Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots

This paper presents an optimisation-based verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated: genetic algorithms (GAs) and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.     

Distributed Cooperative Path Planning for Tracking Ground Moving Target by Multiple Fixed-wing UAVs via DMPC-GVD in Urban Environment

In this paper, a real-time distributed path planning method is developed for cooperatively tracking ground moving target in urban by multiple fixed-wing unmanned aerial vehicles (UAVs). For reasons of changeable movement of target, the commanded speed and turning rate of each UAV are both taken as control input variables. In urban environment, buildings may occlude the line of sight of on-board sensor. Hence the target coverage degree is proposed as objective function instead of distance. To save energy of UAV system as much as possible, the control input cost and sensor energy consumption are also taken as objectives. For preemptive priority requirement, the objective functions are fuzzified and the satisfactory degree order is designed to model priority. To guarantee the feasibility of solution, the varying domain is introduced to replace the strict order constraint. On this basis, generalized varying domain (GVD) method is developed to balance optimization and priority. In terms of the maneuverability of UAVs, the diverse constraints are considered, including real speed and turning rate, control input saturation, collision avoidance between UAVs, and obstacle avoidance between UAV and buildings. Consequently, distributed model predictive control (DMPC) strategy is designed to calculate the optimal path of each UAV, where the state information in finite period of UAV is transferred to the adjacent ones. The simulations show the effectiveness of proposed method by comparing with hierarchical optimization (HO).

     

固定翼无人机在线航迹规划方法

在不确定环境下,针对固定翼无人机（UAV）航迹规划问题,提出了一种基于滚动时域控制的模糊粒子群优化算法与改进人工势场法相结合的在线航迹规划方法。首先,对凸多边形障碍物进行最小外接圆拟合;然后,根据静态威胁,将规划问题转化为一系列时域窗口内的在线子问题,利用模糊粒子群算法实时优化求解以实现静态避障;当环境中存在动态威胁时,使用改进人工势场法对航迹进行调整完成动态避障。为了满足固定翼无人机的动态约束,同时提出固定翼UAV的碰撞检测法,可提前判断障碍物是否为真正威胁源,以此减少转弯频率和幅度,降低飞行代价。仿真实验结果表明,所提方法在固定翼UAV航迹规划中能有效提升规划速度、稳定性与实时避障能力,且克服了传统人工势场容易陷入局部最优的缺点。     

基于双层模糊逻辑的多机器人路径规划与避碰

针对无通信情况下的多机器人系统在未知动态环境下的路径规划问题,设计了基于双层模糊逻辑的多机器人路径规划与动态避碰系统。方向模糊控制器充分考虑了障碍物的距离信息和目标的角度信息,转化为机器人与障碍物的碰撞可能性,从而输出转向角度实现机器人的动态避障;速度模糊控制器将障碍物的距离信息作为输入,将速度因子作为输出,提高了多机器人路径规划与动态避碰系统的效率和鲁棒性。在Pioneer3-DX机器人实体上验证了该系统的可行性。     

基于滚动速度障碍法的无人机山地航测避障路径规划研究

无人机在进行山地航测时,经常遭遇鸟类等动态障碍,若不能及时规避掉障碍,极容易发生坠机事故。为此,研究一种基于滚动速度障碍法的无人机山地航测避障路径规划方法。基于山地环境模型,结合飞行路径长度、路径平滑度建立一个综合目标函数并利用改进布谷鸟搜索算法求解,得到无人机山地航测的初始路径。对图像进行预处理后,识别无人机初始路径飞行过程中遇到的障碍物,并通过超声波测量无人机与障碍物之间的距离,以此建立速度障碍模型,实现速度障碍碰撞分析,通过滚动窗口的方式确定无人机与障碍物是否存在飞行冲突。基于滚动速度障碍避障方法实现滚动角度避障和速度避障,获取最终的优化路径,完成基于滚动速度障碍法的无人机山地航测避障路径规划。测试结果表明:航测避障路径长度为571.45m,平滑度为165.52,规划的方案更具合理性。     

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。     

基于柱状空间和改进A*算法的无人机规避方法

针对无人机避障问题,提出了基于改进A~*算法和柱状空间的无人机规避方法。首先,根据无人机飞行区域的障碍物分布情况,建立飞行区域的柱状空间;然后将障碍物对无人机的影响引入到估价函数中,重新设计启发函数;最后将基于柱状空间和改进A~*算法的无人机规避方法应用于无人机的规避中,并对规划的路径进行平滑处理。仿真结果表明,该算法能够有效地实现无人机的规避。     

无人机三维路径规划及避障方法

针对无人机(UAV)在三维环境中如何由起始点到目标点合理地规划路径避开障碍物,提出了一种基于改进粒子群算法与滚动策略相结合的UAV路径规划与避障方法.该方法首先以UAV为中心,通过传感器建立UAV的可视区域模型;其次结合滚动策略滚动探知UAV周围环境信息;最后,利用改进的粒子群算法进行路径搜索,并加入综合转角控制提高路径的平滑性.在传统粒子群算法中加入信息素与启发函数,增强算法的全局搜索能力,并对参数进行特定设计提高算法的收敛速度.仿真结果表明,该方法可以实现实时避障,所规划的路径相对平滑,且改进算法比传统算法具有较高的收敛性.     

基于遗传算法的机械臂实时避障路径规划研究

针对模块化机械臂在运行时可能与工作空间中的障碍物发生碰撞的问题, 提出一种基于遗传算法的避障路径规划算法。首先采用D-H(Denavit-Hartenberg)表示法对机械臂进行建模, 并进行运动学和动力学分析, 建立机械臂运动学和动力学方程。在此基础上, 利用遗传算法分别在单/多个障碍物工作环境中, 以运动的时间、移动的空间距离和轨迹长度作为优化指标, 实现机械臂避障路径规划的优化。通过仿真验证了基于遗传算法的机械臂避障路径规划算法的有效性与可行性, 该算法提高了运行中的机械臂有效避开工作空间中障碍物的效率。     

基于遗传算法和B 样条曲线的平滑避障路径规划

传统的避障路径规划中常常存在不连续点,提出一种新的平滑避障路径规划的方法—遗传算法和B样条曲线法。首先,先通过碰撞侦测,能够侦测出前进路径中的障碍物,然后通过遗传算法再结合B样条曲线规划出平滑的避障路径。该算法可以避免运动过程因打滑而造成与目标位置的误差,解决机器人在静态环境中全局、局部路径规划和规划路径中存在不连续点的问题。仿真和实验结果证明了算法的有效性。     

A path planning algorithm for industrial robots

Instead of using the tedious process of robot teaching, an off-line path planning algorithm has been developed for industrial robots to improve their accuracy and efficiency. Collision avoidance is the primary concept to achieve such goal. By use of the distance maps, the inspection of obstacle collision is completed and transformed to the configuration space in terms of the robot joint angles. On this configuration map, the relation between the obstacles and the robot arms is obvious. By checking the interference conditions, the collision points are indicated with marks and collected into the database. The path planning is obtained based on the assigned marked number of the passable region via wave expansion method. Depth-first search method is another approach to obtain minimum sequences to pass through. The proposed algorithm is experimented on a 6-DOF industrial robot. From the simulation results, not only the algorithm can achieve the goal of collision avoidance, but also save the manipulation steps.     

Three-Dimensional Path Planning of a Climbing Robot Using Mixed Integer Linear Programming

The City-Climber robot is a novel wall-climbing robot developed at The City College of New York that has the capability to move on floors, climb walls, walk on ceilings and transit between them. In this paper, we first develop the dynamic model of the City-Climber robot when it travel on different surfaces, i.e., floors, walls and ceilings, respectively. Then, we present a path planning method for the City-Climber robot using mixed integer linear programming (MILP) in three-dimensional (3-D) building environments that consist of objects with primitive geometrical shapes. MILP provides an optimization framework that can directly incorporate dynamic constraints with logical constraints such as obstacle avoidance and waypoint selection. In order to use MILP to solve the obstacle avoidance problem, we simplify and decouple the robot dynamic model into a linear system by introducing a restricting admissible controller. The decoupled model and obstacle can be rewritten as a linear program with mixed-integer linear constraints that account for the collision avoidance. A key benefit of this approach is that the path optimization can be readily solved using the AMPL and CPLEX optimization software with a MATLAB interface. Simulation results show that the framework of MILP is well suited for path planning and obstacle avoidance problems for the wall-climbing robot in 3-D environments.     

基于贝叶斯决策的无人机飞行路径自动规划方法

传统无人机飞行路径自动规划方法无法获取全部障碍物信号,使无人机飞行不能达到避障效果,导致飞行路线规划效果较差;为此提出基于贝叶斯决策的无人机飞行路径自动规划方法;无人机飞行路径自动规划硬件模块包含自动规划模块、动画演示模块、地图导航模块和数据导出模块,自动规划模块负责控制无人机飞行;动画演示模块使用240PRO型号的LEWITT声卡,为展示飞机飞行路线提供声音;LS-TM8N地图导航模块通过串口将射频信号发送到天线的输入端,再由数据导出模块导出并保存相关数据;基于贝叶斯决策原理,结合贝叶斯元胞蚁群算法,计算贝叶斯先验概率和后验概率,规划无人机飞行路径,获取最优路径;实验结果表明,该方法遇到静态障碍物捕获的避障信号在-28～30mV范围内波动,动态障碍物捕获的避障信号在-27～30 mV范围内波动,与实际障碍物信号波动范围一致,避障效果较优.     

基于改进A*算法的无人机三维空间避障路径规划

无人机在有障碍物的三维空间环境中飞行,采用常规A*算法进行避障航线的规划存在搜索节点多、搜索区域大、搜索时间长、搜索效率低、生成的航线拐角多且含有大量非必要冗余航点、没有考虑无人机自身体积与尺寸而引发的飞行中与障碍物边界碰撞的航线不安全等问题。因此，设计一种改进A*算法,首先,考虑无人机本身体积与尺寸,提出一种消除边界碰撞事故的子节点扩展方法；其次,改进评价函数,减少往复搜索次数,缩小搜索区域面积,提高搜索效率；然后,根据Floyd思想,对生成的航线进行简化处理,消除航线中的冗余航路点,减少航线转角数量,达到简化航线并改善航线平滑度的效果；最后,非线性仿真及飞行试验表明了改进的A*算法生成的航线更加安全、高效,并使无人机的飞行连续和顺畅。     

Two-frame structure from motion using optical flow probability distributions for unmanned air vehicle obstacle avoidance

See-and-avoid behaviors are an essential part of autonomous navigation for Unmanned Air Vehicles (UAVs). To be fully autonomous, a UAV must be able to navigate complex urban and near-earth environments and detect and avoid imminent collisions. While there have been significant research efforts in robotic navigation and obstacle avoidance during the past few years, this previous work has not focused on applications that use small autonomous UAVs. Specific UAV requirements such as non-invasive sensing, light payload, low image quality, high processing speed, long range detection, and low power consumption, etc., must be met in order to fully use this new technology. This paper presents single camera collision detection and avoidance algorithm. Whereas most algorithms attempt to extract the 3D information from a single optical flow value at each feature point, we propose to calculate a set of likely optical flow values and their associated probabilities—an optical flow probability distribution. Using this probability distribution, a more robust method for calculating object distance is developed. This method is developed for use on a UAV to detect obstacles, but it can be used on any vehicle where obstacle detection is needed.     

A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

This article presents a genetic algorithm approach to multi-criteria motion planning of mobile manipulator systems. For mobile robot path planning, traveling distance and path safety are considered. The workspace of a mobile robot is represented as a grid by cell decomposition, and a wave front expansion algorithm is used to build the numerical potential fields for both the goal and the obstacles. For multi-criteria position and configuration optimization of a mobile manipulator, least torque norm, manipulability, torque distribution and obstacle avoidance are considered. The emphasis of the study is placed on using genetic algorithms to search for global optimal solutions and solve the minimax problem for manipulator torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than the conventional methods.     

基于双粒子群算法的矿井搜救机器人路径规划

针对在复杂地形中标准的粒子群算法用于矿井搜救机器人路径规划存在迭代速度慢和求解精度低的问题,提出了一种基于双粒子群算法的矿井搜救机器人路径规划方法。首先将障碍物膨胀化处理为规则化多边形,以此建立环境模型,再以改进双粒子群算法作为路径寻优算法,当传感器检测到搜救机器人正前方一定距离内有障碍物时,开始运行双改进粒子群算法:改进学习因子的粒子群算法(CPSO)粒子步长大,适用于相对开阔地带寻找路径,而添加动态速度权重的粒子群算法(PPSO)粒子步长小,擅长在障碍物形状复杂多变地带寻找路径;然后评估2种粒子群算法得到的路径是否符合避障条件,若均符合避障条件,则选取最短路径作为最终路径;最后得到矿井搜救机器人在整个路况模型中的最优行驶路径。仿真结果表明,通过改进学习因子和添加动态速度权重提高了粒子群算法的收敛速度,降低了最优解波动幅度,改进的双粒子群算法能够与路径规划模型有效结合,在复杂路段能够寻找到最优路径,提高了路径规划成功率,缩短了路径长度。     

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.     

基于速度修正项的机械臂避障路径规划

针对机械臂运行过程中存在的碰撞问题, 提出一种基于速度修正项的机械臂避障路径规划方法. 利用B 样条曲线进行机械臂关节空间规划, 使机械臂能够在特定时刻运行到指定构型. 在运行过程中, 利用碰撞检测算法实时计算机械臂与障碍物的最小距离, 在碰撞即将发生时引入积分为零的避障速度修正项改变机械臂运行轨迹, 使得机械臂能够在实现障碍回避的同时, 保证其在特定时刻通过指定构型的要求. 仿真实验表明了所提出方法的正确性和有效性.     

基于改进A*算法的无人机避障路径规划

近年来,物流行业的飞速发展,运输是物流的重要环节之一,根据数据显示,运输的成本占据整个物流成本的50%以上.无人机的使用有效的控制了运输成本,合理规划物流无人机的飞行路线,也起着至关重要的作用.在物流无人机的航迹规划中,必须保证无人机飞行过程中能够准确避开禁飞区.本文基于A^*算法,结合多种类型的禁飞区,设计出一种改进算法,能够找到任意两客户点间无人机避障飞行的最优路线.仿真结果表明,本文所设计的算法能够有效解决多类型禁飞区并存的无人机避障路径规划问题.