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。     

An obstacle avoidance algorithm for robot manipulators based on decision-making force

Obstacle avoidance is a significant skill not only for mobile robots but also for robot manipulators working in unstructured environments. Various algorithms have been proposed to solve off-line planning and on-line adaption problems. However, it is still not able to ensure safety and flexibility in complex scenarios. In this paper, a novel obstacle avoidance algorithm is proposed to improve the robustness and flexibility. The method contains three components: A closed-loop control system is used to filter the preplanned trajectory and ensure the smoothness and stability of the robot motion; the dynamic repulsion field is adopted to fulfill the robot with primitive obstacle avoidance capability; to mimic human’s complex obstacle avoidance behavior and instant decision-making mechanism, a parametrized decision-making force is introduced to optimize all the feasible motions. The algorithms were implemented in planar and spatial robot manipulators. The comparative results show the robot can not only track the task trajectory smoothly but also avoid obstacles in different configurations.     

New technique of mobile robot navigation using a hybrid adaptive fuzzy potential field approach

This paper presents a summary of the research aimed at developing a new reliable methodology for robot navigation and obstacle avoidance. This new approach is based on the artificial potential field (APF) method, which is used extensively for obstacle avoidance. The classical APF is dependent only on the separation distance between the robot and the surrounding obstacles. The new scheme introduces a variable, which is used to determine the importance that each obstacle has on the robot's future path. The importance variable is dependent on the obstacles position, both angle and distance, with respect to the robot. Simulation results are presented demonstrating the ability of the algorithm to perform successfully in simple environments.     

基于模型预测控制与改进人工势场法的多无人机路径规划

模型预测控制(model predictive control,MPC)已成功地应用于无人机集群的路径规划.但其存在计算量大及单步运算时间长等不足,在实时运行中往往难以获得较高的控制频率.而离线的MPC需要准确的地图信息,难以处理地图中无法预测的动态障碍物.对此,提出一种结合离线MPC全局规划与在线改进人工势场法局部规划的方法.在利用MPC方法生成安全、平滑轨迹的同时,提高无人机在动态障碍物影响下的避障能力.通过引入调节力来处理传统人工势场法的局部极小值问题,并将目标与无人机的相对距离引入斥力函数,同时改进引力函数,以改善无人机在目标点处低速徘徊的问题.此外,设计一种事件触发的无人机轨迹变更与轨迹恢复策略,使无人机仅在必要时实施动态避障行为.在此基础上,最大化利用原来的规划轨迹.仿真验证结果表明,所提出的路径规划方法能够使无人机集群安全飞行至目标点,并且具有良好的动态避障能力.     

基于BP神经网络的移动机器人避障设计及仿真

为了解决移动机器人在复杂环境中如何高效精确地躲避障碍物的问题,提出了一种基于BP神经网络的避障方法。建立了机器人的避障运动模型并设计了神经网络避障控制系统;分析了机器人在运动过程中与障碍物的位置关系,使用超声波传感器采集距离信息,进行BP神经网络输入、输出训练并采用Matlab工具进行仿真试验。结果表明,该方法可以高效精确地实现移动机器人的自主避障,运行相对稳定、轨迹连续平滑,达到了较为理想的避障效果。验证了方法的可行性和有效性,为移动机器人自主避障提供了一种新的控制方法。     

Trajectory time scaling of a mobile robot to avoid dynamic obstacles on the basis of the INLVO

To ensure the collision safety of mobile robots, the velocity of dynamic obstacles should be considered while planning the robot’s trajectory for high-speed navigation tasks. A planning scheme that computes the collision avoidance trajectory by assuming static obstacles may result in obstacle collisions owing to the relative velocities of dynamic obstacles. This article proposes a trajectory time-scaling scheme that considers the velocities of dynamic obstacles. The proposed inverse nonlinear velocity obstacle (INLVO) is used to compute the nonlinear velocity obstacle based on the known trajectory of the mobile robot. The INLVO can be used to obtain the boundary conditions required to avoid a dynamic obstacle. The simulation results showed that the proposed scheme can deal with typical collision states within a short period of time. The proposed scheme is advantageous because it can be applied to conventional trajectory planning schemes without high computational costs. In addition, the proposed scheme for avoiding dynamic obstacles can be used without an accurate prediction of the obstacle trajectories owing to the fast generation of the time-scaling trajectory.     

Obstacle avoidance for redundant robots using configuration control

The article presents a new and simple solution to the obstacle avoidance problem for redundant robots. In the proposed approach, called configuration control, the redundancy is utilized to configure the robot so as to satisfy a set of kinematic inequality constraints representing obstacle avoidance, while the end-effector is tracking a desired trajectory. The robot control scheme is very simple, and uses on-line adaptation to eliminate the need for the complex dynamic model and parameter values of the robot. Several simulation results for a four-link planar robot are presented to illustrate the versatility of the approach. These include reaching around a stationary obstacle, simultaneous avoidance of two obstacles, robot reconfiguration to avoid a moving obstacle, and avoidance of rectangular obstacles. The simplicity and computational efficiency of the proposed scheme allows on-line implementation with a high sampling rate for real-time obstacle avoidance in a dynamically varying environment.     

基于障碍预估与概率方向权值的移动机器人动态路径规划

主要研究了移动机器人在未知动态环境中的路径规划问题.提出一种将障碍预估与概率方向权值相结合的动态路径规划新方法.该方法将卡尔曼滤波引入到规划算法中,使得对障碍物运动状态的实时有效预估成为可能.同时,为实现移动机器人的实时路径规划,提出一种新的概率方向权值方法,基于周期规划将障碍物与目标信息进行融合,能够有效处理室内环境下对于障碍物的速度和运动轨迹均未知的动态路径规划问题.仿真结果以及基于SmartROB2移动机器人平台所进行的实验结果验证了该方法的有效性和实用性.     

Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.     

地图构建中动态障碍干扰滤除的一种方法

在移动机器人环境建图中,动态障碍物的存在直接影响传感器的读数,导致产生不一致的环境地图,因此,移动机器人构建地图必须滤除动态障碍物干扰。采用直线插补的方法在先前的局部图上搜寻机器人与目标点之间是否存在障碍物,若存在障碍,则可判定该障碍物已移走(即为动态障碍),应该予以滤除。实验结果证明,该方法能在建图过程中有效地滤除动态障碍,并能有效减少静态障碍物探测的误差累积,算法复杂度小。     

复杂环境下改进APF的机器人路径规划

针对移动机器人在有大型障碍物和运动空间相对狭窄的复杂环境中,人工势场法（APF）容易出现反复震荡、路径规划时间较长以及大型障碍物附近避障困难的问题,提出了在结合边缘探测法的APF路径规划基础上,加入自适应动态步长调整算法来克服APF的上述缺陷,实现移动机器人在复杂环境下的平滑路径规划,在确保路径近似最优的同时提高APF算法的收敛速度和路经规划的避障性能。实验结果证明了上述方法的有效性。     

Obstacle avoidance tracking control with antiswing and tracking errors constraint for underactuated automated lifting robots with load hoisting/lowering

The existing automated lifting robot technology focuses merely on motion control and ignores the surrounding environment. In practice, obstacles inevitably exist in the movement path of the automated lifting robot, which affects construction safety. Furthermore, due to the underactuated characteristics of the automated lifting robot, the load can be difficult to control when it swings violently, which undoubtedly poses huge challenges to obstacle avoidance trajectory planning and controller design. In this paper, an obstacle avoidance trajectory and its tracking controller with antiswing and tracking errors constraint are proposed. To ensure accurate load positioning and effective obstacle avoidance, the proposed control method introduces a four-segment polynomial trajectory interpolation curve to construct an obstacle avoidance trajectory based on analyzing the geometric relationship between variables. To improve the transient coupling control performance of the system, combined with the passive analysis of the automated lifting robot system, this method constructs a potential function that limits the tracking error and a coupling signal that enhances the coupling relationship between the system variables. Barbalat's lemma and Lyapunov techniques are used to analyze the stability of the system. Simulation and experimental results show that the proposed control method can significantly suppress or even eliminate load oscillation, accurately locate the load, avoid obstacles, improve the safety and efficiency of the working automated lifting robot, and have strong robustness to changes in system parameters and the addition of external disturbances.     

On-line Planning for Collision Avoidance on the Nominal Path

In this paper a solution to the obstacle avoidance problem for a mobile robot moving in the two-dimensional Cartesian plane is presented. The robot is modelled as a linear time-invariant dynamic system of finite size enclosed by a circle and the obstacles are modelled as circles travelling along rectilinear trajectories. This work deals with the avoidance problem when the obstacles move in known trajectories. The robot starts its journey on a nominal straight line path with a nominal velocity. When an obstacle is detected to be on a collision course with the robot, the robot must devise a plan to avoid the obstacle whilst minimising a cost index defined as the total sum squared of the magnitudes of the deviations of its velocity from the nominal velocity. The planning strategy adopted here is adjustment of the robot's velocity on the nominal path based on the time of collision between the robot and a moving obstacle, and determination of a desired final state such that its Euclidean distance from the nominal final state is minimal. Obstacle avoidance by deviation from the nominal path in deterministic and random environments is based on the work presented here and is investigated in another paper.     

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。     

Roadmap-based motion planning in dynamic environments

In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a goal configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the goal configuration. The approach is applicable to any robot type in configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.     

改进DWA算法的移动机器人避障研究

针对传统动态窗口法（DWA）在稠密障碍物区域存在最优路径难以选取及生成路径不平滑等问题,提出了一种改进的DWA移动机器人避障算法。基于微分流形切向量选取与障碍物不相交的机器人预轨迹,引入障碍物数量因子与方向角变化因子来改进评价函数,提高机器人在障碍物密集区域运行的安全性,使用改进后的评价函数对选取的轨迹进行评价,进而确定最优轨迹对应的速度。通过多组仿真实验对比表明：改进的DWA算法在障碍物密集区域能规划出更合理、平滑的运行路径,在保证了机器人安全性的同时还具有更好的避障效果。     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

Dynamic optimal payload path planning of mobile manipulators among moving obstacles

This paper is dealt with dynamic analysis of the wheeled mobile manipulators in the presence of moving obstacles considering optimal payload criterion. General dynamic formulation of the system was derived, and the moving obstacle avoidance strategy was proposed in terms of dynamic potential functions. The problem of dynamic motion planning and payload maximization was formulated using open-loop optimal control theory. Then, the indirect solution based on Pontryagin’s minimum principle was employed to solve the problem. Using the proposed method, complete nonlinear states and control constraints were treated without any simplifications such as linearizing the dynamics equations, discretizing the robot’s workspace, or parameterizing the solution. The proposed method will be useful for the system design and in the situation where the trajectories of obstacles are predefined. Finally, capability and applicability of the proposed method were investigated by the number of simulations on a two-link mobile manipulator.     

融合改进A*与DWA算法的移动机器人路径规划

针对移动机器人在复杂环境下实现全局路径最优、未知环境下动态实时避障这一路径规划需求,对传统A*（A-star）算法进行改进,并融合动态窗口法（DWA）实现动态实时避障。首先分析栅格环境下的障碍物占比,将障碍物占比引入传统A*算法,优化启发函数h(n),从而改进评价函数f(n),提高其在不同环境下的搜索效率;其次针对复杂栅格环境下传统A*算法优化后的轨迹与障碍物顶点相交问题,优化子节点选择方式,同时删除路径中的冗余节点,提高路径的平滑度;最后融合动态窗口法,实现复杂环境下移动机器人的动态实时避障。通过MATLAB下的对比仿真实验表明,改进算法在轨迹长度、轨迹平滑度以及历经时间上得到优化,满足全局最优且能实现动态实时避障,具有更优秀的路径规划效果。     

Planning collision-free trajectories in time-varying environments: a two-level hierarchy

We propose a two-level hierarchy for planning collision-free trajectories in time varying environments. Global geometric algorithms for trajectory planning are used in conjunction with a local avoidance strategy. Simulations have been developed for a mobile robot in the plane among stationary and moving obstacles. Essentially, the robot has a global geometric planner that provides a coarse global trajectory (the path and velocity along it), which may be locally modified by the low-level local avoidance module if local sensors detect any obstacles in the vicinity of the robot. This hierarchy makes effective use of the complementary aspects of the global trajectory planning approaches and the local obstacle avoidance approaches.