Formation control and collision avoidance for multi-agent systems based on position estimation

Xia, Yuanqing; Na, Xitai; Sun, Zhongqi; Chen, Jing

doi:10.1016/j.isatra.2015.12.010

Cited by 93 publications

(31 citation statements)

References 20 publications

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“…The trajectories planning happens in the pyramid frame o p x p y p , because both distributions can be translated into the pyramid frame according to the relationship between the pyramid frame and common frame, as shown in Eq. (13). And the key points are to separate the positions into groups evenly and generate the combined sub-groups meeting Figure 13 (Color online) Common frame (ocxcyc) and pyramid frame (opxpyp).…”

Section: Lemma 1 ( [26])mentioning

confidence: 99%

“…To form the formations, formation control methods play essential roles, which include position-based, displacement-based and distance-based methods, according to the capacity of sensors and the topology of robots [10]. Position-based methods are the most popular and the easiest way to build one formation by updating the global positions online [11][12][13], while displacement-based methods need all the local robot frames to be aligned to the same orientation [14]. Distance-based methods have no special demand for each local robot frame, whereas it is a big challenge to propose the satisfying algorithms [15].…”

Section: Introductionmentioning

confidence: 99%

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Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

Xiao-min

Benozzi

Zerr³

et al. 2019

Sci. China Inf. Sci.

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Multi-robot system has become a research hotspot because of low demand on the sensors' accuracy, high reliability, and high efficiency. To put all the robots together, formation control is a crucial problem. In this paper, we propose a local position-based method to plan trajectories and build a pyramid pattern for a fleet of NAOs in the obstacle-free environment by refering to the position-based method and giving an O(n log n) collision avoidance strategy inspired from one graph theory, where the local positions are estimated from optical sensors. To get the local positions, an integrated image processing method is developped. Firstly a mask-base is generated to store the features of NAOs, and a cross-correlation method is introduced to recognize the NAO. Subsequently, the distance and angle models are proposed to get the local information from a single image. Then, a visual compass is introduced to obtain the orientation of one NAO. After the local information exchange by the WiFi communication, a neighbor-check method is put foward to distinguish the homogeneous NAOs (all the NAOs look like the same). Further, a common frame is constructed as an artificial global frame, and straight non-intercrossing trajectories are planned according to the O(n log n) collision avoidance strategy. At last, the performance of our proposed local position-based method is verified by the simulations with up to 15 robots and the indoor experiments with 3 NAOs in a real environment. The convergence of the method has been demonstrated in both obstacle-free and static obstacle environments.

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Section: Lemma 1 ( [26])mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

Xiao-min

Benozzi

Zerr³

et al. 2019

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…In [18], a stable formation control law and collision avoidance have been investigated for multiple robots with single integrator dynamics under an undirected and connected graph. Formation control and collision avoidance are also presented in [19] for double-integrator systems, which is based on position estimation. In [20], the problem of formation control with collision avoidance for networked Lagrangian systems with uncertain parameters is investigated under directed network topology.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the previous relative studies on the control framework of UUVs for formation tracking problem [1][2][3][4][5][6][7][8], the proposed control framework can allow a group of UUVs to handle simultaneously and continuously the formation tracking problem and collision-obstacle avoidance. Compared with the collision-obstacle avoidance in [18,19], which were studied for single and double integrator systems, the proposed control framework is subject to nonlinearity, parametric uncertainties of UUVs. More specifically, this proposed control framework is a combination of four terms: the consensus control, the neural network control, the robust control, the collision-obstacle avoidance control.…”

Section: Introductionmentioning

confidence: 99%

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

et al. 2020

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This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are used to emphasize the realistic low-cost aspect of underwater drones. The proposed approach is validated by evaluating in different experimental scenarios.

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“…Several classic formation control strategies, including leader-follower, virtual structure, and behavior based methods, were applied in the scientific community [8,9]. For example, formation control of multiple quadrotor UAVs, based on position estimation [10], backstepping design technique [11], and finite time algorithms [12], respectively, was investigated so as to make a construct and keep the formation shape during flying. It should be pointed out that time varying formation tracking problems arise in some scenarios, such as source seeking and target enclosing.…”

Section: Introductionmentioning

confidence: 99%

Event‐Based Time Varying Formation Control for Multiple Quadrotor UAVs with Markovian Switching Topologies

Zhou

Wang

2018

Complexity

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Time varying formation control problem for a group of quadrotor unmanned aerial vehicles (UAVs) under Markovian switching topologies is investigated through a modified dynamic event-triggered control protocol. The formation shape is specified by a time varying vector, which prescribes the relative positions and bearings among the whole agents. Instead of the general stochastic topology, the graph is governed by a set of Markov chains to the edges, which can recover the traditional Markovian switching topologies in line with the practical communication network. The stability proof for the state space origin of the overall closed-loop system is derived from the singular perturbation method and Lyapunov stability theory. An event-triggered formation control protocol in terms of a dynamically varying threshold parameter is delicately carried out, while acquiring satisfactory resource efficiency, and Zeno behavior of triggering time sequences is excluded. Finally, simulations on six quadrotor UAVs are given to verify the effectiveness of the theoretical results.

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Formation control and collision avoidance for multi-agent systems based on position estimation

Cited by 93 publications

References 20 publications

Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

Formation building and collision avoidance for a fleet of NAOs based on optical sensor with local positions and minimum communication

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

Event‐Based Time Varying Formation Control for Multiple Quadrotor UAVs with Markovian Switching Topologies

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