Conditions for target tracking with range-only information

This paper addresses the problem of guiding a mobile robot towards a target using only range sensors. The bearing information is not available. The target can be stationary or moving. It can be the source of some gas leakage or nuclear radiation or it can be some landmark or beacon or any manoeuvring vehicle. The mobile robot can be a ground vehicle or an aerial vehicle flying at a fixed altitude. In literature, many different strategies are proposed which use the range only measurement but they involve estimation of different parameters or have switching control strategy which make them difficult to implement. We propose two sets of conditions, one for stationary target and another for both stationary and moving target. Any control strategy, that will satisfy these conditions, can bring the robot arbitrarily close to the target. There are no restrictions on the initial conditions. Estimation of any parameter is not required. Some candidate controllers are presented that included continuous controllers and switching controllers. Simulations are carried out with these controllers to validate our result with and without measurement noise. Experimental results with ground mobile robot are presented.     

Sensor-Based Motion Planning of Wheeled Mobile Robots in Unknown Dynamic Environments

This paper presents a new sensor-based online method for generating collision-free paths for differential-drive wheeled mobile robots pursuing a moving target amidst dynamic and static obstacles. At each iteration, the set of all collision-free directions are calculated using velocity vectors of the robot relative to each obstacle, forming the Directive Circle (DC), which is the fundamental concept of our proposed method. Then, the best feasible direction close to the optimal direction to the target is selected from the DC, which prevents the robot from being trapped in local minima. Local movements of the robot are governed by the exponential stabilizing control scheme that provides a smooth motion at each step, while considering the robot’s kinematic constraints. The robot is able to catch the target at a desired orientation. Extensive simulations demonstrated the efficiency of the proposed method and its success in coping with complex and highly dynamic environments with arbitrary obstacle shapes.     

Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model

This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.      

Active vision and receptive field development in evolutionary robots

In this paper, we describe the artificial evolution of adaptive neural controllers for an outdoor mobile robot equipped with a mobile camera. The robot can dynamically select the gazing direction by moving the body and/or the camera. The neural control system, which maps visual information to motor commands, is evolved online by means of a genetic algorithm, but the synaptic connections (receptive fields) from visual photoreceptors to internal neurons can also be modified by Hebbian plasticity while the robot moves in the environment. We show that robots evolved in physics-based simulations with Hebbian visual plasticity display more robust adaptive behavior when transferred to real outdoor environments as compared to robots evolved without visual plasticity. We also show that the formation of visual receptive fields is significantly and consistently affected by active vision as compared to the formation of receptive fields with grid sample images in the environment of the robot. Finally, we show that the interplay between active vision and receptive field formation amounts to the selection and exploitation of a small and constant subset of visual features available to the robot.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

机器人二维环境下仿人虚拟力场避障研究

动态环境下避障是机器人实现自主运动的关键。首先建立了适合虚拟力场算法的机器人工作环境数学描述。将人避障行走策略引入虚拟力场中,具体包括：设计了单元格障碍物可信度的邻域平滑累积值计算方法,模拟人对移动障碍物的躲避策略;建立可信度的不确定推理计算方法,处理信号和环境存在干扰问题;设计了基于目标点方位角的吸引力计算公式来解决目标点超出感知空间问题;设计了变权重加权排斥力计算方法,使机器人对前进方向的障碍更敏感;借鉴人绕开障碍物策略,采用临时旋转目标点方向得到的虚拟目标点来使机器人沿障碍物运动直到绕开。针对房间和街面环境,在MobotSim平台上进行仿真实验,给出了实验结果和分析。在合理设置参数下,机器人能避开障碍物到达目标点,且避障路径优于传统的虚拟力场方法。结果验证了该方法的有效性。     

在未知环境中基于模糊逻辑的移动机器人行为控制

本文介绍了一种在未知环境中基于模糊逻辑的移动机器人行为控制方法．传统的行为控制方法存在两个弱点：①行为不易描述；②多个行为之间的冲突和竞争难以协调．这篇文章的主要思想是将模糊逻辑控制与行为控制相结合致使这两个问题得到有效的解决．仿真实验结果表明：所提的方法通过多个行为如避障边沿行走和目标导向的融合，能够有效地对机器人在复杂和未知环境中导航．另外，该方法还适用于多传感器的融合与集成．     

Humanoid robot arm performance optimization using multi objective evolutionary algorithm

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria; minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.     

动态环境中移动机器人地图构建的研究进展

大部分现有的移动机器人地图构建方法都是基于静态环境的假设,而实际应用中移动机器人的工作环境是随时间变化的.综述了动态环境中移动机器人地图构建的最新研究进展,介绍了基于地图、基于运动和基于跟踪的检测动态障碍物的各种方法,分析比较了动态环境中移动机器人过滤运动障碍物传感器观测信息和结合运动障碍物传感器观测信息构建环境地图的主要方法,并总结了各种方法的优缺点.探讨了动态环境中移动机器人地图构建存在的难点问题,并展望了该领域的研究方向.     

A new moving target interception algorithm for mobile robots based on sub-goal forecasting and an improved scout ant algorithm

It is difficult to make a robot intercept a moving target, whose trajectory and speed are unknown and dynamically changing, in a comparatively short distance when the environment contains complex objects. This paper presents a new moving target interception algorithm in which the robot can intercept such a target by following many short straight line trajectories. In the algorithm, an intercept point is first forecasted assuming that the robot and the target both move along straight line trajectories. The robot rapidly plans a navigation path to this projected intercept point by using the new ant algorithm. The robot walks along the planned path while continuously monitoring the target. When the robot detects that the target has moved to a new grid it will re-forecast the intercept point and re-plan the navigation path. This process will be repeated until the robot has intercepted the moving target. The simulation results have shown that the algorithm is very effective and can successfully intercept a moving target while moving along a relatively short path no matter whether the environment has complex obstacles or not and the actual trajectory of the moving target is a straight line or a complex curve.     

Emergent behaviors of a fuzzy sensory-motor controller evolved bygenetic algorithm

Recently, there has been extensive work on the construction of fuzzy controllers for mobile robots by a genetic algorithm (GA); therefore, we can realize evolutionary optimization as a promising method for developing fuzzy controllers. However, much investigation on the evolutionary fuzzy controller remains because most of the previous works have not seriously attempted to analyze the fuzzy controller obtained by evolution. This paper develops a fuzzy logic controller for a mobile robot with a GA in simulation environments and analyzes the behaviors of the controller with a state transition diagram of the internal model. Experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved wen enough to smoothly drive the robot in different environments. The robot produces emergent behaviors by the interaction of several fuzzy rules obtained.     

Navigation system for an autonomous robot using an ocellus camera in an indoor environment

Autonomous and mobile robots are being expected to provide various services in human living environments. However, many problems remain to be solved in the development of autonomous robots that can work like humans. When a robot moves, it is important that it be able to have self-localization abilities and recognize obstacles. For a human, the present location can be correctly checked through a comparison between memorized information assuming, it is correct, and the present situation. In addition, the distance to an object and the perception of its size can be estimated by a sense of distance based on memory or experience. Therefore, the environment for robotic activity assumed in this study was a finite-space such as a family room, an office, or a hospital room. Because an accurate estimation of position is important to the success of a robot, we have developed a navigation system with self-localization ability which uses only a CCD camera that can detect whether the robot is moving accurately in a room or corridor. This article describes how this system has been implemented and tested with our developed robot.     

A new class of adaptive controllers for robot trajectory tracking

This article presents a new class of adaptive schemes for the motion control of robot manipulators. The proposed controllers are very general and computationally efficient because they do not require knowledge of either the mathematical model or the parameter values of the manipulator dynamics, and are implemented without calculation of the robot inverse dynamics or inverse kinematic transformations. It is shown that the control strategies are globally uniformly bounded in the presence of bounded disturbances, and that in the absence of disturbances the ultimate bound on the size of the tracking errors can be made arbitrarily small. Computer simulation results are given for a PUMA 560 manipulator, and demonstrate that accurate and robust trajectory tracking can be achieved by using the proposed controllers. © 1994 John Wiley & Sons, Inc.     

Evolution of adaptive synapses: robots with fast adaptive behavior in new environments.

This paper is concerned with adaptation capabilities of evolved neural controllers. We propose to evolve mechanisms for parameter self-organization instead of evolving the parameters themselves. The method consists of encoding a set of local adaptation rules that synapses follow while the robot freely moves in the environment. In the experiments presented here, the performance of the robot is measured in environments that are different in significant ways from those used during evolution. The results show that evolutionary adaptive controllers solve the task much faster and better than evolutionary standard fixed-weight controllers, that the method scales up well to large architectures, and that evolutionary adaptive controllers can adapt to environmental changes that involve new sensory characteristics (including transfer from simulation to reality and across different robotic platforms) and new spatial relationships.     

An integrated path-planning and control approach for nonholonomic unicycles using switched local potentials

In this paper, navigation and control of an autonomous mobile unicycle robot in an obstacle-ridden environment is considered. The unicycle dynamic model used has two differentially driven wheels, with the motor torques as the system input. Two novel potential-field-based controllers are derived, which stabilize the robot within a surrounding circular area (henceforth called a bubble) of arbitrary size. The first controller takes the unicycle to the center of its bubble, while the second corrects its orientation. The designed potentials also work with a kinematic model. Explicit bounds for permissible initial speeds are derived, such that maximum torque limits and/or maximum speed limits are not violated once the controller is activated. These controllers are then embedded in a navigation framework. An existing global planner is used to first create a string of variable-sized bubbles which connect the start point to the goal point, with each bubble's size indicative of the radial obstacle clearance available from its center. The robot then keeps itself within a fixed-sized bubble, which it then moves in discrete steps, according to the direction provided by the global plan, while repulsively avoiding unexpected obstacles. Hence, the gross movement is created by switching local potential-field-based controllers. This scheme is first verified in computer simulation of a single robot moving in a maze. It is then implemented on an experimental setup of robots equipped with proximity sensors. Results are presented to illustrate the effectiveness of the system.     

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 modelling and resolution for robotic mobile manipulators: a general approach

In this work the topic of kinematic redundancy modelling and resolution for robotic mobile manipulators is considered. A set of redundancy parameters is introduced to define a general inverse kinematic procedure for mobile manipulators. Then, redundancy is treated as a non-linear optimization problem with the purpose of finding robot configurations that maximize the designed metric measures. Some strategies to design the optimization objective function are introduced in order to achieve desirable redundant behaviours, such as obstacles avoidance, mobile base motions reductions and dexterity optimization. Moreover, the robot controller has been developed following an object-oriented software architecture principle that allows to keep it general and robot independent. As a prove of reliability and generality of our approach, the same controller has been used to control several different mobile manipulators in a simulation environment, as well as a real KUKA youBot robot.     

Visual Motor Control of a 7 DOF Robot Manipulator Using Function Decomposition and Sub-Clustering in Configuration Space

This paper deals with real-time implementation of visual-motor control of a 7 degree of freedom (DOF) robot manipulator using self-organized map (SOM) based learning approach. The robot manipulator considered here is a 7 DOF PowerCube manipulator from Amtec Robotics. The primary objective is to reach a target point in the task space using only a single step movement from any arbitrary initial configuration of the robot manipulator. A new clustering algorithm using Kohonen SOM lattice has been proposed that maintains the fidelity of training data. Two different approaches have been proposed to find an inverse kinematic solution without using any orientation feedback. In the first approach, the inverse Jacobian matrices are learnt from the training data using function decomposition. It is shown that function decomposition leads to significant improvement in accuracy of inverse kinematic solution. In the second approach, a concept called sub-clustering in configuration space is suggested to provide multiple solutions for the inverse kinematic problem. Redundancy is resolved at position level using several criteria. A redundant manipulator is dexterous owing to the availability of multiple configurations for a given end-effector position. However, existing visual motor coordination schemes provide only one inverse kinematic solution for every target position even when the manipulator is kinematically redundant. Thus, the second approach provides a learning architecture that can capture redundancy from the training data. The training data are generated using explicit kinematic model of the combined robot manipulator and camera configuration. The training is carried out off-line and the trained network is used on-line to compute the joint angle vector to reach a target position in a single step only. The accuracy attained is better than the current state of art.     

Velocity planning for a mobile robot to track a moving target — a potential field approach

Potential field method has been widely used for mobile robot path planning, but mostly in a static environment where the target and the obstacles are stationary. The path planning result is normally the direction of the robot motion. In this paper, the potential field method is applied for both path and speed planning, or the velocity planning, for a mobile robot in a dynamic environment where the target and the obstacles are moving. The robot’s planned velocity is determined by relative velocities as well as relative positions among robot, obstacles and targets. The implementation factors such as maximum linear and angular speed of the robot are also considered. The proposed approach guarantees that the robot tracks the moving target while avoiding moving obstacles. Simulation studies are provided to verify the effectiveness of the proposed approach.