Integrating a PCA Learning Algorithm with the SUSD Strategy for a Collective Source Seeking Behavior

Al-Abri, Said; Maxon, Sean; Zhang, Fumin

doi:10.23919/acc.2018.8431870

Cited by 15 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…which is to maintain a desired distance d ij only along the q i direction [1]. As shown in the right of Fig.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

“…Additionally, V1 → −∞ as θ → 2. This along with the fact that V 1 → ∞ whenever θ → 2 and ∇z c > µ 1 > 0, implies that D 1 is a forward invariant set, and thus θ ∈ [0, 2) for all t. For the forced system f (t, θ, δ), we obtain (35) where 1 . Therefore, using Definition 3.3 of local input-to-state stability in [15], and according to Theorem 4.19 in [23], the origin of the forced system f (t, θ, δ) is locally input-to-state stable.…”

Section: Proof Consider the Domain Dmentioning

confidence: 92%

“…Let Assumption II.3 and Assumption III.1 hold. Suppose the graph is complete and let the motion dynamics of each agent be described by (1). Then, for both source seeking and level curve tracking, λ n (t) < λ q (t) = λ q (t 0 ), where λ q (t 0 ) is the initial maximum variance of the spatial distribution of the agents.…”

Section: B the Distributed Control Lawmentioning

confidence: 99%

“…Preliminary results of this paper appear in our two conference papers [1], [3]. In these two papers, we only considered complete graphs, and in the convergence analysis, we ignored the higher-order terms of the field in both the dynamics derivation and convergence analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking

Al-Abri

Zhang

2022

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

Algorithms for multi-agent systems to locate a source or to follow a desired level curve of spatially distributed scalar fields generally require sharing field measurements among the agents for gradient estimation. Yet, in this paper, we propose a distributed active perception strategy that enables swarms of various sizes and graph structures to perform source seeking and level curve tracking without the need to explicitly estimate the field gradient or explicitly share measurements. The proposed method utilizes a consensus-like Principal Component Analysis perception algorithm that does not require explicit communication in order to compute a local body frame. This body frame is used to design a distributed control law where each agent modulates its motion based only on its instantaneous field measurement. Several stability results are obtained within a singular perturbation framework which justifies the convergence and robustness of the strategy. Additionally, efficiency is validated through various computer simulations and robots implementation in 2-D scalar fields. The active perception strategy leverages the available local information and has the potential to be used in various applications such as modeling information propagation in biological and robotic swarms.

show abstract

“…which is to maintain a desired distance d ij only along the q i direction [1]. As shown in the right of Fig.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Section: Proof Consider the Domain Dmentioning

confidence: 92%

Section: B the Distributed Control Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking

Al-Abri

Zhang

2022

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tian et al [10] proposed an improved Vicsek model with limited field of view, and this model was further extended by Zhang et al [11] with random line-of-sight directions. Among the extensive studies on flocking control problems, most of them used traditional methods such as those based on LQR [12], PCA [13] or a virtual leader [14], which are not effective in dealing with the external disturbance and the nonlinear time-varying nature of the flocking control problem. This paper uses the deep reinforcement learning (DRL) method to complete the flocking task without requiring accurate modeling and sophisticated control design that are required in traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Flocking Control Based on Deep Reinforcement Learning

et al. 2020

View full text Add to dashboard Cite

In this paper, we apply deep reinforcement learning (DRL) to solve the flocking control problem of multi-robot systems in complex environments with dynamic obstacles. Starting from the traditional flocking model, we propose a DRL framework for implementing multi-robot flocking control, eliminating the tedious work of modeling and control designing. We adopt the multi-agent deep deterministic policy gradient (MADDPG) [1] algorithm, which additionally uses the information of multiple robots in the learning process to better predict the actions that robots will take. To address the problems such as low learning efficiency and slow convergence speed of the MADDPG algorithm, this paper studies a prioritized experience replay (PER) [2] mechanism and proposes the Prioritized Experience Replay-MADDPG (PER-MADDPG) algorithm. Based on the temporal difference (TD) error, a priority evaluation function is designed to determine which experiences are sampled preferentially from the replay buffer. In the end, the simulation results verify the effectiveness of the proposed algorithm. It has a faster convergence speed and enables the robot group to complete the flocking task in the environment with obstacles.

show abstract