Hiding Leader's Identity in Leader-Follower Navigation through Multi-Agent Reinforcement Learning

Leader-follower navigation is a popular class of multi-robot algorithms where a leader robot leads the follower robots in a team. The leader has specialized capabilities or mission critical information (e.g. goal location) that the followers lack which makes the leader crucial for the mission's success. However, this also makes the leader a vulnerability - an external adversary who wishes to sabotage the robot team's mission can simply harm the leader and the whole robot team's mission would be compromised. Since robot motion generated by traditional leader-follower navigation algorithms can reveal the identity of the leader, we propose a defense mechanism of hiding the leader's identity by ensuring the leader moves in a way that behaviorally camouflages it with the followers, making it difficult for an adversary to identify the leader. To achieve this, we combine Multi-Agent Reinforcement Learning, Graph Neural Networks and adversarial training. Our approach enables the multi-robot team to optimize the primary task performance with leader motion similar to follower motion, behaviorally camouflaging it with the followers. Our algorithm outperforms existing work that tries to hide the leader's identity in a multi-robot team by tuning traditional leader-follower control parameters with Classical Genetic Algorithms. We also evaluated human performance in inferring the leader's identity and found that humans had lower accuracy when the robot team used our proposed navigation algorithm

Leader–Follower Navigation in Obstacle Environments While Preserving Connectivity Without Data Transmission

In this paper, we propose a control method for leader-follower navigation in obstacle environments while preserving sensing network connectivity without data transmission between robots. Unlike most connectivity-preserving algorithms, the control input is determined in such a way as to not only guarantee connectivity preservation and collision avoidance, but also to ensure that input constraints are not violated at each time step. We also introduce a simple rule for changing network topology depending on environments such that some sensing links are deactivated in order to pass through narrow spaces, while active links are increased in free spaces to keep the group as cohesive as possible. The effectiveness of the proposed method is demonstrated in simulations and experiments

Leader follower pattern formation control using ros for mobile robot application

In this project, the mobile robots with the leader-follower formation control is being created. The formation problem is converted to a trajectory tracking problem and a reference trajectory is generated for each follower. After that, a tracking controller from the literature is applied to drive the robots towards their corresponding desired trajectories and then the formation will be formed as the follower will also move. Our target and objective in this project are to design a mobile robot leader follower with obstacle avoidance while analyze the robot formation control based on the desired position in different environment. In addition, we are also to design a control strategy of the mobile robot formation with obstacle avoidance. In facts, this is the major parts to ensure that all unwanted incident can be avoid after be applied in later. To achieve all the desired objective, a new framework is proposed for implementing the formation control laws on nonholonomic mobile robots based on ROS (Robot Operating System). ROS will be used to be the main system in controlling the hardware and also to create a virtual environment, generate robot model called Turlebot and implement the algorithms such as SLAM. ROS provides some convenient packages with ROS node and the SLAM algorithms that make the formation problem easier to solve. The creation of the hardware parts is using the raspberry pi 3b+ and the OpenCR as the main component while the creation of the virtual environment will be done by using Gazebo and Rviz (ROS Visualization). Our propose work in this project is to design for the turtlebot3 burger equipped with Lidar sensor as the main mobile robot to have the obstacle avoidance and the formation control. The environment of different formation control will be created and will be analyze in this project. As addition, We will create the track and the indoor environment map to simulate all the testing in this project. We will investigate Turtlebot ability to keep the formation with desired velocity. After that, the obstacle avoidance task will be applied to the same Turtlebot to avoid any circumstances that block its way. This Turtlebot soon will be a leader for multiple mobile robots that will follow it as follower with the help of LDS to detect the distance between them with their leader and provide the information for reference trajectory

Connectivity Preservation in Multi-Agent Systems using Model Predictive Control

Flocking of multiagent systems is one of the basic behaviors in the field of control of multiagent systems and it is an essential element of many real-life applications. Such systems under various network structures and environment modes have been extensively studied in the past decades. Navigation of agents in a leader-follower structure while operating in environments with obstacles is particularly challenging. One of the main challenges in flocking of multiagent systems is to preserve connectivity. Gradient descent method is widely utilized to achieve this goal. But the main shortcoming of applying this method for the leader-follower structure is the need for continuous data transmission between agents and/or the preservation of a fixed connection topology. In this research, we propose an innovative model predictive controller based on a potential field that maintains the connectivity of a flock of agents in a leader-follower structure with dynamic topology. The agents navigate through an environment with obstacles that form a path leading to a certain target. Such a control technique avoids collisions of followers with each other without using any communication links while following their leader which navigates in the environment through potential functions for modelling the neighbors and obstacles. The potential field is dynamically updated by introducing weight variables in order to preserve connectivity among the followers as we assume only the leader knows the target position. The values of these weights are changed in real-time according to trajectories of the agents when the critical neighbors of each agent is determined. We compare the performance of our predictive-control based algorithm with other approaches. The results show that our algorithm causes the agents to reach the target in less time. However, our algorithm faces more deadlock cases when the agents go through relatively narrow paths. Due to the consideration of the input costs in our controller, the group of agents reaching the target faster does not necessarily result in the followers consuming more energy than the leader