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.     

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.     

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.     

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.     

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...     

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.     

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.     

Decentralized control of cooperative robots without velocity-force measurements

One of the main practical problems on cooperative robots is the complexity of integrating a large amount of expensive velocity-force sensors. In this paper, the control of cooperative robots using only joint measurements is considered to manipulate an object firmly. Experimental results are shown to support the developed theory.     

Extending obstacle avoidance methods through multiple parameter-space transformations

Obstacle avoidance methods approach the problem of mobile robot autonomous navigation by steering the robot in real-time according to the most recent sensor readings, being suitable to dynamic or unknown environments. However, real-time performance is commonly gained by ignoring the robot shape and some or all of its kinematic restrictions which may lead to poor navigation performance in many practical situations. In this paper we propose a framework where a kinematically constrained and any-shape robot is transformed in real-time into a free-flying point in a new space where well-known obstacle avoidance methods are applicable. Our contribution with this framework is twofold: the definition of generalized space transformations that cover most of the existing transformational approaches, and a reactive navigation system where multiple transformations can be applied concurrently in order to optimize robot motion decisions. As a result, these transformations allow existing obstacle avoidance methods to perform better detection of the surrounding free-space, through “sampling” the space with paths compatible with the robot kinematics. We illustrate how to design these space transformations with some examples from our experience with real robots navigating in indoor, cluttered, and dynamic scenarios. Also, we provide experimental results that demonstrate the advantages of our approach over previous methods when facing similar situations.

Fuzzy truck control scheme for obstacle avoidance

The fuzzy system can be a good solution when a mathematical model of the system is either unavailable or too complex. Truck backer-upper control problem is one example of a standard highly nonlinear control problem. Bearing this in mind the control scheme that considers obstacles near the truck is much more complex than other conventional approaches. In this paper a fuzzy truck control system for obstacle avoidance, using newly designed 33 fuzzy inference rules for steering control and 13 rules for speed control, is proposed. Through simulations of various real world situations, we observed that the proposed fuzzy controller could drive the truck to the goal smoothly while avoiding the obstacles, and showed a reasonably good trajectory. This flexible and applicable fuzzy control logic can be adapted to provide easy interaction with the driver for state-of-the-art intelligent cruise control systems.     

Decentralized sliding mode control for flexible link robots

A technique using augmented sliding mode control for robust, real-time control of flexible multiple link robots is presented. For the purpose of controller design, the n-link, n-joint robot is subdivided into n single joint, single link subsystems. A sliding surface for each subsystem is specified so as to be globally, asymptotically stable. Each sliding surface contains rigid-body angular velocity, angular displacement and flexible body generalized velocities. The flexible body generalized accelerations are treated as disturbances during the controller design. This has the advantage of not requiring explicit equations for the flexible body motion. The result is n single input, single output controllers acting at the n joints of the robot, controlling rigid body angular displacement and providing damping for flexible body modes. Furthermore, the n controllers can be operated in parallel so that compute speed is independent of the number of links, affording real-time, robust, control.     

可重构模块机器人分散容错控制

针对可重构模块机器人的执行器故障,提出一种基于自适应模糊系统的分散被动容错控制方法.该方法不需要机器人动力学模型与模块之间的信息交换,模块控制器分别采用间接和直接自适应方法设计,自适应参数的更新律基于Lyapunov稳定性理论设计,保证了系统的稳定性和H∞跟踪性能.数值仿真结果表明了所提出方法的有效性.     

A local obstacle avoidance method for mobile robots in partially known environment

Local obstacle avoidance is a principle capability for mobile robots in unknown or partially known environment. A series of velocity space methods including the curvature velocity method (CVM), the lane curvature method (LCM) and the beam curvature method (BCM) formulate the local obstacle avoidance problem as one of constrained optimization in the velocity space by taking the physical constraints of the environment and the dynamics of the vehicle into account. We present a new local obstacle avoidance approach that combines the prediction model of collision with the improved BCM. Not only does this method inherit the quickness of BCM and the safety of LCM, but also the proposed prediction based BCM (PBCM) can be used to avoid moving obstacles in dynamic environments.     

Multiple robots formation manoeuvring and collision avoidance strategy

This paper presents a multiple robots formation manoeuvring and its collision avoidance strategy. The direction priority sequential selection algorithm is employed to achieve the raw path, and a new algorithm is then proposed to calculate the turningcompliant waypoints supporting the multi-robot formation manoeuvre. The collision avoidance strategy based on the formation control is presented to translate the collision avoidance problem into the stability problem of the formation. The extension-decompositionaggregation scheme is next applied to solve the formation control problem and subsequently achieve the collision avoidance during the formation manoeuvre. Simulation study finally shows that the collision avoidance problem can be conveniently solved if the stability of the constructed formation including unidentified objects can be satisfied.     

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.     

Neural network approach to acquiring free-gait motion of quadruped robots in obstacle avoidance

In obstacle avoidance by a legged mobile robot, it is not necessary to avoid all of the obstacles by turning only, because it can climb or stride over some of them, depending on the obstacle configuration and the state of the robot, unlike a wheel-type or a crawler-type robot. It is thought that mobility efficiency to a destination is improved by crawling over or striding over obstacles. Moreover, if robots have many legs, like 4-legged or 6-legged types, then the robot's movement range is affected by the order of the swing leg. In this article a neural network (NN) is used to determine the action of a quadruped robot in an obstacle-avoiding situation by using information about the destination, the obstacle configuration, and the robot's self-state. To acquire a free gait in static walking, the order of the swing leg is realized using an alternative NN whose inputs are the amount of movement and the robot's self-state. The design parameters of the former NN are adjusted by a genetic algorithm (GA) off-line. This work was presented in part at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004