Vision-based Three Dimensional Navigation Frame Mapping and Planning for Collision Avoidance for Micro Air Vehicles

Path planning and obstacle avoidance for Micro Air Vehicles (MAVs) involve planning a feasible path from an initial state to a goal state while avoiding obstacles in the environment. This paper presents a vision-based navigation frame multi-resolution mapping and path planning technique for MAVs using a forward-looking onboard camera. A depth map representing time to collision and bearing to obstacles is obtained by computer vision algorithms. To account for measurement uncertainties introduced by the camera, a multiresolution map in the navigation frame of the MAV is created using an occupancy grid. The map is represented by log-polar mapping that is more compatible with time to collision estimates and also reflects the pixel quantization uncertainty of vision. The log polar representation gives finer resolution to areas close to the MAV and coarser resolution to areas far from the MAV. We employ the RRT algorithm to find a collision-free path in the navigation frame. The experimental results show that the proposed technique is successful in solving path planning and multiple obstacles avoidance problems for MAVs.

In this paper a novel combination of data structures and algorithms is presented as a fast and efcient practical approach to path planning for unmanned vehicles. This approach utilizes an architecture that integrates effective obstacle representation using an efcient Quadtree data structure, with path planning based on a modied Rapidly-exploring Random Tree (RRT) algorithm, and a path pruning technique based on Dijkstra’s algorithm, resulting in rapid discovery of sub-optimal feasible paths. One of the computationally expensive steps of the RRT algorithm is checking RRT segments for collision with obstacles. To address this problem, an efcient obstacle representation using Quadtrees is implemented enabling fast segment checks. While the RRT approach is guaranteed to nd a feasible path through complex obstacle elds with the probability of discovery approaching one as the number of iterations increases, the resulting paths may be far from optimal as the only criterion embedded in the RRT search is feasibility. For this reason, the paths generated by the RRT algorithm are passed through Dijkstra algorithm that nds a shortest obstacle-free path among the points on the path. Dijkstra algorithm that re-samples the segments of the path and nds the shortest path among the new points is implemented resulting in paths that are very close to the optimal ones. To speed up the computation, the basic search algorithm is implemented as a dual RRT from the initial and nal points. To account for motion transients along the path, a suitable size corridor is introduced, assuring that the vehicle has enough room for maneuvering among the obstacles. This computationally efcient architecture lends itself to real-time implementation for both pre-planning with known obstacles as well as rapid reactive re-planning in the presence of new pop-up obstacles. The proposed technique is evaluated using several challenging path-planning examples characterized by a large number of non-convex constraints arising in cluttered obstacle elds with known and unknown obstacles. A modication that allows an easy way to introduce vehicle dynamics constraints or actuation failures is also implemented. Algorithms have been implemented for two as well as three dimensional path planning applications.

The online cooperative path planning problem is discussed for multi-quadrotor maneuvering in an unknown dynamic environment. Based on the related basic concepts, typical three-dimensional obstacle models, such as spherical and cubic, and their collision checking criteria are presented in this article. An improved rapidly exploring random tree (RRT) algorithm with goal bias and greed property is proposed based on the heuristic search strategy to overcome the shortcomings of the classical RRT algorithm. Not only are the kinematic constraints of the quadrotor established but the time and space coordination strategy matching with the improved RRT algorithm is also presented in this article. Furthermore, a novel online collision avoidance strategy according to the partial information of the surrounding environment is proposed. On the basis of the above work, a distributed online path planning strategy is proposed to obtain the feasible path for each quadrotor. Numerical simulation results show that the improved RRT algorithm has better search efficiency than the classical RRT algorithm. And the satisfactory path planning and path tracking results prove that the above model and related planning strategies are reasonable and effective.

An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.

Double manipulator path planning is the basis for overhead line live working robot to complete the task. The previous dual manipulator motion path planning method has some problems, such as low success rate, low convergence speed, unstable guidance, and easy to lead to path discontinuity in complex environment. Therefore, this paper proposes a decoupling motion planning method for two manipulators of live working robot based on hybrid RRT (Rapidly-Exploring Random Trees) algorithm. The hybrid RRT algorithm combines target offset RRT algorithm, bidirectional RRT algorithm and dynamic expansion RRT algorithm. MATLAB robot toolbox is used to simulate the motion path planning of 6-DOF double manipulator of overhead line live working robot. The results show that the method proposed in this paper can realize the loose coupling and efficient collision free motion planning of double manipulator of live working robot.

Sampling-based search algorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT*) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT*algorithm directly samples a point (node) from the LIDAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT*. This process improves conventional RRT*in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm’s convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm’s enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

PurposeThe purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.Design/methodology/approachAnother RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.FindingsIt is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.Originality/valueThe paper presents a new nested RRT algorithm for fault tolerant motion planning.

MAVs (micro air vehicles) with a maximal dimension of 15 cm and nominal flight speeds of around 10 m s−1, operate in a Reynolds number regime of 105 or lower, in which most natural flyers including insects, bats and birds fly. Furthermore, due to their light weight and low flight speed, the MAVs' flight characteristics are substantially affected by environmental factors such as wind gust. Like natural flyers, the wing structures of MAVs are often flexible and tend to deform during flight. Consequently, the aero/fluid and structural dynamics of these flyers are closely linked to each other, making the entire flight vehicle difficult to analyze. We have recently developed a hummingbird-inspired, flapping flexible wing MAV with a weight of 2.4–3.0 g and a wingspan of 10–12 cm. In this study, we carry out an integrated study of the flexible wing aerodynamics of this flapping MAV by combining an in-house computational fluid dynamic (CFD) method and wind tunnel experiments. A CFD model that has a realistic wing planform and can mimic realistic flexible wing kinematics is established, which provides a quantitative prediction of unsteady aerodynamics of the four-winged MAV in terms of vortex and wake structures and their relationship with aerodynamic force generation. Wind tunnel experiments further confirm the effectiveness of the clap and fling mechanism employed in this bio-inspired MAV as well as the importance of the wing flexibility in designing small flapping-wing MAVs.

Micro Air Vehicles (MAVs) have become more attractive for various missions including surveillance or reconnaissance in recent years. MAVs should be capable of maintaining their attitudes through either inherent passive stability or active feedback in order to successfully perform their directives. Stability and Controllability Augmentation Systems (SCASs) are usually employed to enhance the flight performance of conventional aircrafts and Unmanned Aerial Vehicles (UAVs). However, it is no simple task to obtain an accurate numerical model for the flight dynamics of a MAV. An alternative approach for SCASs would be to incorporate reinforcement learning in order to address this numerical complexity. Such implementation has already been successful in other vehicles, such as unmanned ground vehicles (UGVs), because of their bettered stability compared to aerial vehicles. However, in order to train MAVs to learn how to fly, they must first be airborne. Similar to teaching infants how to walk, this paper presents a new method to provide an effective environment where a MAV can learn how to fly. A test setup was constructed to enable magnetic levitation of a MAV embedded with a permanent magnet. This apparatus allows for flexible experimentation: the position and the altitude of the MAV, the constraint forces, and the resulting moments are all adjustable and fixable. This ‘Pseudo Flight Environment’ was demonstrated with a fixed wing MAV model. In order for the model to maintain a constant altitude, a height hold control system was devised and implemented.

The current path planning algorithms such as A-star(all stars) algorithm and RRT (Rapidly-exploring Random Trees) algorithm can meet the obstacle avoidance planning of the 6-DOF robot, but the smoothness of the path is not considered. Working in an unreasonable path for a long time will produce a great load on the joints of the 6-DOF robot and seriously affect its life. In this paper, we use reinforcement learning reconcile A-star algorithm and RRT algorithm for smooth path planning of the robot. Experimental results show that compared with A-star algorithm and RRT algorithm, the fusion algorithm has smoother path and more reasonable time.

Aiming at the problems of long planning time and path feasibility existing in genetic algorithm (GA) for path planning, an improved method integrating GA with Rapidly-exploring Random Tree (RRT) algorithm is proposed. By generating initial path populations using the RRT algorithm, eliminating redundant paths with a deletion operator, and introducing an elite selection strategy to optimize the selection operation, the continuity of paths and the convergence speed of the algorithm are effectively improved. Additionally, a path point backtracking method is utilized to further optimize path length and turning points. Experimental results show that the improved algorithm outperforms the traditional <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A^{*}$</tex> algorithm and other improved GA in terms of path length, number of inflection points, and iteration times. The path length is shortened by an average of 8%, and the iteration times are reduced to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$53 \%-85 \%$</tex> of those of the comparative algorithms, verifying its high efficiency and robustness in complex environments.

Missions envisioned for Micro Air Vehicles (MAVs) such as Intelligence, Surveillance and Reconnaissance (ISR) in urban environments require exceptional agility. These missions typically involve ight through or in close proximity to buildings, tunnels and trees, and require the MAVs to maneuver using sharp dives and climbs along with small-radius turns. The small size of MAVs allows such ight operations within con ned space but the control e ectors must provide su cient maneuverability, while maintaining stability, with only limited sensing capability onboard the platform. To meet these challenges, researchers have long been attracted by the amazing attributes of biological systems, such as those exhibited by birds and insects. Birds can y in dense ocks, executing rapid maneuvers with g-loads far in excess of modern ghter aircrafts, and yet never collide with each other, despite the absence of air tra c controllers. This paper introduces a novel framework for the design and control of a Micro Air Vehicle. The vehicle's conceptual design is based on biologically-inspired principles and emulates a dragon y (Odonata-Anisoptera). The author is borrowing ight mechanism features from the dragon y and merging them with indigenous designs in creating a novel version of a Flapping Wing MAV, called the QV. The QV design incorporates the simplicity of mechanical construction and the complexity of a sophisticated multi-layered Hybrid & Linear / Non-Linear controller to achieve extended ight times and improved agility compared to other Rotary and Flapping Wing MAV designs. The paper talks about the design and control features of the proposed QV design and gives an overview on the developmental e orts been made towards the prototyping of the QV yer. The potential applications for such a high endurance vehicle are numerous, including air-deployable mass surveillance in cluster and swarm formations. The disposability of the vehicle would help in battleeld deployment as well, where such a MAV would be made available to soldiers for proximity sensing and threat level assessment. Other applications would include search and rescue operations and civilian law-enforcement.

Aiming at the problems of strong randomness, unsmooth path and local optimum in the path planning of the Rapidly-exploring Random Trees (RRT) algorithm. The paper designs an improved RRT algorithm with variable step size and goal orientation (VO-RRT), which introduces variable step size, oriented expansion node, optimized initial path and smoothed path four parts. Through several experiments in 2D and 3D space to prove that the VO-RRT algorithm has enhanced obstacle avoidance ability and improved path smoothness in different obstacle spaces. The path nodes generated by the VO-RRT algorithm in 3D space are tested in URsim simulation software and manipulator body to verify the executability of the optimized path.

Many path planning algorithms have been introduced so far, but most are costly, in path cost and in processing time, in large-scale uncluttered 3D environments such as underground mining stopes explored by an unmanned aerial vehicle (UAV). Rapidly-exploring Random Tree (RRT) algorithms are popular because of their probabilistic completeness and rapidity in finding a feasible path in single-query problems. Many of the algorithms (e.g. Informed RRT*, RRT#) developed to improve RRT need considerable time to converge in large environments. Shortcutting an RRT is an old idea that has been proven to outperform RRT variants. This paper introduces a new method, RRT-Rope, that aims at finding a near-optimal solution in a drastically shorter amount of time. The proposed approach benefits from fast computation of a feasible path with an altered version of RRT-connect, and post-processes it quickly with a deterministic shortcutting technique, taking advantage of intermediate nodes added to each branch of the tree. This paper presents simulations and statistics carried out to show the efficiency of RRT-Rope, which gives better results in terms of path cost and computation time than other popular RRT variations and shortening techniques in all our simulation environments, and is up to 70% faster than the next best algorithm in a representative stope.