CBUG: A Quadratically Competitive Mobile Robot Navigation Algorithm

This paper is concerned with online navigation of a size $D$ mobile robot in an unknown planar environment. A formal means for assessing algorithms for online tasks is competitiveness. For the navigation task, competitiveness measures the algorithm's path length relative to the optimal offline path length. While competitiveness usually means constant relative performance, it is measured in this paper in terms of a quadratic relationship between online performance and optimal offline solution. An online navigation algorithm for a size D robot called CBUG is described. The competitiveness of CBUG is analyzed and shown to be quadratic in the length of the shortest offline path. Moreover, it is shown that, in general, quadratic competitiveness is the best achievable performance over all online navigation algorithms. Thus, up to constants, CBUG achieves optimal competitiveness. The algorithm is improved with some practical speedups, and the usefulness of its competitiveness in terms of path stability is illustrated in office-like environments.      

Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment

Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.     

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance.

     

Generation of Bezier Curve-Based Flyable Trajectories for Multi-UAV Systems with Parallel Genetic Algorithm

In recent years, Unmanned Aerial Vehicles (UAVs) have been used in many military and civil application areas, due to their increased endurance, performance, portability, and their larger payload-carrying, computing and communication capabilities. Because of UAVs’ complex operation areas and complicated constraints related to the assigned task, they have to fly on a path, which is calculated online and/or offline to satisfy these constraints and to check some control points in the operation theatre. If the number of control points and constraints increases, finding a feasible solution takes up too much time in this large operation area. In this case, the use of multi-UAVs decreases operation completion time; however, this usage increases the complexity of finding a feasible path problem. This problem is typically NP-hard and genetic algorithms have been successfully utilized for solving it in the last few decades. This paper presents how a flyable trajectory can be constructed for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment and at a constant altitude. A GA is implemented parallel in a multi-core environment to increase the performance of the system. First, a feasible path is calculated by using a parallel GA, and then the path is smoothed by using Bezier curves to convert it flyable. Preliminary results show that the proposed method provides an effective and feasible path for each UAV in an Unmanned Aerial System with multi-UAVs. The proposed system is realized in Java with a GUI for showing results. This paper also outlines future work that can be conducted on the multi-UAV path planning.     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

Imperialist Competitive Algorithm for AUV Path Planning in a Variable Ocean

An imperialist competitive algorithm (ICA) is introduced for solving the optimal path planning problem for autonomous underwater vehicles (AUVs) operating in turbulent, cluttered, and uncertain environments. ICA is a new sociopolitically inspired global search metaheuristic based on a form of competition between “imperialist” forces and opposing colonies. In this study, ICA is applied to optimize the coordinates of a set of control points for generating a curved spline path. The ICA-based path planner is tested to find an optimal trajectory for an AUV navigating through a variable ocean environment in the presence of an irregularly shaped underwater terrain. The genetic algorithm (GA) and quantum-behaved particle swarm optimization (QPSO) are described and evaluated with the ICA for the path optimization problem. Simulation results show that the proposed ICA approach is able to obtain a more optimized trajectory than the GA- or QPSO-based methods. Monte Carlo simulations demonstrate the robustness and superiority of the proposed ICA scheme compared with the GA and QPSO schemes.     

基于曲率优化的机器人砂带磨削轨迹规划

针对复杂曲面零件砂带磨削编程效率低、精度差的问题,基于B样条曲线曲面重构和机器人离线编程技术,提出了一种根据关键接触点曲率值生成工业机器人磨削轨迹的方法.首先,利用零件表面上需要进行砂带磨削的关键接触点和积累弦长参数化法构造节点矢量,从而计算出磨削轨迹的B样条基函数;其次,根据控制顶点反求矩阵得到全部未知控制点和3次B样条加工曲线;然后,分析关键接触点之间的曲率变化率和弧长,对关键点细化生成符合磨削工艺要求的目标点;最后,通过求解双3次B样条插值曲面方程获得目标点的加工姿态.以水龙头磨削为例进行试验,结果表明曲率优化算法磨削的零件表面轮廓形状明显优于截面法,且其粗糙度值能稳定在0.082 μm左右,可以有效提高工件表面加工质量.     

Autonomous Navigation of UAV in Foliage Environment

This paper presents a navigation system that enables small-scale unmanned aerial vehicles to navigate autonomously using a 2D laser range finder in foliage environment without GPS. The navigation framework consists of real-time dual layer control, navigation state estimation and online path planning. In particular, the inner loop of a quadrotor is stabilized using a commercial autopilot while the outer loop control is implemented using robust perfect tracking. The navigation state estimation consists of real-time onboard motion estimation and trajectory smoothing using the GraphSLAM technique. The onboard real-time motion estimation is achieved by a Kalman filter, fusing the planar velocity measurement from matching the consecutive scans of a laser range finder and the acceleration measurement of an inertial measurement unit. The trajectory histories from the real-time autonomous navigation together with the observed features are fed into a sliding-window based pose-graph optimization framework. The online path planning module finds an obstacle-free trajectory based the local measurement of the laser range finder. The performance of the proposed navigation system is demonstrated successfully on the autonomous navigation of a small-scale UAV in foliage environment.     

A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV

A new framework which adopts a rapidly-exploring random tree (RRT) path planner with a Gaussian process (GP) occupancy map is developed for the navigation and exploration of an unknown but cluttered environment. The GP map outputs the probability of occupancy given any selected query point in the continuous space and thus makes it possible to explore the full space when used in conjunction with a continuous path planner. Furthermore, the GP map-generated path is embedded with the probability of collision along the path which lends itself to obstacle avoidance. Finally, the GP map-building algorithm is extended to include an exploration mission considering the differential constraints of a rotary unmanned aerial vehicle and the limitation arising from the environment. Using mutual information as an information-theoretic measure, an informative path which reduces the uncertainty of the environment is generated. Simulation results show that GP map combined with RRT planner can achieve the 3D navigation and exploration task successfully in unknown and complex environments.     

帝国竞争算法在无人机三维航线规划中的应用研究

针对实际飞行环境中无人机的三维航线规划问题,提出了一种创新启发式优化算法——牛顿帝国主义竞争算法(NICA,Newtonian imperialist competitive algorithm).该算法能够根据无人机的飞行轨迹,从起始位置到任务目标位置生成平滑的航线路径,约束航线规划,使得目标完成任务的时间最小化.该算法也能为无人机在真实地形上的航线提供最佳轨迹路径.最后通过与ICA、GA和PSO算法进行比较,验证了改进算法的有效性.结果表明:改进帝国算法提高了全局最优解的搜索能力,在收敛速度和精度上优于其他3种算法,适合用来解决无人机的三维航线规划问题.     

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.     

Mobile robot path planning and tracking using simulated annealing and fuzzy logic control

This article describes the development and implementation of an automatic controller for path planning and navigation of an autonomous mobile robot using simulated annealing and fuzzy logic. The simulated annealing algorithm was used to obtain a collision-free optimal trajectory among fixed polygonal obstacles. C-space was used to represent the working space and B-spline curves were used to represent the trajectories. The trajectory tracking was performed with a fuzzy logic algorithm. A detailed explanation of the algorithm is given. The objectives of the control algorithm were to track the planned trajectory and to avoid collision with moving obstacles. Simulation and implementation results are shown. A Nomadic 200 mobile robot was used to perform the experiments.     

Adaptive evolutionary planner/navigator for mobile robots

Based on evolutionary computation (EC) concepts, we developed an adaptive evolutionary planner/navigator (EP/N) as a novel approach to path planning and navigation. The EP/N is characterized by generality, flexibility, and adaptability. It unifies off-line planning and online planning/navigation processes in the same evolutionary algorithm which 1) accommodates different optimization criteria and changes in these criteria, 2) incorporates various types of problem-specific domain knowledge, and 3) enables good tradeoffs among near-optimality of paths, high planning efficiency, and effective handling of unknown obstacles. More importantly, the EP/N can self-tune its performance for different task environments and changes in such environments, mostly through adapting probabilities of its operators and adjusting paths constantly, even during a robot's motion toward the goal     

Mita Laboratory,Electric Engineering Section,Faculty of Engineering,The Chiba University

This paper describes a local path planning method for a mobile robot to search for a path in an unknown environment by using visual information. The mobile robot system has a hierarchical path planning system which searches for a path efficiently in an uncertain environment. The planning system consists of a global planner and a local planner. The global planner gives a global path in terms of a sequence of visual sub-goals. Then the local planner generates a local path between the sub-goals with the help of a visual sensor. The main focus of this paper is on local path planning, which provides real-time guidance to the system. A visual sensor can provide useful information about the environment. So, an algorithm is proposed to generate avoiding points by using visual information to bypass unknown obstacles in the local path planning. Local path planning in a simple environment is simulated by using three-dimensional graphics. A simple experiment is also done for the case where there are two obstacles. The validity of the proposed method is verified by these simulations and experimental results.     

Conflict-free navigation in unknown urban environments

This paper presents an autonomous exploration method in an unknown environment that uses model predictive control (MPC)-based obstacle avoidance with local map building by onboard sensing. An onboard laser scanner is used to build an online map of obstacles around the vehicle with outstanding accuracy. This local map is combined with a real-time MPC algorithm that generates a safe vehicle path, using a cost function that penalizes the proximity to the nearest obstacle. The adjusted trajectory is then sent to a position tracking layer in the hierarchical unmanned aerial vehicle (UAV) avionics architecture. In a series of experiments using a Berkeley UAV, the proposed approach successfully guided the vehicle safely through the urban canyon.     

UAV Online Path Planning Algorithm in a Low Altitude Dangerous Environment

UAV online path-planning in a low altitude dangerous environment with dense obstacles, static threats (STs) and dynamic threats (DTs), is a complicated, dynamic, uncertain and real-time problem. We propose a novel method to solve the problem to get a feasible and safe path. Firstly STs are modeled based on intuitionistic fuzzy set (IFS) to express the uncertainties in STs. The methods for ST assessment and synthesizing are presented. A reachability set (RS) estimator of DT is developed based on rapidly-exploring random tree (RRT) to predict the threat of DT. Secondly a subgoal selector is proposed and integrated into the planning system to decrease the cost of planning, accelerate the path searching and reduce threats on a path. Receding horizon (RH) is introduced to solve the online path planning problem in a dynamic and partially unknown environment. A local path planner is constructed by improving dynamic domain rapidly-exploring random tree (DDRRT) to deal with complex obstacles. RRT* is embedded into the planner to optimize paths. The results of Monte Carlo simulation comparing the traditional methods prove that our algorithm behaves well on online path planning with high successful penetration probability.      

Vision-based navigation of unmanned aerial vehicles

This paper presents a vision-based navigation strategy for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using a single embedded camera observing natural landmarks. In the proposed approach, images of the environment are first sampled, stored and organized as a set of ordered key images (visual path) which provides a visual memory of the environment. The robot navigation task is then defined as a concatenation of visual path subsets (called visual route) linking the current observed image and a target image belonging to the visual memory. The UAV is controlled to reach each image of the visual route using a vision-based control law adapted to its dynamic model and without explicitly planning any trajectory. This framework is largely substantiated by experiments with an X4-flyer equipped with a fisheye camera.     

结合B样条优化的UAV多区域路径规划融合算法

为解决无人机(UAV,unmanned aerial vehicle)在多个目标区域之间快速找到最佳遍历路径的类旅行商问题(TSP,travelling salesman problem),设计一种基于蚁群算法、A*算法以及三次B样条优化的融合规划算法;尽管蚁群算法相对其他优化算法在解决TSP问题上有较为良好的表现,但其规划路径处理时间长、生成路径转折多、路径质量和安全性较差;算法首先改进传统A*算法的节点扩展方式,快速生成两两目标区之间的局部路径,然后将蚁群算法和改进A*算法融合使用进行全局路径规划,最后结合改进三次B样条对路径进行平滑处理;基于栅格地图的仿真结果证明了该算法相比传统算法具有更好的高效性和稳定性。     

A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation.

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.     

An Efficient Path Planning and Control Algorithm for RUAV’s in Unknown and Cluttered Environments

This paper presents an efficient planning and execution algorithm for the navigation of an autonomous rotary wing UAV (RUAV) manoeuvering in an unknown and cluttered environment. A Rapidly-exploring Random Tree (RRT) variant is used for the generation of a collision free path and linear Model Predictive Control(MPC) is applied to follow this path. The guidance errors are mapped to the states of the linear MPC structure by using the nonlinear kinematic equations. The proposed path planning algorithm considers the run time of the planning stage explicitly and generates a continuous curvature path whenever replanning occurs. Simulation results show that the RUAV with the proposed methodology successfully achieves autonomous navigation regardless of its lack of prior information about the environment.