Three-dimensional Obstacle Avoidance Research Articles

Three-Dimensional Obstacle Avoidance Harvesting Path Planning Method for Apple-Harvesting Robot Based on Improved Ant Colony Algorithm

The cultivation model for spindle-shaped apple trees is widely used in modern standard apple orchards worldwide and represents the direction of modern apple industry development. However, without an effective obstacle avoidance path, the robotic arm is prone to collision with obstacles such as fruit tree branches during the picking process, which may damage fruits and branches and even affect the healthy growth of fruit trees. To address the above issues, a three-dimensional path -planning algorithm for full-field fruit obstacle avoidance harvesting for spindle-shaped fruit trees, which are widely planted in modern apple orchards, is proposed in this study. Firstly, based on three typical tree structures of spindle-shaped apple trees (free spindle, high spindle, and slender spindle), a three-dimensional spatial model of fruit tree branches was established. Secondly, based on the grid environment representation method, an obstacle map of the apple tree model was established. Then, the initial pheromones were improved by non-uniform distribution on the basis of the original ant colony algorithm. Furthermore, the updating rules of pheromones were improved, and a biomimetic optimization mechanism was integrated with the beetle antenna algorithm to improve the speed and stability of path searching. Finally, the planned path was smoothed using a cubic B-spline curve to make the path smoother and avoid unnecessary pauses or turns during the harvesting process of the robotic arm. Based on the proposed improved ACO algorithm (ant colony optimization algorithm), obstacle avoidance 3D path planning simulation experiments were conducted for three types of spindle-shaped apple trees. The results showed that the success rates of obstacle avoidance path planning were higher than 96%, 86%, and 92% for free-spindle-shaped, high-spindle-shaped, and slender-spindle-shaped trees, respectively. Compared with traditional ant colony algorithms, the average planning time was decreased by 49.38%, 46.33%, and 51.03%, respectively. The proposed improved algorithm can effectively achieve three-dimensional path planning for obstacle avoidance picking, thereby providing technical support for the development of intelligent apple picking robots.

An Obstacle Avoidance Trajectory Planning Methodology Based on Energy Minimization (OTPEM) for the Tilt-Wing eVTOL in the Takeoff Phase

Electric tilt-wing flying cars are an efficient, economical, and environmentally friendly solution to urban traffic congestion and travel efficiency issues. This article addresses the high energy consumption and obstacle interference during the takeoff phase of the tilt-wing eVTOL (electric Vertical Takeoff and Landing), proposing a trajectory planning method based on energy minimization and obstacle avoidance. Firstly, based on the dynamics analysis, the relationship between energy consumption, spatial trajectory, and obstacles is sorted out and the decision variables for the trajectory planning problem with obstacle avoidance are determined. Secondly, based on the power discretization during the takeoff phase, the energy minimization objective function is established and the constraints of performance limitations and spatial obstacles are derived. Thirdly, by integrating the optimization model with the SLSQP (Sequential Least Squares Quadratic Programming algorithm), the second-order sequential quadratic programming model and decision variable update equations are derived, establishing the solution process for the trajectory planning problem of the tilt-wing eVTOL takeoff with obstacle avoidance. Finally, the Airbus Vahana A3 is taken as an example to verify and validate the effectiveness, stability, and robustness of the model and optimization algorithm proposed. The validation results show that the OTPEM (obstacle avoidance trajectory planning methodology based on energy minimization) can effectively handle changes in the takeoff end state and exhibits good stability and robustness in different obstacle environments. It can provide a certain reference for the three-dimensional obstacle avoidance trajectory planning of Airbus Vahana A3 and other tilt-wing eVTOL trajectory planning problems.

Three-Dimensional Obstacle Avoidance and Path Planning for Unmanned Underwater Vehicles Using Elastic Bands

Three-Dimensional Obstacle Avoidance and Path Planning for Unmanned Underwater Vehicles Using Elastic Bands

3D PathPlanning of RobotFishBased on ImprovedAntColonyAlgorithm

For the following problems:three-dimensional obstacle avoidance path selection when using robot fish to find targets in a complex coral reef geographical environment, an improved ant colony algorithm that can be applied in an environment with multiple complexities is proposed. Combined with the underwater terrain, several maps with different obstacle complexities are established with the three-dimensional environment modeling scheme. Aiming at easy stagnation, slow search speed, and easiness to produce local optimization in the conventional ant colony algorithm, this paper improved the function design and helped the robot fish carry out the early search based on three pieces of heuristic information (the global distance, traffic ability cost, and path consumption cost). Then the path is sorted according to the path length to adjust the pheromone update mode. At the same time, an elite ant system is constructed to adjust the pheromone value of elite ants separately. The simulation results show that convergence speed and searchabilityof the algorithm have been improved to some extent, which proves feasibility of the algorithm.

Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method

This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.

Three-Dimensional Obstacle Avoidance for Autonomous Underwater Robot

Obstacle avoidance in the 3-D region for an underwater robot such as autonomous underwater vehicle is still a difficult research topic. A novel 3-D obstacle avoidance algorithm for an autonomous underwater robot with a sphere cross section method is discussed in this letter. The proposed mechanism utilizes a Dubins-like curve to design a continuous path in a 3-D underwater region while adjusting its heading angle with coordinate system rotation and sphere cross section methods if there is one convex polygon obstacle along the designed trajectory. Numerical simulation results verify that the proposed algorithm can avoid obstacles while maintaining a continuous and smooth cruising trajectory.

Three‐dimensional obstacle avoidance for UAV based on reinforcement learning and RealSense

With the increasingly widespread application of unmanned aerial vehicle (UAV), safety issues such as effectiveness of obstacle avoidance have been paid more attentions. The classical obstacle avoidance algorithms are mostly suitable for mobile robots, but these algorithms are not ideal for UAV using in three-dimensional space. Most of the three-dimensional obstacle avoidance algorithms which are more effective using RGB image data as input. Thus, a large amount of image data is involved in complex computing process. This study proposes an effective obstacle avoidance algorithm for UAV with less input data and fewer sensors based on RealSense and reinforcement learning. It combines the feature map of the depth image of RealSense as the input data of reinforcement learning and the current direction of flight of UAV to calculate the direction and angle of avoiding. The proposed algorithm that implements real-time obstacle avoidance for UAV has been verified by simulation and tested in three-dimensional space scenario.

Three-Dimensional Obstacle Avoidance Strategies for Uninhabited Aerial Systems Mission Planning and Replanning

The ability to perform autonomous mission planning is considered one of the key enabling technologies for uninhabited aerial systems. Subsequently, a big effort is made in the development of algorithms capable of computing safe and efficient routes in terms of distance, time, and fuel. In this paper an innovative 3-D planning algorithm is presented. The algorithm is based on considering the uninhabited aerial systems representation of real world systems as objects moving in a virtual environment (terrain, obstacles, and no fly zones), which replicates the airspace. Original obstacle avoidance strategies have been conceived to generate mission plans that are consistent with flight rules and with the vehicle performance constraints. Simulation test results show that efficient routes are computed in a few seconds.

Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

Everyday movements pursue diverse and often conflicting mixtures of task goals, requiring sensorimotor strategies customized for the task at hand. Such customization is mostly ignored by traditional theories emphasizing movement geometry and servo control. In contrast, the relationship between the task and the strategy most suitable for accomplishing it lies at the core of our optimal feedback control theory of coordination. Here, we show that the predicted sensitivity to task goals affords natural explanations to a number of novel psychophysical findings. Our point of departure is the little-known fact that corrections for target perturbations introduced late in a reaching movement are incomplete. We show that this is not simply attributable to lack of time, in contradiction with alternative models and, somewhat paradoxically, in agreement with our model. Analysis of optimal feedback gains reveals that the effect is partly attributable to a previously unknown trade-off between stability and accuracy. This yields a testable prediction: if stability requirements are decreased, then accuracy should increase. We confirm the prediction experimentally in three-dimensional obstacle avoidance and interception tasks in which subjects hit a robotic target with programmable impedance. In additional agreement with the theory, we find that subjects do not rely on rigid control strategies but instead exploit every opportunity for increased performance. The modeling methodology needed to capture this extra flexibility is more general than the linear-quadratic methods we used previously. The results suggest that the remarkable flexibility of motor behavior arises from sensorimotor control laws optimized for composite cost functions.

Three-Dimensional Obstacle Avoidance of Blimp-Type Unmanned Aerial Vehicle Flying in Unknown and Non-Uniform Wind Disturbance

A blimp-type unmanned aerial vehicle (BUAV) maintains its longitudinal motion using buoyancy provided by the air around it. This means the density of a BUAV equals that of the surrounding air. Because of this, the motion of a BUAV is seriously affected by flow disturbances, whose distribution is usually non-uniform and unknown. In addition, the inertia in the heading motion is very large. There is also a strict limitation on the weight of equipment in a BUAV, so most BUAVs are so-called under-actuated robots. From this situation, it can be said that the motion planning of the BUAV considering the stochastic property of the disturbance is needed for obstacle avoidance. In this paper, we propose an approach to the motion planning of a BUAV via the application of Markov decision process (MDP). The proposed approach consists of a method to prepare a discrete MDP model of the BUAV motion and a method to maintain the effect of the unknown wind on the BUAV’s motion. A dynamical simulation of a BUAV in an environment with wind disturbance shows high performance of the proposed method.