A Motion Planning Framework with Connectivity Management for Multiple Cooperative Robots

This paper proposes a decentralized motion planning algorithm for multiple cooperative robots subject to constraints imposed by sensors and the communication network. It consists of decentralized receding horizon planners that reside on each vehicle to navigate to individual target positions. A routing algorithm which modify the network topology based on the position of the robots and the limited range of transmitters and receivers, enables to reduce the communication link failures. A comparative study between the proposed algorithm and other existing algorithms is provided in order to show the advantages especially in terms of traveling time and communication link failure.     

Decentralized estimation and control of graph connectivity for mobile sensor networks

The ability of a robot team to reconfigure itself is useful in many applications: for metamorphic robots to change shape, for swarm motion towards a goal, for biological systems to avoid predators, or for mobile buoys to clean up oil spills. In many situations, auxiliary constraints, such as connectivity between team members or limits on the maximum hop-count, must be satisfied during reconfiguration. In this paper, we show that both the estimation and control of the graph connectivity can be accomplished in a decentralized manner. We describe a decentralized estimation procedure that allows each agent to track the algebraic connectivity of a time-varying graph. Based on this estimator, we further propose a decentralized gradient controller for each agent to maintain global connectivity during motion.     

Coordinated transportation of a single object by two nonholonomic mobile robots

We propose a decentralized control algorithm for transporting a single object by two nonholonomic mobile robots. One of the robots acts as a leader, whose trajectory is planned by itself or defined previously, whereas the other robot, referred to as a follower, follows the leader by keeping a constant distance from the leader. The follower can also avoid obstacles while following the leader without any absolute information about their position. Furthermore, the two mobile robots can realize an omnidirectional motion of the object when the leader broadcasts some simple information to the follower. Some simulation results show a good performance by the proposed decentralized control algorithm. This work was presented, in part, at the Seventh International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002.     

一种编队控制的动态调整与规划方法

This article is concerned with cooperative control problems in formation of mobile robots under the nonholonomic constraints that certain geometrical constraints are imposed on multiple mobile robots throughout their travel. For this purpose, a new method of motion control for formation is presented, which is based on the dynamic regulation and scheduling scheme. It is attractive for its adaptability to the formation structure and desired trajectory. The quality of formation keeping can be evaluated by the instantaneous errors of formation offset and spacing distance. Some kinematics laws are developed to regulate and maintain the formation shape. Simulation results and data analysis show the validity of the proposed approach for a group of robots.     

基于QPSO算法的机器人时间最优轨迹规划

在考虑关节约束的前提下,为得到工业机器人时间最优的关节运动轨迹,提出一种工业机器人时间最优轨迹规划新算法。采用五次非均匀B样条插值法构造各关节运动轨迹,得到的机器人各关节位置准确,各关节速度、加速度和加加速度曲线连续。利用量子行为粒子群优化算法(Quantum-behaved Particle Swarm Optimization,简称QPSO)进行时间最优的轨迹规划,该算法可以在整个可行域上搜索,具有较强的全局搜索能力。与标准粒子群算法(Particle Swarm Optimization,简称PSO)和差分进化算法(Differential Evolution Algorithm,简称DE)相比较,结果显示使用该算法进行时间最优的轨迹规划得到的数值结果更小。     

一种多非完整移动机器人分布式编队控制方法

本文研究了多非完整移动机器人编队控制算法。在该算法中,参考轨迹被视为虚拟领导者,只有部分机器人可以接收到领导者信息,机器人之间只能进行局部信息交互。利用坐标变换将机器人系统的编队问题转化为变换后系统的一致性问题,在持续激励的条件下,设计了一种分布式控制算法,通过图论与Lyapunov 理论证明了该分布式控制算法可以使移动机器人队伍指数收敛于期望队形,并使队形的几何中心指数收敛到参考轨迹。最后,数值仿真验证了该控制算法的有效性。     

Obituary

A model-based decentralized adaptive controller is proposed for multiple manipulators in a class of co-operations called holonomic co-operations, in which the manipulators are holonomically constrained. In this controller we calculate the control input and estimate unknown robotic parameters in individual state spaces of the manipulators instead of that of the whole system. Consequently, no coordinator exists in the system and the control architecture is decentralized. The model-based adaptive algorithm is used to estimate the unknown or uncertain parameters. It is proven that a Lyapunov function guarantees asymptotic convergence of tracking errors of both the trajectory and interactive force among the manipulators. We also discuss issues regarding communication among the robots according to motion constraints associated with the co-operation. Finally, the validity and performance of the proposed method are verified by simulations on two six-DOF manipulators.     

Decentralized control of a group of mobile robots for deployment in sweep coverage

This paper addresses a problem of sweep coverage by deploying a network of autonomous mobile robots. We propose a decentralized control algorithm for the robots to accomplish the sweep coverage. The sweep coverage is achieved by coordinating the robots to move along a given path that is unknown to the vehicles a priori. The motion coordination algorithm is developed based on simple consensus algorithms. The effectiveness of the algorithm is demonstrated via numerical simulations. The proposed algorithm would have applications to military and civilian operations.     

Robust formation control without velocity measurement of the leader robot

In this paper a control problem of leader–follower motion coordination of multiple nonholonomic mobile robots is addressed and subsequently in the proposed scheme, a reference trajectory generated based on the information from the leader is tracked by the follower robots. To alleviate demanded information on the leader, specifically to eliminate the measurement requirement or estimation of the leader's velocity and dynamics, a virtual vehicle is constructed whereby its trajectory converges to the reference trajectory of the follower. Trajectory tracking controller is then designed to allow the follower robot to track the virtual vehicle using neural network approximation, in combination with the backstepping and Lyapunov direct design technique and finally the performance and effectiveness of the controller is verified throughout the experiments.     

Convex Optimization Strategies for Coordinating Large-Scale Robot Formations

This paper investigates convex optimization strategies for coordinating a large-scale team of fully actuated mobile robots. Our primary motivation is both algorithm scalability as well as real-time performance. To accomplish this, we employ a formal definition from shape analysis for formation representation and repose the motion planning problem to one of changing (or maintaining) the shape of the formation. We then show that optimal solutions, minimizing either the total distance or minimax distance the nodes must travel, can be achieved through second-order cone programming techniques. We further prove a theoretical complexity for the shape problem of O(m^1.5) as well as O(m) complexity in practice, where m denotes the number of robots in the shape configuration. Solutions for large-scale teams (1000's of robots) can be calculated in real time on a standard desktop PC. Extensions integrating both workspace and vehicle motion constraints are also presented with similar complexity bounds. We expect these results can be generalized for additional motion planning tasks, and will prove useful for improving the performance and extending the mission lives of large-scale robot formations as well as mobile ad hoc networks.     

Controlling Ensembles of Robots via a Supervisory Aerial Robot

We consider the task of controlling a large team of non-holonomic ground robots with an unmanned aerial vehicle in a decentralized manner that is invariant to the number of ground robots. The central idea is the development of an abstraction for the team of ground robots that allows the aerial platform to control the team without any knowledge of the specificity of individual vehicles. This happens in much the same way as a human operator can control a single robot vehicle by simply commanding the forward and turning velocities without a detailed knowledge of the specifics of the robot. The abstraction includes a gross model of the shape of the formation of the team, and information about the position and orientation of the team in the plane. We derive controllers that allow the team of robots to move in formation while avoiding collisions and respecting the abstraction commanded by the aerial platform. We propose strategies for controlling the physical spread of the ensemble of robots by splitting and merging the team based on distributed techniques. We provide simulation and experimental results using a team of indoor mobile robots and a three-dimensional, cable-controlled, parallel robot which serves as our indoor unmanned aerial platform.     

Optimum motion planning in joint space for robots using genetic algorithms

The optimum motion planning in joint space (OMPJS) for robots, which generally consists of two subproblems, optimum path planning and optimum trajectory planning, was considered as a whole in the paper. A new method for optimum motion planning problem based on an improved genetic algorithm is proposed, which is more general, flexible and effective. This approach incorporates kinematics constraints, dynamics constraints, and control constraints of robotic manipulator. The simulation results for a two and a three degrees of freedom robots are presented and discussed. The simulations are based on genetic algorithm class library WGAClass 1.0 developed by us with Borland C++ 3.1.     

基于人工协调场的多移动机器人实时协调避碰规划

为克服传统人工势场在动态未知环境下机器人避碰规划中存在的缺陷,提出人工协调场法(ACF).将场函数与机器人的风险状态相结合,给出并讨论了人工协调场的基本设计.基于人工协调场,考虑机器人的运动约束,实现了多机器人之间以及机器人与环境间的实时协调避碰,提出了一个多移动机器人无死锁实时避碰规划算法.理论分析和仿真试验证明所提方法的有效性.     

A control strategy for platoons of differential drive wheeled mobile robot

The strategy for the control of vehicle platooning is proposed and tested on different mobile robot platforms. The decentralized platooning is considered, i.e. a virtual train of vehicles where each vehicle is autonomous and decides on its motion based on its own perceptions. The following vehicle only has information about its distance and azimuth to the leading vehicle. Its position is determined using odometry. The reference position and the orientation of the following vehicle are determined by the estimated path of the leading vehicle in a parametric polynomial form. The parameters of the polynomials are determined using the least-squares method. This parametric reference path is also used to determine the feed-forward part and to suppress tracking errors by a feed-back part of the applied globally stable nonlinear control law. The results of the experiment and simulations demonstrate the applicability of the proposed algorithm for vehicle platoons.     

Online decentralized information gathering with spatial–temporal constraints

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial–temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents’ trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial–temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources.     

Robust control of mobile manipulators

A mobile manipulator is basically a manipulator mounted on a mobile vehicle. This arrangement has some advantages over stationary robots and mobile robots, such as the infinite workspace and the ability to avoid singularities. However, the control problem becomes a sophisticated one. This is due to the nonlinear and nonholonomic constraints governing the motion of the vehicle. Moreover, the dynamics of the manipulator and the vehicle are highly coupled; ground-surface irregularities, for example, affect the motion of the end effector kinematically and dynamically. A mobile manipulator is expected to pass through different environmental conditions, a fact which calls for a robust control scheme. Unfortunately, the robust control problem for nonholonomic systems is not well defined yet. Since the ultimate goal of control is to control the motion of the manipulator's end effector, it is proposed in this article to tackle the robustness issue by designing a manipulator decoupling controller. This controller aims at rejecting disturbances arising from the motion of the vehicle. © 1996 John Wiley & Sons, Inc.     

Trajectory design and control for aggressive formation flight with quadrotors

In this work we consider the problem of controlling a team of micro-aerial vehicles moving quickly through a three-dimensional environment while maintaining a tight formation. The formation is specified by shape vectors which prescribe the relative separations and bearings between the robots. To maintain the desired shape, each robot plans its trajectory independently based on its local information of other robot plans and estimates of states of other robots in the team. We explore the interaction between nonlinear decentralized controllers, the fourth-order dynamics of the individual robots, time delays in the network, and the effects of communication failures on system performance. Simulations as well as an experimental evaluation of our approach on a team of quadrotors suggests that suitable performance is maintained as the formation motions become increasingly aggressive and as communication degrades.     

Collision-Free Cartesian Trajectory Generation Using Raster Scanning and Genetic Algorithms

An algorithm for Cartesian trajectory generation by redundant robots in environments with obstacles is presented. The algorithm combines a raster scanning technique, genetic algorithms and functions for interpolation in the joint coordinates space in order to approximate a desired Cartesian curve by the robot's hand tip under maximum allowed position deviation. A raster scanning technique determines a minimal set of knot points on the desired curve in order to generate a Cartesian trajectory with bounded position approximation error. Genetic algorithms are used to determine an acceptable robot configuration under obstacle avoidance constraints corresponding to a knot point. Robot motion between two successive knot points is finally achieved using well known interpolation techniques in the joint coordinates space. The proposed algorithm is analyzed and its performance is demonstrated through simulated experiments carried out on planar redundant robots.     

A decentralized approach for the conflict-free motion of multiple mobile robots

This article presents a novel approach to decentralized motion planning and conflict-resolution for multiple mobile robots. The proposed multi-robot motion planning is an on-line operation, based on cost wave propagation within a discretized configuration space-time. By use of the planning method, a framework for negotiation is developed, which permits quick decentralized and parallel decision making. The key objective of the negotiation procedure is dynamic assignment of robot motion priorities. Thus, robots involved in a local conflict situation cooperate in planning and execution of the lowest cost motion paths without application of any centralized components. The features required for individual and cooperative motion are embedded in a hybrid control architecture. Results obtained from realistic simulation of a multi-robot environment and also from experiments performed with two mobile robots demonstrate the flexibility and the efficiency of the proposed method.     

INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.