions are crucial in such situations, and path planning is an important part of such actions. Artificial Potential Field (APF) method is a popular path planning method, but it has limitations in complex dynamic environments such as forest fires. In this paper, we propose an improved APF method for forest fire emergency rescue and evacuation path planning. This method uses a dynamic repulsive field to avoid obstacles in the fire scene and an improved attractive field to guide people towards the target. We also combine the classic Rothermel model in forest fire spread to further improve the realism of dynamic spread path planning in forest fires. Computational simulation results show that compared with the original APF method, this method still maintains an acceptable level of computational efficiency as the number of fire scene obstacles increases and is superior to traditional APF method in terms of path planning safety.

This paper presents a modified artificial potential field (APF) based method for an Autonomous Surface Vessel (ASV) obstacles avoidance in a dynamic riverine environment. The APF method is combined with a balance control scheme to achieve river tracking and obstacles avoidance simultaneously. The APF method is further modified modification to comply with marine collision avoidance regulations (COLREGs). The overtaking and head-on scenarios are simulated in MATLAB platform. The simulation results are compared with other APF methods to prove that the proposed method is efficient for the ASV riverine navigation.

In order to ensure the safety and efficiency of the section tracking operation of a guided transport system, a safety headway control method of section tracking based on intersection conditions is proposed in this paper. Considering the difference of signal phase, the evaluation model of road conditions was established based on a fuzzy comprehensive evaluation method, FAGT. Based on the artificial potential field method, the time-varying hybrid artificial potential field (TH-APF) method was proposed, and the tracking headway control algorithm was designed to realize the dynamic control of the tracking headway of the guide transport vehicle. The simulation results verified the effectiveness and applicability of the evaluation model of intersection road conditions; the tracking headway can be maintained at about 120 s. The tracking headway control algorithm of guided transport vehicles can respond to the road conditions and avoid the local optimum of the artificial potential field method, thus improving the operating efficiency.

Wireless ultraviolet light MIMO assisted UAV direction perception and collision avoidance method

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness.

Formation coordination can improve the overall performance of unmanned aerial vehicle (UAV) systems, so it is important to realize coordinated formation control. To solve the problems of ineffective internal collision avoidance and communication distance maintenance in existing formation control methods, a UAV formation control algorithm based on an improved artificial potential field and consensus is proposed. First, an improved artificial potential field obstacle avoidance method is used to solve the problems of collision avoidance and communication distance maintenance within the formation by setting a communication risk zone and a collision risk zone and by defining the dynamic adjustment parameters of the inter‐UAV virtual force to improve the traditional artificial potential field method. Second, a time integrating factor is introduced, and an improved consensus‐based formation control method is employed to overcome the problem of the artificial potential field method falling into the local optimum easily. Simulation experiments were designed to analyse the trends of the UAV formation motion trajectory, inter‐UAV relative distance, attitude, and formation time. The analysis results show that the method can make the UAV formation consistent with the flight trajectory of the intended formation, solving the problems of collision avoidance and communication distance maintenance within the UAV formation.

In this paper, artificial potential field is applied to real-time path planning for obstacle avoidance of manipulators. In this approach, a collision-free path from the initial state to target is planned in three-dimensional space. Artificial potential field is widely used in obstacle avoidance for manipulators, with the advantages of simple structure and real-time performance, but there is a local minima problem in this method. Firstly, the artificial potential field method is used to determine the joint-space path of manipulator, and the path is smoothed to ensure the flexibility of joint-motion. Secondly, the local minima problem is decomposed into two categories: unreachable target and local minima. Virtual obstacles and targets are set to avoid the problem. Finally, regarding time as the optimization target, simulated annealing algorithm is used to search the optimal parameters of artificial potential field. The simulation results verify the effectiveness and adaptability of the algorithm.

A two-stage dynamic collision avoidance algorithm for unmanned surface vehicles based on field theory and COLREGs

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

A novel model predictive artificial potential field based ship motion planning method considering COLREGs for complex encounter scenarios

In this study1, formation control and collision avoidance problem of multi-agent system with switching communication topology has been researched. Due to changes in the communication topology, collision between agents may occur during the course of formation, especially when the distance between agents is very close. Therefore, we have proposed a stable and effective formation control law with collision avoidance capability for the multi-agent system. It is assumed that the topology graph is directed and switching. Besides, the agent is modeled as a single integrator. Our formation control law is the combination of a consensus-based formation control method and a collision avoidance algorithm. The collision avoidance algorithm is adopted based on the artificial potential field (APF) method. In addition, we have adapted the potential function to ensure connectivity. The convergence is guaranteed even in the simultaneous execution of the consensus-based controller and the collision avoidance algorithm. And also, all agents can converge to the desired position rapidly. Some simulations have shown the validation of our scheme.

Ships navigable in the polar regions are troubled by sea ice in a changing state of ice all year round, and collisions with sea ice can easily cause damage to the ship’s hull. In order to solve the above problems, this paper proposes an improved artificial potential field method to avoid ship ice collision. The non-navigable area near the planned voyage is defined by the ice number and ice multiplier, which is regarded as a dynamic dangerous target. Then according to the time and space relationship between the non-navigable area and the ship, the collision risk factor is used to adjust the resultant force in the artificial potential field. Finally, a PID-based MMG ship maneuverability model is used to simulate the navigation process of the ship to solve the problem of local minimum in the artificial potential field. In this paper, simulation experiments on static and dynamic sea ice rinks show that this method will effectively realize the local collision avoidance of ship ice, and the ship can safely pass through the above scenes.

Motion planning for multi-HUG formation in an environment with obstacles