多移动机器人的领航-跟随编队避障控制

针对多移动机器人的编队控制问题,提出了一种结合Polar Histogram避障法的领航-跟随协调编队控制算法。该算法在领航-跟随l-φ编队控制结构的基础上引入虚拟跟随机器人,将编队控制转化为跟随机器人对虚拟跟随机器人的轨迹跟踪控制。结合移动机器人自身传感器技术,在简单甚至复杂的环境下为机器人提供相应的路径运动策略,实现实时导航的目的。以两轮差动Qbot移动机器人为研究对象,搭建半实物仿真平台,进行仿真实验。仿真结果表明:该方法可以有效地实现多移动机器人协调编队和避障控制。     

考虑时变时滞的多移动智能体分布式编队控制

考虑到多移动智能体编队控制中的时变时滞问题,在所设计的编队框架下,基于一致性算法设计了具有不对称时变时滞的分布式编队控制律.该控制律仅使用全局速度导引信息和邻居状态反馈信息;在固定通信拓扑条件下,推导了具有时变时滞的闭环系统状态方程,应用改进的自由权矩阵方法获得了保守性更小的系统稳定条件,并在时变通信拓扑条件下,将拓扑变化处理为系统结构的不确定性,同样获得了时变通信拓扑下的系统稳定条件;进行了6个智能体在平面内编队运动的仿真,实例证明,理论结果是正确的.     

Potential-based obstacle avoidance in formation control

Based on the double integrator mathematic model, a new kind of potential function is presented in this paper by referring to the concepts of the electric field; then a new formation control method is proposed, in which the potential functions are used between agent-agent and between agent-obstacle, while state feedback control is applied for the agent and its goal. This strategy makes the whole potential field simpler and helps avoid some local minima. The stability of this combination of potential functions and state feedback control is proven. Some simulations are presented to show the rationality of this control method.     

Fuzzy behavior-based control of mobile robots

An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment. The behavior-based architecture decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions. Fuzzy logic is used to implement individual behaviors, to coordinate the various behaviors, to select roles for each robot and, for robot perception, decision-making, and speed control. The architecture is implemented on a team of three soccer robots performing different roles interchangeably. The robot behaviors and roles are designed to be complementary to each other, so that a coherent team of robots exhibiting good collective behavior is obtained.     

Decentralized and adaptive control of multiple nonholonomic robots for sensing coverage

This work deals with decentralized control of multiple nonholonomic mobile sensors for optimal coverage of a given area for sensing purposes. We assume a density function over the region to be covered, which can be viewed as a probability density of the phenomena to be sensed. The density function is unknown but assumed to be linearly parameterized with unknown parameter weights. We consider a second‐order dynamic model for the mobile agents and derive decentralized adaptive control laws to achieve optimal coverage of the region. We then consider the case where the dynamic model of the agents are not fully known, and then develop parameter adaptation laws to achieve the optimal coverage objective. We test the derived algorithms using simulations and compare our proposed controllers with kinematics‐based controllers. We find that the feedback control design based on the dynamic model performs significantly better than controllers solely relying on kinematic models. Furthermore, for the unknown dynamics case, our controller outperforms the nonadaptive controller with poor initial parameter estimates.     

Formation control of mobile robots with obstacle avoidance based on GOACM using onboard sensors

This paper deals with the problem of formation control for nonholonomic mobile robots under a cluttered environment. When the obstacles are not detected, the follower robot calculates its waypoint to track, based on the leader robot’s state. The proposed geometric obstacle avoidance control method (GOACM) guarantees that the robot avoids the static and dynamic obstacles using onboard sensors. Due to the difficulty for the robot to simultaneously get overall safe boundary of an obstacle in practice, a safe line, which is perpendicular to the obstacle surface, is used instead of the safe boundary. Since GOACM is executed to find a safe waypoint for the robot, GOACM can effectively cooperate with the formation control method. Moreover, the adaptive controllers guarantee that the trajectory and velocity tracking errors converge to zero with the consideration of the parametric uncertainties of both kinematic and dynamic models. Simulation and experiment results present that the robots effectively form and maintain formation avoiding the obstacles.     

Redundancy resolution and obstacle avoidance for cooperative industrial robots

Planning methods for effective manipulation of single or multiple redundant arm systems must take account of DOF, the task, constraints, and joint drifts. Here, a new approach to redundancy resolution and obstacle avoidance for cooperative robot arms is proposed. In this development, a relative Jacobian and a relative dexterity measure for cooperative robot arms are derived. A nonlinear programming method is used to optimize the relative dexterity while satisfying cooperative task requirements, limits on joint angles, and obstacle avoidance. With this approach, it is not necessary to balance the weightings between the cost term to be optimized and the penalty from constraints. Configuration jumps over obstacles are avoided. Further, since globally optimal joint configurations are produced, drifts in joint configurations will be absent from the resulting configurations. This article includes several illustrative examples to demonstrate the effectiveness and usefulness of this approach. Results have indicated the benefits of both the relative dexterity and the sum of individual arm dexterities in planning of cooperative tasks. ©1999 John Wiley & Sons, Inc.     

自主移动机器人避障技术研究现状

对避障技术的核心内容暨避障算法展开归纳介绍,依照传统算法和智能算法分类,详细介绍了应用较多的各避障算法的基本原理、优缺点及部分改进方案;对自主移动机器人避障技术的应用前景和发展趋势作出了展望.     

A real-time planning algorithm for obstacle avoidance of redundant robots

A computationally efficient, obstacle avoidance algorithm for redundant robots is presented in this paper. This algorithm incorporates the neural networks and pseudodistance function D _p in the framework of resolved motion rate control. Thus, it is well suited for real-time implementation. Robot arm kinematic control is carried out by the Hopfield network. The connection weights of the network can be determined from the current value of Jacobian matrix at each sampling time, and joint velocity commands can be generated from the outputs of the network. The obstacle avoidance task is achieved by formulating the performance criterion as D _p>d _min (d _min represents the minimal distance between the redundant robot and obstacles). Its calculation is only related to some vertices which are used to model the robot and obstacles, and the computational times are nearly linear in the total number of vertices. Several simulation cases for a four-link planar manipulator are given to prove that the proposed collision-free trajectory planning scheme is efficient and practical.     

基于群集行为的分布式多无人机编队动态避障控制

针对无人机编队保持和动态障碍物规避控制问题,本文提出了一种新的基于群集行为的分布式多无人机编队控制和避障控制算法.首先考虑了由机间气流等因素带来的干扰,基于吸引/排斥势场和一致性方法,设计了分布式无人机编队的队形保持控制算法,对编队内无人机之间的距离进行控制.进一步考虑外部移动障碍对无人机编队的影响,引入了排斥势场产生...     

双轮移动机器人安全目标追踪与自动避障算法

设计了双轮移动机器人安全目标追踪算法和双回路的追踪与避障控制方案.内层控制回路是目标追踪的控制律,用来指导机器人追踪到指定目标并保持一定的安全距离,而且兼顾了机器人在运行速度上的限制和追踪的时间效率,其控制的渐近稳定性用Lyapunov函数法进行了证明.当遇到障碍物时,外层控制回路根据超声传感器的信息和阻抗控制的概念产生阻抗虚拟力,将期望目标调整到虚拟位置,使机器人能够自动转向以避开障碍物.仿真研究和实验结果证明了追踪算法的有效性和避障方法的可行性.     

Neural networks based reinforcement learning for mobile robots obstacle avoidance

This study proposes a new approach for solving the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The purpose of this research is to provide mobile robots a collision-free trajectory within an uncertain workspace which contains both stationary and moving entities. The developed solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm presented proves to be effective in navigation scenarios where global information is available. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. The solution is deployed in both Virtual Reality (VR) for easier visualization and safer testing activities, and on a real mobile robot for experimental validation. The algorithm is compared with Powerbot's ARNL proprietary navigation algorithm. Results show that the proposed solution has a good conversion rate computed at a satisfying speed.     

Region filling operations with random obstacle avoidance for mobile robots

The article presents a new topic in path planning for mobile robots, region filling. which involves a sweeping operation to fill a whole region with random obstacle avoidance. The approaches for global strip filling and local path searching driven by sensory data procedures are developed. A computer graphic simulation is used to verify the filling strategy available. The research was developed from the program for the design of a robot lawn mower. However, the solution appears generic. The significance is that a problem of wide application and generic solutions for general autonomous mobile robots have been developed.     

Moving obstacle avoidance for cable-driven parallel robots using improved RRT

Microsystem Technologies - Moving obstacle avoidance is one of the most challenging problems for cable-driven parallel robots (CDPRs) due to various constraints. In this work, the improved rapidly...     

Polygonal obstacle avoidance method for swarm robots via fluid dynamics

Artificial Life and Robotics - Swarm robots in obstacle environments perform search tasks efficiently while avoiding obstacles. Obstacle avoidance methods are classified by the way obstacles are...     

具有速度、加速度约束的机器人编队避障控制

针对轮式移动机器人的编队避障问题,提出了一种改进的控制算法,能够有效实现避免碰撞和规避障碍.首先,建立多机器人不同控制增益的全员"航标"引导的非线性循环追踪控制编队,当编队过程某成员监测执行功能失效(但仍能运行)时,可对非失效机器人进行系统降级重组,并继续执行任务且避免机器人之间发生碰撞;再通过引入速度和加速度约束,以满足轮式移动机器人控制接口及保护电机的需要,从而保证控制算法的稳定与收敛;最后,通过引入惩罚因子对该控制算法进行改进,使编队成功规避障碍物并进行最短路径规划.结果表明,改进的控制算法增加了多机器人编队的鲁棒性、提高了抗干扰能力及编队恢复执行任务的可靠性,更有效地实现了编队避障控制.     

基于生存理论的非完整约束轮式机器人高速避障控制

轮式移动机器人现有的避障控制方法大多需要在避障过程中进行减速处理, 会影响移动效率. 鉴于此, 将生存理论应用于轮式移动机器人的反应式避障控制. 分析非完整约束轮式机器人的仿射非线性系统模型和约束条件, 利用弹性边界升维和控制模型退化的方法给出系统的生存性设计, 并利用最优化方法得出机器人高速避障控制器. 最后通过仿真实验, 表明了轮式机器人高速避障控制的有效性.

     

智能轮椅避障测距控制系统

介绍了一种智能轮椅避障测距系统,给出了多路测距系统的结构和超声波传感器的分布,设计了避障测距与控制程序,并进行了自主避障与相关实验.实验结果表明:系统测量误差小,可以应用于智能轮椅避障系统中.     

Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots

This paper presents an optimisation-based verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated: genetic algorithms (GAs) and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.