A Variable-Step RRT^* Path Planning Algorithm for Quadrotors in Below-Canopy

Abstract

Rapidly-exploring random tree (RRT * ) algorithm has been widely applied to path planning problem for quadrotor, which takes a great amount of static and dynamic constraints into account. However, conventional RRT * algorithm is suffering low convergence rate and efficiency in below-canopy environment, where is usually occupied with narrow aisles and uneven distributed obstacles. In order to enrich the forest information database and obtain information efficiently in blow-canopy, an improved variable-step RRT * algorithm has been proposed in this paper. In the proposed algorithm, the sampling nodes are determined by the target bias sampling strategy with variable probability. To further increase the efficiency of node generation and balance the searching time and security in areas with different obstacles densities, the eventtriggered step size extension has been proposed according to hyperbolic tangent function. After that, both Euclidean distance and angle constraints are adopted in the cost function of node connection optimization. Then, the path is further optimized by path trimming and Bezier curves smooth method. Finally, the demonstration of the proposed algorithm is presented to show its efficiency, and the comparison result with existing RRT-like algorithms indicates that the performance of our algorithm is better than RRT, RRT * and DDRRT in terms of convergence and accuracy. Comparing to other classical path planning methods such as A * , Artificial potential field and Fuzzy-logic, our method also shows its superiority.INDEX TERMS RRT * , Path planning, quadrotor, below-canopy environment, variable-step.

Potential Force Algorithm with Kinematic Control as Path Planning for Disinfection Robot

“…That is, 𝑢 1 (𝑡) = 𝜃 ̇𝑅(𝑡), 𝑢 2 (𝑡) = 𝜃 ̇𝐿(𝑡), and 𝜃̇̇(𝑡) = 𝜔(𝑡). From the three parameters of equation ( 8 The Artificial Potential Field (APF) algorithm [51]- [54] was first discovered by Khatib [55], which employs the theory of attractive and repulsive forces such as magnetic fields. In other words, artificial potential field force [56] 𝐹 𝐴𝑃𝐹 is the sum of the gravitational potential field force [57] 𝐹 𝑎𝑡𝑡 and the repulsive potential field force [58] 𝐹 𝑟𝑒𝑝 .…”

Collision-Free Path Planning for Automated Vehicles Risk Assessment via Predictive Occupancy Map

“…Thus, if the risks of lane change or driving in a longitudinal direction are similar, the longitudinal deceleration to avoid crash will be selected. Therefore, the risks of a traffic lane can be written as (14) where 𝑊𝑊 𝑜𝑜𝑚𝑚𝑛𝑛𝑒𝑒 is the width of lane marking, and 𝑅𝑅 𝑜𝑜𝑚𝑚𝑛𝑛𝑒𝑒−𝑚𝑚𝑚𝑚𝑥𝑥 is the maximum risk value of lane markings. It can be seen from the equation above, the closer to the lane marking, the greater the risk value on the costmap.…”

“…Optimal rapidly-exploring random tree algorithm is a motion planning algorithm for a vehicle travels through a known costmap [14], which was applied to find the safest trajectory on the global coordinate based on the existing POM, given starting point and endpoint of the path. Some vehicle constraints are also implemented with customized values in RRT* such as tolerance around goal pose, the connection between consecutive poses, and the minimum turning radius of the vehicle.…”

Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an SAC-LSTM Algorithm

“…The Lazy PRM [ 7 ] algorithm is an improved version of the PRM algorithm that enhances efficiency by reducing the number of calls to the local planner. Liu et al [ 8 ] improved the RRT algorithm by using a goal-biased sampling strategy to determine the nodes and introduced an event-triggered step length extension based on the hyperbolic tangent function to improve node generation efficiency. Euclidean distance and angle constraints were used in the cost function of node connection optimization.…”