Entrapment/escorting and patrolling missions in multi-robot cluster space control

Abstract-The tasks of entrapping/escorting and patrolling around an autonomous target are presented making use of the multi-robot cluster space control approach. The cluster space control technique promotes simplified specification and monitoring of the motion of mobile multi-robot systems of limited size. Previous work has established the conceptual foundation of this approach and has experimentally verified and validated its use for 2-robot, 3-robot and 4-robot systems, with varying implementations ranging from automated trajectory control to human-in-the-loop piloting. In this publication, we show that the problem of entrapping/escorting/patrolling is trivial to define and manage from a cluster space perspective. Using a 3-robot experimental testbed, results are shown for the given tasks. We also revise the definition of the cluster space framework for a three-robot formation and incorporate a robotlevel obstacle avoidance functionality

Design and construction of a formation control testbed with wheeled and levitated robots.

Tse, Kim Fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 103-109).Abstracts in English and Chinese.Abstract --- p.iList of Figure --- p.iiiList of Table --- p.viChapter Chapter 1 : --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Background information --- p.2Chapter 1.2.1 --- Similar researches on testbed construction --- p.2Chapter 1.2.2 --- Formation control theories --- p.2Chapter 1.2.3 --- Robot control architectures --- p.3Chapter 1.3 --- Basic design of our testbed --- p.4Chapter 1.4 --- The organization of this thesis --- p.6Chapter Chapter 2 : --- Literature Survey --- p.7Chapter 2.1 --- Similar researches on testbed construction --- p.7Chapter 2.2 --- Sensors for Distance Detection --- p.10Chapter 2.2.1 --- IR Sensor --- p.10Chapter 2.2.1 --- Ultrasonic Sensor --- p.11Chapter 2.3 --- Formation control theories --- p.11Chapter 2.3.1 --- Behavior-based approach --- p.11Chapter 2.3.2 --- Leader-follower approach --- p.13Chapter 2.3.3 --- Virtual Structure approach --- p.13Chapter 2.4 --- Robot control architectures --- p.14Chapter 2.4.1 --- Centralized robot controlling system --- p.14Chapter 2.4.2 --- Decentralized robot controlling system --- p.15Chapter 2.5 --- Summary --- p.16Chapter Chapter 3 : --- Wheeled Robot Design --- p.18Chapter 3.1 --- Layer Concept in Robot Construction --- p.19Chapter 3.1.1 --- Processing layer --- p.20Chapter 3.1.2 --- Sensing layer --- p.22Chapter 3.1.3 --- Actuating layer (Wheeled Robot) --- p.24Chapter 3.2 --- Control Station Setup --- p.27Chapter 3.3 --- Sensor performance --- p.31Chapter 3.3.1 --- Distance Detection --- p.31Chapter 3.3.2 --- Direction Detection --- p.34Chapter 3.4 --- "Experiments, results and discussions" --- p.42Chapter 3.4.1 --- Experiment 1 - Experiment on MICA performance --- p.42Chapter 3.4.2 --- Experiment 2 - Distance maintaining --- p.43Chapter 3.4.3 --- Experiment 3 - Robot tracking --- p.45Chapter 3.5 --- Summary --- p.47Chapter Chapter 4 : --- Levitated Robot Design --- p.49Chapter 4.1 --- Possible methods to lift the robots --- p.49Chapter 4.2 --- Air table for robot lifting --- p.50Chapter 4.2.1 --- Table with air pump --- p.51Chapter 4.2.2 --- Table with air compressor --- p.54Chapter 4.2.3 --- Comparisons and experiments on the designs --- p.56Chapter 4.3 --- New actuating layer for the levitated robot --- p.56Chapter 4.3.1 --- Possible actuators for robot to move on air table --- p.57Chapter 4.3.2 --- Actuator selection --- p.62Chapter 4.4 --- "Experiments, results and discussions" --- p.65Chapter 4.4.1 --- Experiment 1 - Testing the performance of actuators --- p.66Chapter 4.4.2 --- Experiment 2 - Movement determination --- p.70Chapter 4.4.3 --- Experiment 3 - Maintaining position on air table --- p.74Chapter 4.5 --- Summary --- p.75Chapter Chapter 5 : --- Improvement of Position Detection --- p.77Chapter 5.1 --- Direction detection --- p.78Chapter 5.1.1 --- One reading approach --- p.79Chapter 5.1.2 --- Three readings approach --- p.79Chapter 5.1.3 --- Effective readings approach --- p.80Chapter 5.1.4 --- Imaginary sensor approach --- p.80Chapter 5.2 --- Distance Detection --- p.87Chapter 5.3 --- Experimental Results --- p.89Chapter 5.4 --- Summary --- p.92Chapter Chapter 6 : --- Conclusions and Future work --- p.93Appendix --- p.97Reference --- p.10

Cooperative Control of Multiple Wheeled Mobile Robots: Normal and Faulty Situations

Recently, cooperative control of multiple unmanned vehicles has attracted a great deal of attention from scientific, industrial, and military aspects. Groups of unmanned ground, aerial, or marine vehicles working cooperatively lead to many advantages in a variety of applications such as: surveillance, search and exploration, cooperative reconnaissance, environmental monitoring, and cooperative manipulation, respectively. During mission execution, unmanned systems should travel autonomously between different locations, maintain a pre-defined formation shape, avoid collisions of obstacles and also other team members, and accommodate occurred faults and mitigate their negative effect on mission execution. The main objectives of this dissertation are to design novel algorithms for single wheeled mobile robots (WMRs) trajectory tracking, cooperative control and obstacle avoidance of WMRs in fault-free situations. In addition, novel algorithms are developed for fault-tolerant cooperative control (FTCC) with integration of fault detection and diagnosis (FDD) scheme. In normal/fault-free cases, an integrated approach combining input-output feedback linearization and distributed model predictive control (MPC) techniques is designed and implemented on a team of WMRs to accomplish the trajectory tracking as well as the cooperative task. An obstacle avoidance algorithm based on mechanical impedance principle is proposed to avoid potential collisions of surrounding obstacles. Moreover, the proposed control algorithm is implemented to a team of WMRs for pairing with a team of unmanned aerial vehicles (UAVs) for forest monitoring and fire detection applications. When actuator faults occur in one of the robots, two cases are explicitly considered: i) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are re-assigned to the remaining healthy robots to complete the mission with graceful performance degradation. Two methods are used to investigate this case: the Graph Theory, and formulating the FTCC problem as an optimal assignment problem; and ii) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure the controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, an FDD unit using a two-stage Kalman filter (TSKF) to detect and diagnose actuator faults is presented. In case of using any other nonlinear controller in fault-free case rather than MPC, and in case of severe fault occurrence, another FTCC strategy is presented. First, the new reconfiguration is formulated by an optimal assignment problem where each healthy WMR is assigned to a unique place. Second, the new formation can be reconfigured, while the objective is to minimize the time to achieve the new formation within the constraints of the WMRs' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to address this problem. Since PSO cannot solve the continuous control inputs, CPTD is adopted to provide an approximate piecewise linearization of the control inputs. Therefore, PSO can be adopted to find the global optimum solution. In all cases, formation operation of the robot team is based on a leader-follower approach, whilst the control algorithm is implemented in a distributed manner. The results of the numerical simulations and real experiments demonstrate the effectiveness of the proposed algorithms in various scenarios

Formation control of mobile robots and unmanned aerial vehicles

In this dissertation, the nonlinear control of nonholonomic mobile robot formations and unmanned aerial vehicle (UAV) formations is undertaken and presented in six papers. In the first paper, an asymptotically stable combined kinematic/torque control law is developed for leader-follower based formation control of mobile robots using backstepping. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. Subsequently, in the second paper, a novel NN observer is designed to estimate the linear and angular velocities of both the follower and its leader robot and a NN output feedback control law is developed. On the other hand, in the third paper, a NN-based output feedback control law is presented for the control of an underactuated quad rotor UAV, and a NN virtual control input scheme is proposed which allows all six degrees of freedom to be controlled using only four control inputs. The results of this paper are extended to include the control of quadrotor UAV formations, and a novel three-dimensional leader-follower framework is proposed in the fourth paper. Next, in the fifth paper, the discrete-time nonlinear optimal control is undertaken using two online approximators (OLA\u27s) to solve the infinite horizon Hamilton-Jacobi-Bellman (HJB) equation forward-in-time to achieve nearly optimal regulation and tracking control. In contrast, paper six utilizes a single OLA to solve the infinite horizon HJB and Hamilton-Jacobi-Isaacs (HJI) equations forward-intime for the near optimal regulation and tracking control of continuous affine nonlinear systems. The effectiveness of the optimal tracking controllers proposed in the fifth and sixth papers are then demonstrated using nonholonomic mobile robot formation control --Abstract, page iv

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Formation and organisation in robot swarms.

A swarm is defined as a large and independent collection of heterogeneous or homogeneous agents operating in a common environment and seemingly acting in a coherent and coordinated manner. Swarm architectures promote decentralisation and self-organisation which often leads to emergent behaviour. The emergent behaviour of the swarm results from the interactions of the swarm with its environment (or fellow agents), but not as a direct result of design. The creation of artificially simulated swarms or practical robot swarms has become an interesting topic of research in the last decade. Even though many studies have been undertaken using a practical approach to swarm construction, there are still many problems need to be addressed. Such problems include the problem of how to control very simple agents to form patterns; the problem of how an attractor will affect flocking behaviour; and the problem of bridging formation of multiple agents in connecting multiple locations. The central goal of this thesis is to develop early novel theories and algorithms to support swarm robots in. pattern formation tasks. To achieve this, appropriate tools for understanding how to model, design and control individual units have to be developed. This thesis consists of three independent pieces of research work that address the problem of pattern formation of robot swarms in both a centralised and a decentralised way.The first research contribution proposes algorithms of line formation and cluster formation in a decentralised way for relatively simple homogenous agents with very little memory, limited sensing capabilities and processing power. This research utilises the Finite State Machine approach.In the second research contribution, by extending Wilensky's (1999) work on flocking, three different movement models are modelled by changing the maximum viewing angle each agent possesses during the course of changing its direction. An object which releases an artificial potential field is then introduced in the centre of the arena and the behaviours of the collective movement model are studied.The third research contribution studies the complex formation of agents in a task that requires a formation of agents between two locations. This novel research proposes the use Of L-Systems that are evolved using genetic algorithms so that more complex pattern formations can be represented and achieved. Agents will need to have the ability to interpret short strings of rules that form the basic DNA of the formation