Liang Yang

A literature review of UAV 3D path planning

3D path planning of unmanned aerial vehicle (UAV) targets at finding an optimal and collision free path in a 3D cluttered environment while taking into account the geometric, physical and temporal constraints. Although a lot of works have been done to solve UAV 3D path planning problem, there lacks a comprehensive survey on this topic, let alone the recently published works that focus on this field. This paper analyses the most successful UAV 3D path planning algorithms that developed in recent years. This paper classifies the UAV 3D path planning methods into five categories, sampling-based algorithms, node-based algorithms, mathematical model based algorithms, Bio-inspired algorithms, and multi-fusion based algorithms. For each category a critical analysis and comparison is given. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering its working mechanism and time complexity.

Survey of Robot 3D Path Planning Algorithms

Robot 3D three-dimension path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints including geometric, physical, and temporal constraints. The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses.

Guiding attraction based random tree path planning under uncertainty: Dedicate for UAV

This paper proposes a path planning algorithm called guiding attraction based random tree (GART), which is built upon the famous sampling-based algorithm RRT* to generate a near optimal path in real time for unmanned aerial vehicle (UAV) navigation under uncertainty. The algorithm takes UAV heading dynamic constraint and `obstacle safe attraction into consideration, and uses a descriptive set method to describe the uncertainty caused by control and sensing error. The analysis shows that the computational complexity of GART is within a constant factor of RRT* and RRT. A number of detailed comparisons of the proposed algorithm with RRT* in 2D are given which verify the efficiency of our algorithm. Moreover, 3D simulation results demonstrate that GART find the near optimal path only after 2400 iterations, which means that GART outperformed RRT* by 833%.

GART: An environment-guided path planner for robots in crowded environments under kinodynamic constraints

The problem of three-dimensional path planning in obstacle-crowded environments is a challenge (an NP-hard problem), which becomes even more complex when considering environmental uncertainty and system control. Int this paper, we mainly focused on more challenging problem, that is, path planning in obstacle-crowded environments, and we try to find the relation between contact information and obstacle modeling. We proposed a newactive exploring sampling-based algorithm based on rapidly exploring random tree (RRT), namely, guiding attraction–based random tree (GART). GART introduces bidirectional potential field to redistribute each newly sampled state, such that the in-collision samples can be redistributed for extension. Furthermore, dynamic constraints are deployed to establish forward extending region by GART. Thus, GART can ensure kinodynamic reachability as well as smoothness. Theoretical analysis demonstrate that GART is probabilistic complete, and it obtains faster convergence rate because of its r...

Generation of dynamically feasible and collision free trajectory by applying six-order Bezier curve and local optimal reshaping

This paper considers the problem of generating dynamically feasible and collision free trajectory for unmanned aerial vehicles(UAVs) in cluttered environments. General random-based searching algorithms output piecewise linear paths, which cause big discrepancy when used as navigation reference for UAVs with high speed. Meanwhile, the disturbance may also occur to lead the UAVs into danger. In order to obtain agile autonomy without potential dangers, this paper introduces a three-step method to generate feasible reference. In the first step, a six-order Bezier curve, which uses Tuning Rotation to decrease the curvature, is introduced to smooth the output of the path planner. Then a forward simulation is implemented to find the potential dangerous regions. Finally, the path is reshaped by local optimal reshaping planner to eliminate residual dangers. The three steps form a circulation, the reshaped path sent to the first step again to check dynamic feasibility and safety. The method combining Six-order Bezier curve, Tuning Rotation, and local optimal reshaping is proposed by us for the first time, where the Tuning Rotation is able to meet various curvature requirements without violating the previous path, local optimal reshaping obtains both temporal and spatial reshaping with high time efficiency. The method addresses the system dynamics to achieve agile autonomy, which provides the geometry reference as well as the low level control. The effectiveness of the proposed method is demonstrated by the simulations.

Wall-climbing robot for non-destructive evaluation using impact-echo and metric learning SVM

The impact-echo (IE) acoustic inspection method is a non-destructive evaluation technique, which has been widely applied to detect the defects, structural deterioration level, and thickness of plate-like concrete structures. This paper presents a novel climbing robot, namely Rise-Rover, to perform automated IE signal collection from concrete structures with IE signal analyzing based on machine learning techniques. Rise-Rover is our new generation robot, and it has a novel and enhanced absorption system to support heavy load, and crawler-like suction cups to maintain high mobility performance while crossing small grooves. Moreover, the design enables a seamless transition between ground and wall. This paper applies the fast Fourier transform and wavelet transform for feature detection from collected IE signals. A distance metric learning based support vector machine approach is newly proposed to automatically classify the IE signals. With the visual-inertial odometry of the robot, the detected flaws of inspection area on the concrete plates are visualized in 2D/3D. Field tests on a concrete bridge deck demonstrate the efficiency of the proposed robot system in automatic health condition assessment for concrete structures.

UAV Path Planning Framework Under Kinodynamic Constraints in Cluttered Environments

A novel kinodynamic planning framework, which covers path panning, smoothing, tracking and emergency threat managing, is proposed. The framework is proposed based on sampling-based algorithm, which is improved to ensure dynamics feasibility as well as emergency threat management ability by applying Bezier curve and Extending Forbidden respectively. The Bezier curve guarantees both (G^{1}) and (G^{2}) continuity to decrease the tracking error of our LQI based tracking controller, where two Bezier curves with different continuity order are discussed. Extending Forbidden is firstly proposed by us to enable generating multiple paths of sampling-based algorithms, thus support on-line switching to avoid emergency threats. Our main contribution is that the proposed framework is a combination of path planning with emergency threat managing, where a time compromised moving obstacle avoiding method is proposed. Results proves the efficiency of the proposed algorithm in generating feasible trajectory for SERVOHELI-40, which not only guarantees kinematic feasible of avoiding obstacles, but also can ensure dynamics feasibility.

An Analytical Local Reshaping Algorithm

A dynamic threat and disturbance rejected path reshaping method is proposed. The method is based upon parametric Bezier curve called Local Optimal Reshaping(LOR), which is easy to adjust the reference velocity for navigation. Before implementation, only minimal safe margin and maximum curvature are needed. The method also purposefully biases the reshaping region of each node, thus, it is computational efficient with easy implementation. Three parts are included in the whole path planner, which are kinematic path planner, disturbance rejector with path smoother, and dynamic threat avoided planner, respectively. LOR acts as main effector in disturbance rejector and dynamic threat avoided planner. Comparative simulations are provided in this paper, and results show that our method has good performance in tackling disturbance and dynamic threats.