Formation Obstacle Avoidance Research Articles

Obstacle avoidance control of UAV formation based on distributed model prediction

Aiming at the formation and maintenance problem of UAVs in an obstacle environment, a distributed model predictive control (DMPC) algorithm with no reference trajectory considering system constraints is proposed. In order to deal with the constraint coupling and cost coupling existing in model predictive control (MPC), the assumed trajectory is introduced to design a low conservative compatibility constraint and a cost function of no reference trajectory, so that the algorithm can be executed in a distributed and synchronous manner. Secondly, the terminal constraint is designed based on the speed barrier method to ensure the safety of the terminal domain, and a feasible terminal control input is given. The cost function is taken as a Lyapunov function, combined with the constructed stability constraint, and the iterative feasibility and system stability of the algorithm are analyzed. In addition, in order to take into account the real-time performance, a non-strictly stable DMPC algorithm that can better meet the requirements of formation obstacle avoidance is given on the basis of the proposed algorithm. The effectiveness and superiority of the proposed algorithm are verified by numerical simulation.

Dynamic Optimal Obstacle Avoidance Control of AUV Formation Based on MLoTFWA Algorithm

In addressing the optimal formation obstacle avoidance control problem for Autonomous Underwater Vehicles (AUVs) in environments with unknown and moving obstacles, this paper employs the Modified Fireworks Algorithm based on a Loser Elimination Mechanism (MLoTFWA) and constructs a Distributed Model Predictive Control (DMPC) framework to achieve obstacle avoidance for AUV formations. Initially, a prediction model is established, followed by feedback compensation to mitigate the effects of unknown perturbations. An appropriate fitness function is then formulated, and enhancements such as the loser elimination rule are introduced to optimize the fireworks algorithm. Additionally, the concept of an adaptive DMPC prediction window is proposed to conserve resources. The local and global stability of the DMPC formation control framework is theoretically proven. Simulations verify that the control system based on the DMPC framework ensures safe obstacle avoidance for the formation, maintains formation consistency, and achieves the shortest and smoothest path. The improved fireworks algorithm demonstrates superior performance compared with the original fireworks algorithm and other optimization algorithms. In testing, the improved fireworks algorithm exhibits better adaptability, higher average fitness, and best fitness, along with a significantly faster convergence speed. Compared with the ordinary fireworks algorithm, the convergence speed is reduced by 30%.

Adaptive Factor Fuzzy Controller for Keeping Multi-UAV Formation While Avoiding Dynamic Obstacles

The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness.

Multi-AUV heterogeneous bipartite consensus formation obstacle avoidance algorithm based on event triggering-RMPC under measurement-communication union framework

This paper investigates a robust model predictive control-based event triggering (ET-RMPC) for heterogeneous autonomous underwater vehicle (AUV) bipartite consensus formation in obstacle avoidance. Firstly, a scheme of heterogeneous AUV bipartite formation control base on signed graph theory and implicit interactive measure-communication union framework is proposed. Next, a RMPC based on the proposed framework for heterogeneous AUV bipartite consensus formation is designed. Additionally, to alleviate the computational burden, a novel event-triggering mechanism is devised based on the error between the estimated state and actual state of the AUV formation trajectory. Then, the stability of the system and the upper and lower bounds of the trigger interval are analyzed based on innovation and Lyapunov theory. Finally, the effectiveness of the proposed method is validated through bipartite consensus formation tracking and obstacle avoidance scenarios.

Research on obstacle avoidance of multi-AUV cluster formation based on virtual structure and artificial potential field method★

The formation of autonomous underwater vehicle (AUV) in the three-dimensional environment is very important for carrying out complex ocean tasks. During formation sailing, it is necessary to consider avoiding obstacles while maintaining the formation. In this paper, an AUV formation obstacle avoidance method based on virtual structure and artificial potential field method is proposed. Firstly, a complete mathematical modeling of AUV is carried out, and a sliding mode position tracker based on virtual position points is designed based on this model. Then the tracking points of each AUV in the formation are calculated according to the expected trajectory and formation feature structure to ensure that the spacing between AUVs meets the requirements of the formation. Finally, the artificial potential field method is introduced into the position tracker to avoid obstacles between the obstacle and the AUV. Five groups of simulation experiments of parallel formation, vertical formation, cross formation, triangular formation and unknown complex environment are designed. The formation scalability of the algorithm is simulated and compared with the virtual leader method in related references. Finally, KKSwarm platform is used to conduct a real simulation in 2D environment to verify the effectiveness of the proposed algorithm.The experimental results show that the proposed method can effectively realize the formation navigation of multiple underwater vehicles and successfully avoid obstacles, which provides an effective method to solve the problem of the formation obstacle avoidance of underactuated AUV.

Finite-Time Output-Feedback Formation Control for High-Order Nonlinear Multiagent Systems With Obstacle Avoidance

This paper investigates the finite-time formation control problem for high-order nonlinear multiagent systems (MASs) with the consideration of obstacle avoidance, unmeasurable states and dead-zone input. A neural networks k-filter observer is designed to estimate the unmeasurable states and cope with the problem of dead-zone input. Also, by using an integral-multiplicative tangent Lyapunov-barrier function (LBF), the obstacle avoidance mission can be completed for MASs without dynamic mismatching. Then, combining the finite-time method and the backstepping theory, an output-feedback finite-time controller is designed for high-order nonlinear MASs. Theoretical analysis indicates that the control algorithm can not only make agents accomplish the formation obstacle avoidance task but also guarantee the boundedness of signals in the system. A simulation demonstrates the effectiveness of the proposed control algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article studies the formation obstacle avoidance problem for MASs which plays an important role in transportation, exploration and rescue activities. Unmeasurable states and dead-zone input, which often occur in practice, are also considered and handled by a neural networks k-filter observer. An integral-multiplicative tangent LBF is utilized to complete the obstacle avoid task which is necessary for actual systems. Moreover, to be more feasible in industrial applications, the finite-time control strategy is used in the controller design.

Fixed-Wing UAV Formation Path Planning Based on Formation Control: Theory and Application

Formation path planning is a significant cornerstone for unmanned aerial vehicle (UAV) swarm intelligence. Previous methods were not suitable for large-scale UAV formation, which suffered from poor formation maintenance and low planning efficiency. To this end, this paper proposes a novel millisecond-level path planning method appropriate for large-scale fixed-wing UAV formation, which consists of two parts. Instead of directly planning paths independently for each UAV in the formation, the proposed method first introduces a formation control strategy. It controls the chaotic UAV swarm to move as a single rigid body, so that only one planning can obtain the feasible path of the entire formation. Then, a computationally lightweight Dubins path generation method with a closed-form expression is employed to plan feasible paths for the formation. During flight, the aforementioned formation control strategy maintains the geometric features of the formation and avoids internal collisions within the formation. Finally, the effectiveness of the proposed framework is exemplified through several simulations. The results show that the proposed method can not only achieve millisecond-level path planning for the entire formation but also excellently maintain formation during the flight. Furthermore, simple formation obstacle avoidance in a special case also highlights the application potential of the proposed method.

A dynamic obstacle avoidance method for collaborative robots based on trajectory optimization

Background Collision detection is crucial in the design of robot planning algorithms. Efficient distance sensors can provide high-resolution environmental collision information to the robot's planning algorithm. However, this also leads to the robot obstacle avoidance performance being limited by the performance of the sensors. Therefore, it becomes a challenge to achieve efficient obstacle avoidance with low-resolution environmental information. Methods First, we use a self-developed capacitive array non-contact distance sensing flexible surface for sensing the proximity of colliding objects. Second, we designed an optimization-based dynamic obstacle avoidance planning algorithm, using only the minimum separation distance and penetration direction as obstacle avoidance information, and referring to the idea of stochastic gradient descent, using real-time collision avoidance information to do single-step optimization adjustment. Results We conducted the dynamic obstacle avoidance test experiment by connecting the electronic skin to the semi-physical prototype and the full physical prototype. The experiments show that efficient dynamic obstacle avoidance can be realized under the maximum effective range of only 5~7cm, and it has strong flexibility to avoid different shapes of dynamic obstacles in a non-contact manner, and finally arrive at the target position. Conclusions In this paper, an online obstacle avoidance planning algorithm designed based on an optimization method that is not limited to the shape of obstacles is proposed, and the effectiveness of the algorithm is verified by physical experiments in combination with a self-developed flexible distance sensing surface. It is of great significance for the safe operation of human-robot interaction in collaborative robots.

A Scheme for Cooperative-Escort Multi-Submersible Intelligent Transportation System Based on SDN-Enabled Underwater IoV

As an emerging multi-submersible system, Human Occupied Vehicle (HOV) under a convoy of a set of Autonomous Underwater Vehicles (AUVs) is regarded as the future framework for underwater exploration. In this work, to improve the interoperability and communication efficiency of the multi-submersible formations, we treat the multi-submersible system as a paradigm of the underwater Internet of Vehicle (IoV) and show how to utilize the Software-Defined Networking (SDN) technique to optimize the system architecture. With the assistance of SDN, we consider the ocean current factors and propose an artificial flow potential field algorithm that combines the artificial potential field algorithm and the gradient descent algorithm, to plan the path for the multi-submersible system. In particular, to improve the safety and efficiency of path planning, we propose a dual leader-follower algorithm-based escort formation obstacle avoidance mechanism for dealing with all categories of obstacle avoidance situations. Simulation tests show that the proposed scheme performs better in data delivery among the multi-submersible system, at a lower energy cost. And it shows high stability and strong practicability in multi-submersible formation control and path planning, respectively.

Multiple Autonomous Underwater Vehicle Formation Obstacle Avoidance Control Using Event-Triggered Model Predictive Control

In this paper, multiple autonomous underwater vehicle (multi-AUV) formation control with obstacle avoidance ability in 3D complex underwater environments based on an event-triggered model predictive control (EMPC) is proposed. Firstly, multi-AUV motion model systems are developed. The navigation reference trajectory of the follower AUVs can be obtained using a multi-AUV relative motion model. Secondly, in order to overcome the speed jump and obstacle avoidance problem in multi-AUV systems, compatibility constraints are presented in MPC that limit the uncertainty deviation of each AUV. The event-triggered mechanism (ET) is designed to decrease the computational load, which is based on the error between the optimal predicted and current state of the AUV. Finally, the effectiveness and superiority of the proposed algorithm are confirmed via simulation and compared with those of other algorithms.

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.

Collision Avoidance Second Order Sliding Mode Control of Satellite Formation with Air-Floated Platform Semi-Physical Simulation

As the number of satellites in orbit increases, the issue of flight safety in spacecraft formation orbit control has become increasingly prominent. With this in mind, this paper designs a second-order terminal sliding mode controller for spacecraft formation obstacle avoidance based on an artificial potential function (APF). To demonstrate the effectiveness of the controller, this paper first constructs a Lyapunov function to prove its stability and then verifies its theoretical validity through numerical simulation. Finally, a satellite simulator is used for semi-physical simulation to verify the practical effectiveness of the controller proposed in this paper.

The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments

This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy (IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy. The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

Research on Obstacle Avoidance Control of Multiple UAV Formation based on Genetic Algorithm

The UAV (Unmanned Aerial Vehicle) group cooperative formation flight technology has the advantages of wide coverage, large activity radius, strong overall search ability and high efficiency of the aircraft group. Therefore, it is suitable for various complex tasks in the military field such as battlefield environment reconnaissance, tactical attack and cooperative search. This paper proposes a consistency control strategy based on GA (Genetic Algorithm) and applies it to multi-UAV formation obstacle avoidance, which can effectively solve the collision between UAVs and between UAV formation and obstacles. Demodulate the received ground desired control command, and introduce an additional auxiliary traction acceleration through GA to avoid the local optimal solution. The auxiliary traction acceleration is related to the speed and relative position of UAV and obstacles. It can be used as a disturbance to solve the local optimal solution, and also as an auxiliary acceleration to improve the speed of avoiding moving obstacles. Finally, the rapid formation and obstacle avoidance of UAV fleet during flight are realized, and the survivability of the fleet in the battlefield environment is improved.

Improved Pigeon-Inspired Optimization in an Integrated Obstacle Avoidance Method for Mars UAV Formation

This paper models the Mars UAV formation exploring the surface of Mars, and then the formation obstacle avoidance is brought up with the assumptions of the Mars circumstance and the UAVs. Based on their specialty, constrained Delaunay triangulation, Yen-[Formula: see text] shortest path algorithm, the collaborative function, and the improved pigeon-inspired optimization (PIO) algorithm are integrated to solve the obstacle avoidance for the formation. Since the steering maneuver costs much energy and increases instabilities vulnerable in extraterrestrial exploration, the paper focuses on the route smoothness problem. The PIO is improved to be suitable for smooth routes and is compatible with other PIO variants. The simulation results show that the sum of the steering angle, namely the performance index, is effectively reduced and satisfies the obstacle avoidance requirements for Mars UAV formation.

Fuzzy Adaptive Group Obstacle Avoidance Control for Second-Order Multi-Agent Systems Under Fixed and Switching Topologies

This paper investigates the multi-group formation obstacle avoidance control problem of second-order nonlinear leader-follower multi-agent systems (MASs) with unknown nonlinear function and uncertain input disturbance. To make multi-group MASs achieve global consensus, a distributed global fuzzy adaptive control method is presented. Then, navigation functions are employed to make agents bypass obstacles and reach the target positions. The adaptive method is applied to adjust parameters online which can substantially reduce computation burdens and make trajectories of agents smooth. Without loss of generality, both fixed and switching interaction topologies are all taken into account. After that, through Lyapunov stability theory and algebraic graph theory, the feasibility of the method proposed in this paper has been proved. Finally, simulation examples are given to validate the effectiveness of the method proposed in this paper.

Multi-Robot Learning Dynamic Obstacle Avoidance in Formation With Information-Directed Exploration

This paper presents an algorithm that generates distributed collision-free velocities for multi-robot while maintain formation as much as possible. The adaptive formation problem is cast as a sequential decision-making problem, which is solved using reinforcement learning that trains several distributed policies to avoid dynamic obstacles on the top of consensus velocities. We construct the policy with Bayesian Linear Regression based on a neural network (called BNL) to compute the state-action value uncertainty efficiently for sequential decision making. The information-directed sampling is applied in our BNL policy to achieve efficient exploration. By further combining the distributional reinforcement learning, we can estimate the intrinsic uncertainty of the state-action value globally and more accurately. For continuous control tasks, efficient exploration can be achieved by optimizing a policy with the sampled action value function from a BNL model. Through our experiments in some contextual Bandit and sequential decision-making tasks, we show that exploration with the BNL model has improved efficiency in both computation and training samples. By augmenting the consensus velocities with our BNL policy, experiments on Multi-Robot navigation demonstrate that adaptive formation is achieved.

Soft formation control for unmanned surface vehicles under environmental disturbance using multi-task reinforcement learning

This paper proposes a distributed soft formation collision avoidance strategy to address the problem of formation obstacle avoidance in complex scenario under the environment interference where unmanned surface vehicles (USVs) are restricted in observation and possess no global reference frame for localisation. A multi-task training framework for formation control is also developed based on the motion characteristics of USVs and the leader–follower method. The soft actor–critic (SAC) reinforcement learning algorithm is adapted to construct agents. With elaborated auxiliary tasks, the soft formation algorithm demonstrates strong resilience to disturbances caused by unknown environmental loads or obstacle avoidance needs with only partial information about the environmental state, thus maintaining the formation shape. The proposed trajectory communication system and policy sharing mechanism allow USVs to approach the destination and avoid collisions whilst maintaining a noncompact formation that can be broken up temporarily to improve obstacle avoidance and can be restored quickly when the obstacle is avoided. Trained agents can be applied to different formations of varying sizes. Simulation results demonstrate the feasibility and effectiveness of the proposed method in different complex sea scenarios and its robustness to unknown environmental disturbances.

Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.