An Information Roadmap Method for Robotic Sensor Path Planning

A new probabilistic roadmap method is presented for planning the path of a robotic sensor deployed in order to classify multiple fixed targets located in an obstacle-populated workspace. Existing roadmap methods have been successful at planning a robot path for the purpose of moving from an initial to a final configuration in a workspace by a minimum distance. But they are not directly applicable to robots whose primary objective is to gather target information with an on-board sensor. In this paper, a novel information roadmap method is developed in which obstacles, targets, sensor’s platform and field-of-view are represented as closed and bounded subsets of an Euclidean workspace. The information roadmap is sampled from a normalized information theoretic function that favors samples with a high expected value of information in configuration space. The method is applied to a landmine classification problem to plan the path of a robotic ground-penetrating radar, based on prior remote measurements and other geospatial data. Experiments show that paths obtained from the information roadmap exhibit a classification efficiency several times higher than that of existing search strategies. Also, the information roadmap can be used to deploy non-overpass capable robots that must avoid targets as well as obstacles.     

Real-Time Collision-Free Path Planning for Robots in Configuration Space

Collision-free path planning for an industrial robot in configuration space requires mapping obstacles from robot‘s workspace into its configuration space.In this paper,an approach to real-time collision-free path planning for robots in configuration space is presented.Obstacle mapping is carried out by fundamental obstacles defined in the workspace and their images in the configuration space.In order to avoid dealing with unimportant parts of the configuration space that do not affect searching a collision-free path between starting and goal configurations,we construct a free subspace by slice configuration obstacles.In this free subspace,the collision-free path is determined by the A^* algorithm.Finally,graphical simulations show the effectiveness of the proposed approach.     

Integrating explicit path planning with reactive control of mobile robots using Trulla

This article describes and investigates a method of interleaving explicit path planning with reactive control. The Trulla all-paths planner computes an a priori set of optimal paths. Minor reactions to obstacles and terrain changes serve to switch the robot from the precomputed path to a new precomputed path, eliminating subgoal obsession. Major deviations suggest that the a priori map is significantly wrong; explicit replanning should be triggered to ensure continued progress of the robot. The dot product is used as the intrinsic measure of a major deviation. This methodology is particularly well-suited for computationally bound robots such as planetary rovers and robots operating in indoor environments with a large number of minor unmodeled obstacles.

The article describes the Trulla and dot product algorithms, and reports on experimental data collected from a mobile robot under representative scenarios. The method is compared to continuous and fixed frequency replanning under differing environments and robot velocities. The results show that the deferred replanning with the Trulla/dot product methodology produced actual paths similar to more frequent replanning in distance and time but with up to 100 times less computation. The reduced computation led to a 8.75% increase in distance traveled and 24% increase in travel time. In the presence of faulty sensor data, Trulla outperformed the other methods which radically changed the path back and forth due to spurious sensor readings.     

View planning for exploration via maximal C-space entropy reduction for robot mounted range sensors

We introduced the concept of C-space entropy recently as a measure of knowledge of configuration space (C-space) for sensor-based exploration and path planning for general robot–sensor systems. The robot plans the next sensing action to maximally reduce the expected C-space entropy, also called the Maximal expected Entropy Reduction (MER) criterion. The resulting view planning algorithms showed significant improvement of exploration rate over physical space-based criteria. However, this expected C-space entropy computation made two idealized assumptions: (i) that the sensor field of view (FOV) is a point and (ii) that there are no occlusion (or visibility) constraints, i.e., as if the sensor can sense through the obstacles. We extend the expected C-space entropy formulation where these two assumptions are relaxed, and consider a range sensor with non-zero volume FOV and occlusion constraints, thereby modeling a realistic range sensor. Planar simulations and experimental results on the SFU Eye-in-Hand system show that the new formulation results in further improvement in C-space exploration efficiency over the point FOV sensor-based MER formulation.     

基于遗传算法的多移动机器人协调路径规划

采用链接图法建立了机器人工作空间模型;应用遗传算法规划多移动机器人运动路径; 引入适应值调整矩阵新概念,以达到对多移动机器人运动路径的全局优化;基于面向对象技术,研 制成功多移动机器人路径规划动态仿真系统.大量仿真实验结果表明,所提方法可行.     

Obstacle Modelling Oriented to Safe Motion Planning and Control for Planar Rigid Robot Manipulators

Trajectory planning and tracking are crucial tasks in any application using robot manipulators. These tasks become particularly challenging when obstacles are present in the manipulator workspace. In this paper a n-joint planar robot manipulator is considered and it is assumed that obstacles located in its workspace can be approximated in a conservative way with circles. The goal is to represent the obstacles in the robot configuration space. The representation allows to obtain an efficient and accurate trajectory planning and tracking. A simple but effective path planning strategy is proposed in the paper. Since path planning depends on tracking accuracy, in this paper an adequate tracking accuracy is guaranteed by means of a suitably designed Second Order Sliding Mode Controller (SOSMC). The proposed approach guarantees a collision-free motion of the manipulator in its workspace in spite of the presence of obstacles, as confirmed by experimental results.     

移动机器人的时间最优编队

针对移动机器人的最速编队问题,结合路径规划和任务分解,提出一种分派问题的新解法和时间最优的编队策略。该策略充分考虑了障碍物环境约束和各机器人运动时的相互影响,通过将系统整体路径规划的复杂问题分解为独立路径规划问题和冲突协调问题来分别求解,降低了计算的复杂性,并能了快编队。     

Workspace mapping based on multisensor information fusion using heterogeneous onboard sensors

In recent years, multiple robot systems that perform team operations have been developed. These robot systems are expected to execute complicated tasks smoothly in a given congested workspace. In this article, we propose a workspace mapping algorithm using ultrasonic stereo sonar and an image sensor in order to operate the mobile robots among obstacles. This workspace mapping algorithm involves two steps: (1) the position detection of obstacles using ultrasonic stereo sonar, and (2) the shape detection of obstacles using an image sensor. While each robot moves around in the given workspace, the two steps of the mapping algorithm are repeated and sensor data are collected. The robot measures the distance and the direction of obstacles using ultrasonic stereo sonar. The shape of obstacles is also captured using an onboard image sensor. A workspace map is created based on the sensor data accumulated from the proposed method, and successful results are also obtained through experiments.     

Path planning for manipulators based on an improved probabilistic roadmap method

An indispensable feature of an intelligent manipulator is its capability to quickly plan a short and safe path in the presence of obstacles in its workspace. Among the path planning methods, the probabilistic roadmap (PRM) method has been widely applied in path planning for a high-dimensional manipulator to avoid obstacles. However, its efficiency remains disappointing when the free space of manipulators contains narrow passages. To solve this problem, an improved PRM method is proposed in this paper. Based on a virtual force field, a new sampling strategy of PRM is presented to generate configurations more appropriate for practical application in the free space. Correspondingly, in order to interconnect these configurations to form a roadmap, a new connection strategy is designed, which consists of three stages and can gradually improve the connectivity of the roadmap. The contributions of this paper are as follows. The new sampling strategy can increase the sampling density at the narrow passages of the free space and reduce the redundancy of the samples in the wide-open regions of the free space; the three-stage connection strategy for interconnecting samples can ensure a high-connectivity roadmap; through synthesizing the above strategies, the improved PRM method is more suitable for path planning of manipulators to avoid obstacles efficiently in a cluttered environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available for manipulator of any degrees of freedom.     

动态不确定环境下移动机器人的在线实时路径规划

提出一种基于极坐标空间的、以机器人期望运动方向角为路径优化指标的动态不确定环境下移动机器人的在线实时路径规划方法。该法通过机器人的传感器系统,实时探测局部环境信息,在每一采样时刻,机器人首先对视野内的动态障碍物的位置进行采样,然后根据所采样的位置信息,利用自回归模型预测出下一采样时刻动态障碍物的位置,再将预测位置上的动态障碍物当作静态障碍物来处理,然后对其规划避碰路径,从而将动态路径规划转化为静态路径规划。仿真和实验结果验证了该方法有效可行,具有实时规划性和良好的避障能力。     

异构多目标差分-动态窗口算法 及其在移动机器人中的应用

为了实现在多移动机器人和多窄通道的复杂动态环境中机器人的节能运动规划,提出异构多目标差分-动态窗口法(heterogeneous multi-objective differential evolution-dynamic window algorithm,HMODE-DWA).首先,建立行驶时间、执行器作用力和平滑度的3目标优化模型,设计具有碰撞约束的异构多目标差分进化算法来获得3个目标函数的最优解,进而在已知的静态环境中获得帕累托前沿,利用平均隶属度函数获得起点与终点间最优的全局路径;其次,定义基于环境缓冲区域的模糊动态窗口法使机器人完成动态复杂环境中避障,利用所提出的HMODE-DWA算法动态避障的同时实现节能规划.仿真和实验结果表明,所提出的混合路径规划控制策略能够有效降低移动机器人动态避障过程中的能耗.     

3D Collision-Free Motion Based on Collision Index

A collision-free motion planning method for mobile robots moving in 3-dimensional workspace is proposed in this article. To simplify the mathematical representation and reduce the computation complexity for collision detection, objects in the workspace are modeled as ellipsoids. By means of applying a series of coordinate and scaling transformations between the robot and the obstacles in the workspace, intersection check is reduced to test whether the point representing the robot falls outside or inside the transformed ellipsoids representing the obstacles. Therefore, the requirement of the computation time for collision detection is reduced drastically in comparison with the computational geometry method, which computes a distance function of the robot segments and the obstacles. As a measurement of the possible occurrence of collision, the collision index, which is defined by projecting conceptually an ellipsoid onto a 3-dimensional Gaussian distribution contour, plays a significant role in planning the collision-free path. The method based on reinforcement learning search using the defined collision index for collision-free motion is proposed. A simulation example is given in this article to demonstrate the efficiency of the proposed method. The result shows that the mobile robot can pass through the blocking obstacles and reach the desired final position successfully after several trials.     

Planning exploration strategies for simultaneous localization and mapping

In this paper, we present techniques that allow one or multiple mobile robots to efficiently explore and model their environment. While much existing research in the area of Simultaneous Localization and Mapping (SLAM) focuses on issues related to uncertainty in sensor data, our work focuses on the problem of planning optimal exploration strategies. We develop a utility function that measures the quality of proposed sensing locations, give a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map.We also provide an efficient algorithm driven by our utility function. This algorithm is able to explore several steps ahead without incurring too high a computational cost. We have compared that exploration strategy with a totally greedy algorithm that optimizes our utility function with a one-step-look ahead.The planning algorithms which have been developed operate using simple but flexible models of the robot sensors and actuator abilities. Techniques that allow implementation of these sensor models on top of the capabilities of actual sensors have been provided.All of the proposed algorithms have been implemented either on real robots (for the case of individual robots) or in simulation (for the case of multiple robots), and experimental results are given.     

人工势场法在移动机器人路径规划中的改进

随着智慧工厂的逐渐发展, 移动机器人在工厂中的应用越来越广泛, 但是在工厂中障碍物较多, 使用传统人工势场法容易产生目标不可达以及局部最小值等问题. 本文针对传统人工势场法在路径规划中出现的目标不可达以及局部最优解进行改进. 首先针对目标不可达的情况, 采用新斥力势场函数, 通过对原人工势场法中的斥力势场函数增加影响函数, 从而解决目标不可达; 其次针对局部最优解, 采用人工势场法与模拟退火法相结合的方法, 利用模拟退火法中的增设子目标点, 打破平衡状态, 从而走出障碍物. 最后通过Matlab对比, 本文算法在10个障碍物中比其他文献中算法的行驶时间提升6.70%, 路径长度减少9.20%. 本文算法在20个障碍物中比其他文献中算法的行驶时间提升9.10%, 路径长度减少12.10%.     

Robot Motion Planning: A Game-Theoretic Foundation

Analysis techniques and algorithms for basic path planning have become quite valuable in a variety of applications such as robotics, virtual prototyping, computer graphics, and computational biology. Yet, basic path planning represents a very restricted version of general motion planning problems often encountered in robotics. Many problems can involve complications such as sensing and model uncertainties, nonholonomy, dynamics, multiple robots and goals, optimality criteria, unpredictability, and nonstationarity, in addition to standard geometric workspace constraints. This paper proposes a unified, game-theoretic mathematical foundation upon which analysis and algorithms can be developed for this broader class of problems, and is inspired by the similar benefits that were obtained by using unified configuration-space concepts for basic path planning. By taking this approach, a general algorithm has been obtained for computing approximate optimal solutions to a broad class of motion planning problems, including those involving uncertainty in sensing and control, environment uncertainties, and the coordination of multiple robots. Received November 11, 1996; revised March 13, 1998.     

Cooperative exploration and protection of a workspace assisted by information networks

We develop strategies that enable multiple robots to cooperatively explore an unknown workspace while building information networks. Every robot deploys information nodes with sensing and communication capabilities while constructing the Voronoi diagram as the topological map of the workspace. The resulting information networks constructed by individual robots will eventually meet, allowing for inter-robot information sharing. The constructed information network is then employed by the mobile robots to protect the workspace against intruders. We introduce the intruder capturing strategy on the Voronoi diagram assisted by information networks.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.     

Obstacle avoidance via articulation

This article presents a practical implementation of the theory for articulation of spatial robots such as the PUMA and TRS type robots. The “Blueprint Algorithm” is introduced as a simple rule-based planning algorithm in which the geometry of interference takes on the role of a basis in a collision-free path planning process amongst obstacles. This implementation presents a novel idea of “Machine Intelligence Via Robot Geometry” which utilizes robot attributes such as workspace and dexterity.     

Autonomous multi-mobile robot system: simulation and implementation using fuzzy logic

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.