Practical formation control of multiple underactuated ships with limited sensing ranges

This paper presents a constructive method to design cooperative controllers that force a group of N underactuated ships with limited sensing ranges to perform a desired formation, and guarantee no collisions between the ships. These ships do not have an independent actuator in the sway axis. The desired formation is stabilized at any sufficiently smooth reference trajectories, including fixed points and nonadmissible trajectories for the ships. The formation control design is based on several nonlinear coordinate changes, the transverse function approach, the backstepping technique, the Lyapunov direct method, and smooth and p-times differentiable step functions. These functions are introduced and incorporated into novel potential functions to solve the collision avoidance problem without the need of switchings despite the ships’ limited sensing ranges. Simulations illustrate the results.     

Finite-time consensus and collision avoidance control algorithms for multiple AUVs

In this paper, finite-time position consensus and collision avoidance problems are investigated for multi-AUV (autonomous underwater vehicle) systems. First, based on the homogeneous control method, finite-time position consensus algorithms are proposed for both leaderless and leader–follower multi-AUV systems without considering collisions between the AUVs. Specifically, in the leader–follower case, a novel distributed finite-time observer is developed for the followers to estimate the leader’s velocity. Second, by constructing collision avoidance and connectivity maintenance functions, modified consensus algorithms containing corresponding gradient terms are presented for multi-AUV systems of both cases, which guarantee collision avoidance, connectivity maintenance, velocity matching, and consensus boundedness. Simulations demonstrate the effectiveness of the proposed control algorithms.     

A bounded distributed connectivity preserving aggregation strategy with collision avoidance property

This paper presents a bounded connectivity preserving control strategy for the aggregation of multi-agent systems. The problem is investigated for two cases of single-integrator agents and unicycles. Under the proposed control strategy, if two agents are in the connectivity range at some point in time, they will stay connected thereafter. The agents finally aggregate while avoiding collision in such a way that the average of the distances between every pair of neighboring agents is bounded by a pre-specified positive real number, which can be chosen arbitrarily small. The results are developed based on some important characteristics of the positive limit set of the closed-loop system under the proposed control strategy and a fundamental property of convex real functions. The theoretical results are verified by simulation.     

Point-to-point and collision avoidance control of the motion of an autonomous bicycle

This work deals with the stabilization and collision avoidance control of a riderless bicycle (see Figure 1). It is assumed here that the bicycle is controlled by a pedalling torque, a directional torque, and by a rotor mounted on the crossbar that generates a tilting torque.Given two points r_A, r_B, and a circular obstacle in the horizontal plane. Also, given a time interval [0, t_f], where 0 < t_f < ∞. It is shown here that by applying a kind of inverse dynamics control, the motion of the bicycle is stabilized while simultaneously controlling its speed and direction in such a manner that the point of contact between the bicycle's rear wheel and the horizontal plane will be able, during [0, t_f], to move from r_A to r_B without hitting the obstacle.     

A framework and automotive application of collision avoidance decision making

Collision avoidance (CA) systems are applicable for most transportation systems ranging from autonomous robots and vehicles to aircraft, cars and ships. A probabilistic framework is presented for designing and analyzing existing CA algorithms proposed in literature, enabling on-line computation of the risk for faulty intervention and consequence of different actions. The approach is based on Monte Carlo techniques, where sampling-resampling methods are used to convert sensor readings with stochastic errors to a Bayesian risk. The concepts are evaluated using a real-time implementation of an automotive collision mitigation system, and results from one demonstrator vehicle are presented.     

Effectiveness and driver acceptance of a semi-autonomous forward obstacle collision avoidance system

This paper proposes a semi-autonomous collision avoidance system for the prevention of collisions between vehicles and pedestrians and objects on a road. The system is designed to be compatible with the human-centered automation principle, i.e., the decision to perform a maneuver to avoid a collision is made by the driver. However, the system is partly autonomous in that it turns the steering wheel independently when the driver only applies the brake, indicating his or her intent to avoid the obstacle. With a medium-fidelity driving simulator, we conducted an experiment to investigate the effectiveness of this system for improving safety in emergency situations, as well as its acceptance by drivers. The results indicate that the system effectively improves safety in emergency situations, and the semi-autonomous characteristic of the system was found to be acceptable to drivers.     

Spacecraft formation reconfiguration with collision avoidance

In this paper we present a behavioral control solution for reconfiguration of a spacecraft formation using the Null-Space Based (NSB) concept. The solution is task based, and aims to reconfigure and maintain a rigid formation while avoiding collisions between spacecraft. A model of relative translation is derived, together with a passivity-based sliding surface controller which globally stabilizes the equilibrium point of the closed-loop system. The NSB control method is implemented by giving each task different priorities and then calculating desired velocity and a Jacobian matrix for each spacecraft and each task. The velocity vector for each task is then projected into the null-space for higher prioritized tasks to remove conflicting velocity components. Simulation results are presented, showing that each spacecraft moves into the predefined formation without breaking any rules for the higher priority tasks, and all collisions are avoided.     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

A feedback stabilization and collision avoidance scheme for multiple independent non-point agents

A navigation functions’ based methodology, established in our previous work for centralized multiple robot navigation, is extended to address the problem of decentralized navigation. In contrast to the centralized case, each agent plans its actions without knowing the destinations of the other agents. Asymptotic stability is guaranteed by the existence of a global Lyapunov function for the whole system, which is actually the sum of the separate navigation functions. The collision avoidance and global convergence properties are verified through simulations.     

Robust sliding-mode formation control and collision avoidance via repulsive vector fields for a group of Quad-Rotors

This paper is focused on solving the collision avoidance problem for a group of Quad-Rotors which are affected by external disturbances when they are moving in a horizontal plane by means of Repulsive Vector Fields (RVFs). The RVFs are included in an attractive potential function control strategy, that allows the Quad-Rotors to reach the desired position in a geometric pattern, together with a Continuous Sliding-Mode Control (C-SMC) strategy based on Sliding-Mode Observers (SMOs) that are used to estimate, in a finite-time, the linear and angular velocities, respectively. For this purpose, a parameter, which depends on the distance among the Quad-Rotors and their velocities, is designed in order to scale the RVFs properly. In this sense, the repulsion force will be proportional to the velocity and acceleration of the Quad-Rotor when it detects any obstacle. A RVF not properly scaled may result in a collision or a Quad-Rotor movement far away from its desired position. The combination between the C-SMC strategy, the SMOs, the attractive potential functions and the RVFs robustly solves the formation control and avoidance collision problem under the presence of disturbances. Numerical simulations illustrate the performance of the RVFs when the Quad-Rotors are in risk of collision.     

Fusion of laser and visual data for robot motion planning and collision avoidance

In this paper, a method for inferring scene structure information based on both laser and visual data is proposed. Common laser scanners employed in contemporary robotic systems provide accurate range measurements, but only in 2D slices of the environment. On the other hand, vision is capable of providing dense 3D information of the environment. The proposed fusion scheme combines the accuracy of laser sensors with the broad visual fields of cameras toward extracting accurate scene structure information. Data fusion is achieved by validating 3D structure assumptions formed according to 2D range scans of the environment, through the exploitation of visual information. The proposed methodology is applied to robot motion planning and collision avoidance tasks by using a suitably modified version of the vector field histogram algorithm. Experimental results confirm the effectiveness of the proposed methodology.Received: 10 May 2002, Accepted: 18 December 2002, Published online: 7 October 2003     

碰撞防护系统分析及铁路应用设计

在分析既有碰撞防护系统的基础上,首次提出适用于轨道交通领域的列车碰撞防护系统概念,设计了列车碰撞系统的功能架构并定义了各子系统的功能,对比分析了列车碰撞防护系统的两种工作模式.作为列车控制系统的安全叠加系统,列车碰撞防护系统可有效提升铁路运输安全性能.     

Multi-agent consensus algorithm with obstacle avoidance via optimal control approach

Multi-agent consensus problem in an obstacle-laden environment is addressed in this study. A novel optimal control approach is proposed for the multi-agent system to reach consensus as well as avoid obstacles with a reasonable control effort. An innovative nonquadratic penalty function is constructed to achieve obstacle avoidance capability from an inverse optimal control perspective. The asymptotic stability and optimality of the consensus algorithm are proven. In addition, the optimal control law only requires local information from the communication topology to guarantee the proposed behaviour, rather than all agents’ information. The consensus and obstacle avoidance are validated through various simulations.     

Cooperative RISE learning-based circumnavigation of networked unmanned aerial vehicles with collision avoidance and connectivity preservation

In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial vehicles (UAVs). A leader–follower structure is adopted, wherein the leader moves with reference dynamics (a target). Different from the existing approaches that necessitate full knowledge of the time-varying reference trajectory, in this paper, it is assumed that only some vehicles (at least one) have access to the bearing relative to the target, and all other vehicles are equipped with sensors capable of measuring the bearings relative to neighboring vehicles. In this paper, a consensus estimator is proposed to estimate the global position for each vehicle using relative bearing measurements and an estimate of neighboring vehicles received from a direct communication network. Then, a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the distributed vector field approach to ensure UAV formation orbiting around the moving target while avoiding obstacles and maintaining network links within available communication ranges. In contrast to the classical RISE control rule, a tanh() function is used instead of the sgn() function to further decrease the high-gain feedback and to obtain a smoother control signal. Furthermore, by using the localized radial basis function (RBF) neural networks (NNs) in a cooperative way, deterministic learning theory is employed to accurately identify/learn model uncertainties resulting from the attitude dynamics. The convergence of the entire closed-loop system is illustrated using the Lyapunov theory and is shown to be uniformly ultimately bounded. Finally, numerical simulations show the effectiveness of the proposed approach.     

一种新的智能避障转向控制方法

针对智能探测车的转向系统建立精确的数学模型十分困难,且其航向受诸多因素干扰问题,研究了预测控制中的动态矩阵法,提出了一种新的智能探测车转向机构模型。该方法用障碍物的方向和距离偏差作为参数,在预测控制算法中采用动态矩阵控制,输出前轮转向角。再将转向角预测量与实际转向角之和作为反馈,对转向角变化趋势做出预测。通过仿真验证了在不同纵向速度下,加入相同干扰时的系统响应。结果表明：该算法在智能探测车障碍物避让控制中,对外界环境的干扰具有较强的鲁棒性,能够满足智能探测车转向控制要求。     

Coordinated formation control of multiple nonlinear systems

A general method of controller design is developed for the purpose of formation keeping and reconfiguration of nonlinear systems with multiple subsystems, such as the formation of multiple aircraft, ground vehicles, or robot arms. The model consists of multiple nonlinear systems. Controllers are designed to keep the subsystems in a required formation and to coordinate the subsystems in the presence of environmental changes. A step-by-step algorithm of controller design is developed. Sufficient conditions for the stability of formation tracking are proved. Simulations and experiments are conducted to demonstrate some useful coordination strategies such as movement with a leader, simultaneous movement, series connection of formatiom, and human-machine interaction.     

基于FPGA和ARM的船舶防撞系统设计

研究设计了一种基于FPGA和ARM的船舶防碰撞告警设备。该设备主要应用在中小型船只上,与船上原有经济型航海雷达配合使用构成一个新系统。防碰撞告警设备从原航海雷达上采集雷达原始数据,并将数据进行变换和二次信息处理,使系统具有自动目标跟踪、危险目标自动识别以及各种危险目标告警的功能。在不影响原航海雷达功能的前提下,为其增加了防撞避碰的功能。