Swarm-based planning and control of robotic functions

University of Technology, Sydney. Faculty of Engineering and Information Technology.Basic issues with a robotic task that requires multiple mobile robots moving in formations are to assemble at an initial point in the work space for establishing a desired formation, to maintain the formation while moving, to avoid obstacles by occasionally splitting/deforming and then re-establishing the formation, and to change the shape of the formation upon requests to accommodate new tasks or safety conditions. In the literature, those issues have been often addressed separately. This research proposes a generic framework that allows for tackling these issues in an integrated manner in the optimal formation planning and control context. Within this proposed framework, a leader robot will be assigned and the path for the leader is obtained by utilising a modified A* search together with a vector approach, and then smoothed out to reduce the number of turns and to satisfy the dynamic and kinematic constraints of mobile robots. Next, a reference trajectory is generated for the leader robot. Based on the formation configuration and the workspace environment, desired trajectories for follower robots in the group are obtained. At the lowest level, each robot tracks its own trajectory using a unified tracking controller. The problem of formation initialisation, in which a group of robots, initially scattering in the workspace, is deployed to get into a desired formation shape, is dealt with by using a Discrete Particle Swarm Optimisation (DPSO) technique incorporated with a behaviour-based strategy. The proposed technique aims to optimally assign desired positions for each robot in the formation by minimisation of a cost function associated with the predefined formation shape. Once each robot has been assigned with a desired position, a search scheme is implemented to obtain a collision free trajectory for each robot to establish the formation. Towards optimal maintenance of the motion patterns, the path that has been obtained for robots in the group by using the modified A* search, is further adjusted. For this, the Particle Swarm Optimisation (PSO) technique is proposed to minimise a cost function involving global motion of the formation, with the main objective of preventing unnecessary changes in the follower robot trajectories when avoiding obstacles. A PSO formation motion planning algorithm is proposed to search for motion commands for each robot. This algorithm can be used to initialise the formation or to navigate the formation to its target. The proposed PSO motion planning method is able to maintain the formation subject to the kinematic and velocity constraints. Analytical work of the thesis is validated by extensive simulation of multiple differential drive wheeled mobile robots based on their kinematic models. The techniques proposed in this thesis are also experimentally tested, in part, on two Amigo mobile robots

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Robot Team Formation Control Using Communication Throughput Approach

In this thesis, we consider a team of robots forming a mobile robot network cooperating to accomplish a mission in an unknown but structured environment. The team has no a-priori knowledge of the environment. Robots have limited memory storage capabilities, not enough to map the environment. Each robot also has limited sensor capability and computational power. Due to the need to avoid obstacles and other environment effects, some robots get delayed from the rest. Using tracking controller, the robot team should follow the leader in a flexible formation shape without losing network connectivity, and that was achieved by monitoring the end-to-end throughput level

Coordination and Control for a Team of Mobile Robots in an Unknown Dynamic Environment

This research presents a dual-level control structure for controlling a mobile robot or a group of robots to navigate through a dynamic environment (such as an object is moving in the workspace of a robot). The higher-level controller operates in cooperation with robot’s state estimation and mapping algorithm, Extended Kalman Filter – Simultaneous Localization and Mapping (EKFSLAM), and the lower-level controller (PID) controls the motion of the robot when it, encounters an obstacle, i.e., it reorients the robot to a predefined rebound angle and move it straight to maneuver around the obstacle until the robot is out of the obstacle range. The higher-level controller jumps in as soon as the robot is out of the obstacle range and moves the robot to the goal. The obstacle avoidance technique involves a novel approach to calculate the rebound angle. Further, the research implements the aforementioned technique to a Leader-Follower formation. Simulation and Experimental results have verified the effectiveness of the proposed control law

Nonlinear control of nonholonomic mobile robot formations

In this thesis, the framework developed to control a single nonholonomic mobile robot is expanded to include the control of formations of multiple nonholonomic mobile robots. A combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers typically found in literature --Abstract, page iv

BACKWARD MOTION PLANNING AND CONTROL OF MULTIPLE MOBILE ROBOTS MOVING IN TIGHTLY COUPLED FORMATIONS

This work addresses the development of a distributed switching control strategy to drive the group of mobile robots in both backward and forward motion in a tightly coupled geometric pattern, as a solution for the deadlock situation that arises while navigating the unknown environment. A generalized closed-loop tracking controller considering the leader referenced model is used for the robots to remain in the formation while navigating the environment. A tracking controller using the simple geometric approach and the Instantaneous Centre of Radius (ICR), to drive the robot in the backward motion during deadlock situation is developed and presented. State-Based Modelling is used to model the behaviors/motion states of the proposed approach in MATLAB/STATEFLOW environment. Simulation studies are carried out to test the performance and error dynamics of the proposed approach combining the formation, navigation, and backward motion of the robots in all geometric patterns of formation, and the results are discussed