Formation Control for Moving Target Enclosing via Relative Localization

In this paper, we investigate the problem of controlling multiple unmanned aerial vehicles (UAVs) to enclose a moving target in a distributed fashion based on a relative distance and self-displacement measurements. A relative localization technique is developed based on the recursive least square estimation (RLSE) technique with a forgetting factor to estimates both the ``UAV-UAV'' and ``UAV-target'' relative positions. The formation enclosing motion is planned using a coupled oscillator model, which generates desired motion for UAVs to distribute evenly on a circle. The coupled-oscillator-based motion can also facilitate the exponential convergence of relative localization due to its persistent excitation nature. Based on the generation strategy of desired formation pattern and relative localization estimates, a cooperative formation tracking control scheme is proposed, which enables the formation geometric center to asymptotically converge to the moving target. The asymptotic convergence performance is analyzed theoretically for both the relative localization technique and the formation control algorithm. Numerical simulations are provided to show the efficiency of the proposed algorithm. Experiments with three quadrotors tracking one target are conducted to evaluate the proposed target enclosing method in real platforms.Comment: 8 Pages, accepted by IEEE CDC 202

DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention

Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient cooperation. This paper proposes a cooperative pursuit algorithm named Decentralized Adaptive COOperative Pursuit via Attention (DACOOP-A) by empowering reinforcement learning with artificial potential field and attention mechanisms. An attention-based framework is developed to emphasize important neighbors by concurrently integrating the learned attention scores into observation embedding and inter-robot interaction rules. A KL divergence regularization is introduced to alleviate the resultant learning stability issue. Improvements in data efficiency and generalization are demonstrated through numerical simulations. Extensive quantitative analysis and ablation studies are performed to illustrate the advantages of the proposed modules. Real-world experiments are performed to justify the feasibility of deploying DACOOP-A in physical systems.Comment: 8 Pages; This manuscript has been accepted by IEEE Robotics and Automation Letter

EASpace: Enhanced Action Space for Policy Transfer

Formulating expert policies as macro actions promises to alleviate the long-horizon issue via structured exploration and efficient credit assignment. However, traditional option-based multi-policy transfer methods suffer from inefficient exploration of macro action's length and insufficient exploitation of useful long-duration macro actions. In this paper, a novel algorithm named EASpace (Enhanced Action Space) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available sub-optimal expert policies. Specifically, EASpace formulates each expert policy into multiple macro actions with different execution {times}. All the macro actions are then integrated into the primitive action space directly. An intrinsic reward, which is proportional to the execution time of macro actions, is introduced to encourage the exploitation of useful macro actions. The corresponding learning rule that is similar to Intra-option Q-learning is employed to improve the data efficiency. Theoretical analysis is presented to show the convergence of the proposed learning rule. The efficiency of EASpace is illustrated by a grid-based game and a multi-agent pursuit problem. The proposed algorithm is also implemented in physical systems to validate its effectiveness.Comment: 15 Page

Collaborative Target Tracking in Elliptic Coordinates: a Binocular Coordination Approach

This paper concentrates on the collaborative target tracking control of a pair of tracking vehicles with formation constraints. The proposed controller requires only distance measurements between tracking vehicles and the target. Its novelty lies in two aspects: 1) the elliptic coordinates are used to represent an arbitrary tracking formation without singularity, which can be deduced from inter-agent distances, and 2) the regulation of the tracking vehicle system obeys a binocular coordination principle, which simplifies the design of the control law by leveraging rich physical meanings of elliptic coordinates. The tracking system with the proposed controller is proven to be exponentially convergent when the target is stationary. When the target drifts with a small velocity, the desired tracking formation is achieved within a small margin proportional to the magnitude of the target's drift velocity. Simulation examples are provided to demonstrate the tracking performance of the proposed controller.Comment: 6 pages, 5 figure

A Novel Fusion Scheme for Vision Aided Inertial Navigation of Aerial Vehicles

Vision-aided inertial navigation is an important and practical mode of integrated navigation for aerial vehicles. In this paper, a novel fusion scheme is proposed and developed by using the information from inertial navigation system (INS) and vision matching subsystem. This scheme is different from the conventional Kalman filter (CKF); CKF treats these two information sources equally even though vision-aided navigation is linked to uncertainty and inaccuracy. Eventually, by concentrating on reliability of vision matching, the fusion scheme of integrated navigation is upgraded. Not only matching positions are used, but also their reliable extents are considered. Moreover, a fusion algorithm is designed and proved to be the optimal as it minimizes the variance in terms of mean square error estimation. Simulations are carried out to validate the effectiveness of this novel navigation fusion scheme. Results show the new fusion scheme outperforms CKF and adaptive Kalman filter (AKF) in vision/INS estimation under given scenarios and specifications

Triangular lattice formation in robot swarms with minimal local sensing

Abstract The problem of triangular lattice formation in robot swarms has been investigated extensively in the literature, but the existing algorithms can hardly keep comparative performance from swarm simulation to real multi‐robot scenarios, due to the limited computation power or the restricted field of view (FOV) of robot sensors. Eventually, a distributed solution for triangular lattice formation in robot swarms with minimal sensing and computation is proposed and developed in this study. Each robot is equipped with a sensor with a limited FOV providing only a ternary digit of information about its neighbouring environment. At each time step, the motion command is directly determined by using only the ternary sensing result. The circular motions with a certain level of randomness lead the robot swarms to stable triangular lattice formation with high quality and robustness. Extensive numerical simulations and multi‐robot experiments are conducted. The results have demonstrated and validated the efficiency of the proposed approach. The minimised sensing and computation requirements pave the way for massive deployment at a low cost and implementation within swarms of miniature robots

Localization Framework for Real-Time UAV Autonomous Landing: An On-Ground Deployed Visual Approach

[-5]One of the greatest challenges for fixed-wing unmanned aircraft vehicles (UAVs) is safe landing. Hereafter, an on-ground deployed visual approach is developed in this paper. This approach is definitely suitable for landing within the global navigation satellite system (GNSS)-denied environments. As for applications, the deployed guidance system makes full use of the ground computing resource and feedbacks the aircraft’s real-time localization to its on-board autopilot. Under such circumstances, a separate long baseline stereo architecture is proposed to possess an extendable baseline and wide-angle field of view (FOV) against the traditional fixed baseline schemes. Furthermore, accuracy evaluation of the new type of architecture is conducted by theoretical modeling and computational analysis. Dataset-driven experimental results demonstrate the feasibility and effectiveness of the developed approach

AGDS: adaptive goal-directed strategy for swarm drones flying through unknown environments

Abstract This paper aims to address a challenging problem of a drone swarm for a specific mission by reaching a desired region, through an unknown environment. A bio-inspired flocking algorithm with adaptive goal-directed strategy (AGDS) is proposed and developed for the drones swarmed across unknown environments. Each drone employs a biological visual mechanism to sense obstacles in within local perceptible scopes. Task information of the destination is only given to a few specified drones (named as informed agents), rather than to all other individual drones (uninformed agents). With the proposed flocking swarm, the informed agents operate collectively with the remaining uninformed agents to achieve a common and overall mission. By virtue of numerical simulation, the AGDS and non-adaptive goal-directed strategy (non-AGDS) are both presented and evaluated. Experiments by flying six DJI Tello quadrotors indoor are conducted to validate the developed flocking algorithm. Additional validations within canyon-like complicated scenarios have also been carried out. Both simulation and experimental results demonstrate the efficiency of the proposed swarm flocking algorithm with AGDS

A Lie group based Gaussian Mixture Model distance measure for multimedia comparison

In this paper, we propose a novel method to measure the distance between two Gaussian Mixture Models. The proposed distance measure is based on the minimum cost that must paid to transform from one Gaussian Mixture Model into the other. We parameterize the components of a Gaussian Mixture Model which are Gaussian probability density functions (pdf) as positive definite lower triangular transformation matrices. Then we identify that Gaussian pdfs form a Lie group. Based on Lie group theory, the geodesic length can be used to measure the minimum cost that must paid to transform from one Gaussian pdf into the other. Combining geodesic length with the earth mover\u27s distance, we propose the Lie group earth mover\u27s distance for Gaussian Mixture Models. We test our distance measure in image retrieval. The experimental results indicate that our distance measure is more effective than other measures including the Kullback-Liebler divergence. Copyright 2009 ACM