Methodology of Concept Control Synthesis to Avoid Unmoving and Moving Obstacles

The dynamic path generation problem of robots in environments with other unmoving and moving objects is considered. Generally, the problem is known in the literature as find path or robot motion planning. In this paper, we apply the behavioral cloning approach to design the robot controller. In behavioral cloning, the system learns from control traces of a human operator. The task for the given problem is to find a controller in the form of an explicit mathematical expression. Thus, machine learning programs to induce the operator's trajectories as a set of symbolic constraints are used. Then, mathematical induction to generalize the obtained equations in order to apply them in situ with an infinite number of obstacles is also used. A method to evaluate cloning success is proposed. The typical kind of noise is included.     

六自由度检修机械臂合理路径规划系统设计

为减少六自由度检修机械臂抓取动作花费的时间,设计一种六自由度检修机械臂合理路径规划系统。此次研究的系统硬件部分主要包含主控制器、伺服电机控制器与通信模块。系统软件部分,计算出机器人与障碍物之间的距离,并计算出所有机器人上点与障碍物之间的距离,采用设置虚拟障碍物的创新型人工势场法进行路径规划,避免传统人工势场法的弊端,以保证机器人与障碍物之间有一定的安全距离,以此完成六自由度检修机械臂合理路径规划。实验结果表明,在有障碍的情况下,此次研究的六自由度检修机械臂合理路径规划系统在2~4 min之内就能够完成抓取动作,并且此次研究的系统控制后的关节曲线较为平稳,证明此次研究的六自由度检修机械臂合理路径规划系统具有较好的控制效果,满足了系统设计需求。     

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.     

融合动态障碍物运动信息的路径规划算法

传统的路径规划算法只能在障碍物不发生位置变化的环境中计算最优路径。但是随着机器人在商场、医院、银行等动态环境下的普及,传统的路径规划算法容易与动态障碍物发生碰撞等危险。因此,关于随机动态障碍物条件下的机器人路径规划算法需要得到进一步改善。为了解决在动态环境下的机器人路径规划问题,提出了一种融合机器人与障碍物运动信息的改进动态窗口法来解决机器人在动态环境下的局部路径规划问题,并且与优化A*算法相结合来实现全局最优路径规划。主要内容体现为：在全局路径规划上,采用优化A*算法求解最优路径。在局部路径规划上,以动态障碍物的速度作为先验信息,通过对传统动态窗口法的评价函数进行扩展,实现机器人在动态环境下的自主智能避障。实验证明,该算法可以实现基于全局最优路径的实时动态避障,具体表现为可以在不干涉动态障碍物的条件下减少碰撞风险、做出智能避障且路径更加平滑、长度更短、行驶速度更快。     

Terrain-evaluation-based motion planning for legged locomotion on irregular terrain

The path planning of legged locomotion is complex in that path generation is based on constraints not only from body motion, but also from leg motion. A general approach to path planning will fail in generating a feasible path for walking machines when facing the huge searching space of legged locomotion. In this paper, an effective method of path planning is introduced by virtue of terrain evaluation. It maps obstacles into the robot configuration space by evaluating the obstacles' influence on the legged locomotion. The evaluation produces an index of terrain, called terrain complexity, for path planning. Using potential-guided searching, the terrain with mapped obstacles is searched to generate a feasible path.     

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.     

基于混合策略的轮式机器入路径规划方法

快速扩展随机树方法（R RT）是解决具有非完整性约束的轮式机器人路径规划问题的一种有效途径。R RT能够在规划过程中引入机器人动力学约束,但是当环境中存在大量障碍物时,R RT算法的路径搜索效率将会降低。另一方面,R RT算法不具有最优性,限制了其在轮式机器人路径规划中的应用。针对经典R RT算法的不足,提出一种混合的路径规划策略,首先通过路径导引点扩展多树R RT结构,利用多树R RT的局部探索与合并特性快速寻找可通行的区域范围,利用启发式搜索算法在可通行区域内快速寻找动力学可行的机器人运动轨迹。仿真与实车实验表明,该方法能够快速有效地解决复杂障碍物环境下的机器人路径规划问题。     

融合环境信息和运动约束的改进A*算法研究

标准A*算法存在着无法考虑移动机器人运动特性及处理后的路径不利于移动机器人运动等问题。针对这一问题提出了一种新改进A*算法,通过环境信息引入障碍物权重系数来改进算法的启发函数并进行全局路径规划;优化搜索节点的选取方式和设定障碍物与路径之间的安全距离;基于对移动机器人的运动特性的考虑优化其路径,并在不同环境地图中与其他算法进行仿真实验对比分析。相关实验表明：基于新改进A*算法规划的路径始终与障碍物保持一定的安全距离;改进A*算法在时间上相比标准A*算法平均减少了80%,路径长度平均减少了2%,路径转角平均降低了82%。改进后算法相比其他算法在时间、搜索节点以及平滑度上有很大的改进,融合机器人环境信息和运动特性的规划路径算法可为移动机器人的路径规划提供一种新的方法。     

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.     

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 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.     

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.     

Path planning for a quadruped robot: an artificial field approach

In this paper, we consider the problem of planning a feasible path for a quadruped walking robot in an environment of obstacles. In conventional path-planning problems, the main focus is merely collision avoidance with obstacles since a wheeled robot is involved. However, in the case of a legged robot, both collision avoidance and crossing over obstacles must be taken into account in the process of path planning. Furthermore, the constraints of the gait should be considered to guarantee the feasibility of a planned path. To resolve this complicated problem in a systematic way, a new concept of an artificial thermal field is proposed. Specifically, with the assumption that a robot walks with a periodic crab gait, a robot and obstacles in a three-dimensional (3D) space are projected on a 2D plane. Next, the 2D obstacles are transformed into the configuration space of a quadruped robot. A feasible path is finally sought in an artificial thermal field which is constructed numerically on the discretized configuration space. To verify the efficacy of the proposed approach, three notable simulation results are provided.     

A Generalized Real-Time Obstacle Avoidance Method Without the Cspace Calculation

An important concept proposed in the early stage of robot path planning field is the shrinking of a robot to a point and meanwhile the expanding of obstacles in the workspace as a set of new obstacles. The resulting grown obstacles are called the Configuration Space (Cspace) obstacles. The find-path problem is then transformed into that of finding a collision-free path for a point robot among the Cspace obstacles. However, the research experiences have shown that the Cspace transform is very hard when the following situations occur: 1) both the robot and obstacles are not polygons, and 2) the robot is allowed to rotate. This situation gets even worse when the robot and obstacles are three dimensional (3D) objects with various shapes. For this reason, direct path planning approaches without the Cspace transformation is quite useful and expected.Motivated by the practical requirements of robot path planning, a generalized constrained optimization problem (GCOP) with not only logic AND but also logic OR relationships was proposed and a mathematical solution developed previously. This paper inherits the fundamental ideas of inequality and optimization techniques from the previous work, converts the obstacle avoidance problem into a semi-infinite constrained optimization problem with the help of the mathematical transformation, and proposes a direct path planning approach without Cspace calculation, which is quite different from traditional methods. To show its merits, simulation results in 3D space have been presented.     

Mobile robot path planning with surrounding point set and path improvement

The objective of the path planning problem for a mobile robot is to generate a collision-free path from a starting position to a target position with respect to a certain fitness function, such as distance. Although, over the last few decades, path planning has been studied using a number of methodologies, the complicated and dynamic environment increases the complexity of the problem and makes it difficult to find an optimal path in reasonable time. Another issue is the existence of uncertainty in previous approaches. In this paper, we propose a new methodology to solve the path planning problem in two steps. First, the surrounding point set (SPS) is determined where the obstacles are circumscribed by these points. After the initial feasible path is generated based on the SPS, we apply a path improvement algorithm depending upon the former and latter points (PI_FLP), in which each point in the path is repositioned according to two points on either side. Through the SPS, we are able to identify the necessary points for solving path planning problems. PI_FLP can reduce the overall distance of the path, as well as achieve path smoothness. The SPS and PI_FLP algorithms were tested on several maps with obstacles and then compared with other path planning methods As a result, collision-free paths were efficiently and consistently generated, even for maps with narrow geometry and high complexity.     

Collision-free motion planning of dual-arm reconfigurable robots

Dual-arm reconfigurable robot is a new type of robot. It can adapt to different tasks by changing its different end-effector modules which have standard connectors. Especially, in fast and flexible assembly, it is very important to research the collision-free planning of dual-arm reconfigurable robots. It is to find a continuous, collision-free path in an environment containing obstacles. A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used in the paper. This method is based on configuration space (C-Space). The method of configuration space and the concepts reachable manifold and contact manifold are successfully applied to the collision-free motion planning of dual-arm robot. The complexity of dual-arm robots’ collision-free planning will reduce to a search in a dispersed C-Space. With this algorithm, a real-time optimum path is found. And when the start point and the end point of the dual-arm robot are specified, the algorithm will successfully get the collision-free path real time. A verification of this algorithm is made in the dual-arm horizontal articulated robot SCARATES, and the simulation and experiment ascertain that the algorithm is feasible and effective.     

A linear programming approach for optimizing the path of robot arm in an obstacle oriented work cell

A robot work cell has many peripheral devices and other components that may be placed inside the work envelop of a robot. The control computer not only has to interact with the peripheral devices but must also avoid collision with the physical components. Futhermore the work pieces can appear in various geometrical forms. All these objects are candidate obstacles in the work envelope.

The problem is to find a shortest route for the end effector, avoiding collision with any of the object and still choose the shortest path to the target point. Present approaches used for the same purpose (1),(2), (3) and (4) are either computationaly inefficient or require expensive equipment. This paper offers an alternative approach of applying Linear Programming techniques to find the shortest path of the robot arm to the target point. For simplicity, the robot considered is a three dimensional gantry robot.

The linear model formulated in this paper is based on the assumption that at any point in time obstacle coordinates of all vertices of obstacles are known to the controller. This model approximates the distance travelled by the end effector by calculating its motion in each axis. The model minimizes the sum total travel of the arm in the X, Y, Z directions.     

基于障碍物运动预测的移动机器人路径规划

针对移动机器人局部动态避障路径规划问题开展优化研究。基于动态障碍物当前历史位置轨迹,提出动态障碍物运动趋势预测算法。在移动机器人的动态避障路径规划过程中,考虑障碍物当前的位置,评估动态障碍物的移动轨迹;提出改进的D*Lite路径规划算法,大幅提升机器人动态避障算法的效率与安全性。搭建仿真验证环境,给出典型的单动态障碍物、多动态障碍物场景,对比验证了避障路径规划算法的有效性。     

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Integrated motion planning and control for the purposes of maneuvering mobile robots under state- and input constraints is a problem of vital practical importance in applications of mobile robots such as autonomous transportation. Those constraints arise naturally in practice due to specifics of robot mechanical construction and the presence of obstacles in motion environment. In contrast to approaches focusing on feedback control design under the assumption of given reference motion or motion planning with neglection of subsequent feedback motion execution, we adopt a controller-driven motion planning paradigm, which has recently gained attention of many researchers. It postulates design of motion planning algorithms dedicated to specific feedback control policies, which compute a sequence of feedback control subtasks instead of classically planned open-loop controls or parametric paths. In this spirit, we propose a motion planning algorithm driven by the VFO (Vector Field Orientation) control law for the waypoint-following task. Presented analysis of the VFO control law reveals its beneficial properties, which are subsequently utilized to solve a generally nonlinear and non-convex optimal motion planning problem by formulating it as a mixed-integer linear program (MILP). The solution proposed in this paper yields a waypoint sequence, which is designed for execution by application of the VFO control law to drive a robot to a prescribed final configuration under an input constraint imposed by bounded curvature of robot motion and state constraints resulting from a convex decomposition of task space. Satisfaction of these constraints is guaranteed analytically and exactly, i.e., without utilization of numerical approximations. Moreover, for a given discrete set of possible waypoint orientations, the proposed algorithm computes plans optimal w.r.t. given cost functional, which can be any convex linear combination of quantities such as robot path length, curvature of robot motion, distance to imposed state constraints, etc. Furthermore, the planning algorithm exploits the possibility of both forward or backward movement of the robot to allow maneuvering in demanding environments. Generated waypoint sequences are a compact representation of a motion plan, which can be immediately executed with the VFO controller without any additional post-processing. Validity of the proposed approach has been confirmed by simulation studies and experimental motion execution with a laboratory-scale mobile robot.     

基于激光雷达的移动机器人障碍测距研究

障碍距离检测是移动机器人导航的关键问题之一。为了实现精确实时的障碍检测,针对某二维TOF激光雷达,对其数据标定、物体表面的属性、混合像素等因素进行试验,评估了其测距性能。同时,通过移动机器人运行过程中激光雷达的测距数据分析,设计了动态自适应滤波器以消除障碍检测中的测距噪声干扰。运行过程中的障碍检测试验表明:该方法可以实现可靠的障碍检测,并为移动机器人导航中环境建模、自定位及路径规划提供支持。