RRT*-SMART: a rapid convergence implementation of RRT*

Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been prposed to overcome the limitaions of RRT*. The goal of the proposecd method is to accelerate the rate of convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*–Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*-Smart algorithm

Sampling-based optimal kinodynamic planning with motion primitives

This paper proposes a novel sampling-based motion planner, which integrates in RRT* (Rapidly exploring Random Tree star) a database of pre-computed motion primitives to alleviate its computational load and allow for motion planning in a dynamic or partially known environment. The database is built by considering a set of initial and final state pairs in some grid space, and determining for each pair an optimal trajectory that is compatible with the system dynamics and constraints, while minimizing a cost. Nodes are progressively added to the tree {of feasible trajectories in the RRT* by extracting at random a sample in the gridded state space and selecting the best obstacle-free motion primitive in the database that joins it to an existing node. The tree is rewired if some nodes can be reached from the new sampled state through an obstacle-free motion primitive with lower cost. The computationally more intensive part of motion planning is thus moved to the preliminary offline phase of the database construction at the price of some performance degradation due to gridding. Grid resolution can be tuned so as to compromise between (sub)optimality and size of the database. The planner is shown to be asymptotically optimal as the grid resolution goes to zero and the number of sampled states grows to infinity

Algoritma Rapidly Exploring Random Tree Star Dengan Integrasi Metode Sampling Goal Biassing, Gaussian, Dan Boundary

The path planning algorithm is to find a path that takes the robot from the start state to the goal state while avoiding collisions with obstacles. In path planning, various applications have been used such as animation, medicine, aircraft, etc. The purpose of this study is to design a new sampling method by integrating sampling methods based on goal biasing, Gaussian and Boundary and then implementing it in path planning problems using the Rapidly Exploring Random Tree* (RRT*) algorithm. We call this sampling method the integration sampling method. The path planning algorithm using this integration sampling method is implemented in the Labview programming language. The algorithm parameters in Labview can be modified to observe the output performance of the RRT* algorithm. The test was carried out in an environment of obstacle clutter, SquareField BW, and traps, where the test was carried out 20 times for each obstacle. The test was conducted to compare the path distance and computation time of the RRT* algorithm using the integration sampling method, against the RRT* algorithm using the Gaussian, and Boundary sampling method. Based on the test results, it is found that the RRT* algorithm using the integration sampling method can produce a shorter path than the RRT* algorithm using the Gaussian method and the RRT* algorithm using Boundary sampling. Comparison of the resulting computational time is faster than the Gaussian integration method. However, a comparison with Boundary shows that Boundary requires less time than integration. Therefore, it can be concluded that the Rapidly Exploring Random Tree* algorithm integration method is superior to the Gaussian method and the Boundary method.Algoritma perencanaan jalur adalah untuk menemukan lintasan yang membawa robot dari keadaan awal (start) ke keadaan tujuan (goal) sambil menghindari tabrakan dengan rintangan. Dalam perencanaan jalur, berbagai aplikasi telah digunakan seperti animasi, kedokteran, pesawat, dll. Tujuan penelitian ini adalah merancang metode sampling baru dengan cara melakukan integrasi metode sampling berbasis goal biassing, Gaussian dan Boundary lalu mengimplementasikannya pada masalah perencanaan jalur menggunakan algoritma Rapidly Exploring Random Tree* (RRT*). Metode sampling tersebut kami namakan metode sampling integrasi. Algoritma perencanaan jalur menggunakan metode sampling integrasi ini diimplementasikan pada bahasa pemograman Labview. Parameter algoritma pada Labview dapat dimodifikasi untuk mengamati performansi output dari algoritma RRT*. Pengujian dilakukan pada lingkungan obstacle clutter, SquareField BW, dan trap, dimana pengujian dilakukan 20 kali percobaan pada masing-masing obstacle. Pengujian dilakukan untuk membandingan jarak jalur serta waktu komputasi dari algoritma RRT* yang menggunakan metode sampling integrasi, terhadap algoritma RRT* yang menggunakan metode sampling Gaussian, dan Boundary. Berdasarkan hasil pengujian, diperoleh bahwa algoritma RRT* yang menggunakan metode sampling integrasi dapat menghasilkan jalur yang lebih pendek dibandingkan dengan algoritma RRT* yang menggunakan metode Gaussian maupun algoritma RRT* yang menggunakan sampling Boundary. Perbandingan waktu komputasi yang dihasilkan lebih cepat metode integrasi dibandingkan dengan Gaussian. Akan tetapi, pada perbandingan dengan Boundary menunjukkan bahwa Boundary memerlukan lebih sedikit waktu dibandingkan dengan integrasi. Maka dari itu dapat disimpulkan bahwa algortima Rapidly Exploring Random Tree* metode integrasi lebih unggul dibandingkan dengan metode Gaussian maupun metode Boundary

Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-

Dynamic Region RRT Application to Kinodynamic Systems

In the general motion planning problem the robot must satisfy basic constraints such as avoiding obstacles and remaining within the boundary of the environment. Kinodynamic motion planning is a type of planning where additional constraints must be satisfied. Kinodynamic planning is a more realistic planning problem as the robot must operate under constraints such as friction, gravity, velocity, and acceleration while avoiding obstacles as well. Sampling-based methods are often used to solve these types of problems. These methods generate robot configurations throughout the environment in order to eventually connect them to form a valid path from the start position to the goal. Rapidly-exploring Random Trees (RRT) are types of sampling-based methods that grow a tree from the start to goal. One important problem with these types of methods appears when planning in an environment with a narrow passage or cluttered space. In these problems it is unlikely to generate a sample in the narrow spaces and the robot does not explore these locations. Dynamic Region-biased Rapidly-exploring Random Trees (DRRRT) is a method that addresses these issues by guiding an RRT with dynamic sampling regions along an embedded graph of the workspace. DRRRT is effective in general motion planning problems, but faces issues in kinodynamic problems. Oftentimes, a sample is generated near an obstacle that is valid, but is found to be unrecoverable because if the robot were to move from that state with any of the available controls it would collide with an obstacle. This often occurs in environments with narrow spaces and tight turns such as a maze. In this work, we aim to address the problems DRRRT faces in kinodynamic problems with a series of improvements. The resulting method is compared with other motion planning techniques on two kinodynamic problems consisting of a car-like robot navigating a grid-like city and a maze, simulating narrow paths with numerous turns

Obstacle-avoidance path planning based on the improved artificial potential field for a 5 degrees of freedom bending robot

Path planning is a key technique used in the operation of bending robots. In this paper, an obstacle-avoidance path-planning method of a 5 degrees of freedom (5 DOF) bending robot based on improved artificial potential field is proposed. Firstly, a connecting-rod coordinate system of the 5 DOF Cartesian bending robot is established to determine an equation of motion trajectory of the bending robot. Secondly, in view of the problem of the local minimum in the artificial potential field (APF) method and the failure of path planning, an improved APF path-planning method based on a rapidly exploring random tree (RRT) algorithm is proposed, which reduces the length of the path and enhances path smoothness. Finally, through simulation and obstacle-avoidance experiments on the path of a mechanical arm, effective path planning based on the improved APF method is verified. The experimental results show that the proposed path-planning method can plan an optimal path and meet the technical requirements of bending robot operations.</p

Dynamic Path Planning and Replanning for Mobile Robots using RRT*

It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current, location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT* is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method