Optimal Rapidly-exploring Random Tree Research Articles

Logistics warehousing system shelving station route planning based on X-ARM

The need for automated control in warehouse and logistics systems has increased recently, particularly in the area of cargo storage. The use of robot arms in manufacturing is an important area for development. Multi-arm actions, including switching between multiple arms, expand the number of potential operations that numerous operators can do, but they also bring more computing hurdles. Multiple robotic arm systems must operate with careful path planning. In this study, Based on previous research papers, we propose the utilization of dRRT* and mmdRRT* algorithms for efficient path planning. The dRRT* algorithm improves exploration and convergence to an ideal path by combining the advantages of the rapidly-exploring random tree (RRT) with the optimal RRT* algorithm. To address complicated and dynamic situations, the mmdRRT* algorithm, on the other hand, uses a multi-modal distribution model. We want to improve the reliability and effectiveness of path planning for various robotic arm systems by combining these two techniques.

A path planning algorithm for autonomous flying vehicles in cross-country environments with a novel TF-RRT∗ method

Autonomous flying vehicles (AFVs) are promising future vehicles, which have high obstacle avoidance ability. To plan a feasible path in a wide range of cross-country environments for the AFV, a triggered forward optimal rapidly-exploring random tree (TF-RRT∗) method is proposed. Firstly, an improved sampling and tree growth mechanism is built. Sampling and tree growth are allowed only in the forward region close to the target point, which significantly improves the planning speed; Secondly, the driving modes (ground-driving mode or air-driving mode) of the AFV are added to the sampling process as a planned state for uniform planning the driving path and driving mode; Thirdly, according to the dynamics and energy consumption models of the AFV, comprehensive indicators with energy consumption and efficiency are established for path optimal procedures, so as to select driving mode and plan driving path reasonably according to the demand. The proposed method is verified by simulations with an actual cross-country environment. Results show that the computation time is decreased by 71.08% compared with Informed-RRT∗ algorithm, and the path length of the proposed method decreased by 13.01% compared with RRT∗-Connect algorithm.

Optimised Informed RRTs for Mobile Robot Path Planning

Path planners based on basic rapidly-exploring random trees (RRTs) are quick and efficient, and thus favourable for real-time robot path planning, but are almost-surely suboptimal. In contrast, the optimal RRT (RRT*) converges to the optimal solution, but may be expensive in practice. Recent work has focused on accelerating the RRT*'s convergence rate. The most successful strategies are informed sampling, path optimisation, and a combination thereof. However, informed sampling and its combination with path optimisation have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking. This paper investigates the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs. We apply different path optimisers and compare their effectiveness. The goal is to ascertain if applying informed sampling and path optimisation can help the quick, though almost-surely suboptimal, path planners based on the basic RRT attain comparable or better performance than RRT*-based planners. Analyses show that RRT-based optimised informed RRTs do attain better performance than their RRT*-based counterparts, both when planning time is limited and when there is more planning time.

Multi-agent motion planning with sporadic communications for collision avoidance

A novel multi-vehicle motion planning and collision avoidance algorithm is proposed and analyzed in this paper. The algorithm aims to reduce the amount of onboard calculations and inter-agent communications needed for each vehicle to successfully navigate through an environment with static obstacles and reach their goals. To this end, each agent first calculates a path to the goal by means of an asymptotically optimal rapidly-exploring random tree (RRT*) with respect to the static obstacles. Then, other agents are treated as dynamic obstacles and potential collisions are determined by means of collision cones. Collision cones depend on the position and velocity from other agents and are grown conservatively between inter-agent communications. Based on the available information, each agent determines if a deconfliction maneuver is needed, if it can continue along its current path, or if communication is needed to make a decision about a conflict. With probability one, our algorithm guarantees that the agents keep from colliding with each other. Under an assumption on the existence of a solution for a vehicle to its goal, this algorithm also solves the planning problem with probability one. Simulations illustrate a group of agents successfully reaching their goal configurations and examine how the uncertainty affects the communication frequency of the multi-agent system.

Safe navigation in a coastal environment of multiple surface vehicles under uncertainties: A combined use of potential field constructions and NMPC

This paper deals with the motion planning problem for surface vehicles in the presence of varying environmental disturbances. The goal is to steer the vehicles from an initial to a final destination while ensuring connectivity and avoiding collision with fixed and moving obstacles. The novelty lies in the coherent combination among various ingredients for path generation and potential field constructions for collision avoidance and connectivity maintenance, all gathered in a distributed NMPC (Nonlinear Model Predictive Control) framework: i) LOS (Line-of-Sight) guidance and RRT* (optimal Rapidly-exploring Random Tree) algorithms are employed for generating a collision-free path considering static surroundings; ii) on-off barrier functions activate the proposed potential field components which are in the view range of the agents, hence ensuring safe navigation in a dynamic coastal environment with a low computation demand; iii) a NDO (Nonlinear Disturbance Observer) is integrated in the NMPC design to compensate the environmental disturbances. The proposed algorithm is validated through simulations and comparisons carried out over a benchmark for USVs (Unmanned Surface Vehicles) safe navigation in the Trondheim fjord, Norway.

Online planning for relative optimal and safe paths for USVs using a dual sampling domain reduction-based RRT* method

A heuristic Dual sampling domain Reduction-based Optimal Rapidly-exploring Random Tree scheme is proposed by guiding the planning procedure of the optimal rapidly-exploring random tree (RRT*) method through learning environmental knowledge. The scheme aims to plan low fuel expenditure, easy-execution, and low collision probability paths online for an unmanned surface vehicle (USV) under constraints. First, an elliptic sampling domain, which is subject to an elliptic equation and the shortest obstacle avoidance path estimation, is created to plan short paths. Second, by the consideration of the USV motion states, obstacles and external interferences of the current, the near sampling domains of tree nodes are reduced to exclude high-cost sampling domains. Path feasibility is ensured by explicitly handling motion constraints. Third, a safe distance-based collision detection (CD) scheme and a velocity-based bounding box of USV are proposed to decrease the path collision probability. Additionally, a layered USV online path planning framework is built in accordance with the model predictive control method, and the path smoothing scheme is applied via the Dubins curve under the curvature constraint. Results demonstrate that the proposed dual sampling domain reduction method outperforms traditional reduction schemes in terms of improving the execution efficiency of RRT*. Meanwhile, the proposed CD method is more reliable than the conventional one.

Online feedback motion planning for spacecraft obstacle avoidance using positively invariant sets

This paper investigates a novel reliable feedback motion planning algorithm for steering a spacecraft to the desired position while satisfying dynamics and environmental constraints, such as actuator constraints, unexpected state constraints, bounded disturbances, etc. The proposed algorithm, termed safeRRT∗, applies positively invariant (PI) set transitions to an online sampling approach based on the asymptotically optimal rapidly-exploring random tree (RRT∗). A time-varying safe corridor is generated which consists of a sequence of PI sets under the instantaneous constraints. A spacecraft is guaranteed to remain within the corridor when the feedback controllers corresponding to the PI set transitions are executed. Simulations of the spacecraft avoiding obstacles validate the feasibility and effectiveness of the proposed safeRRT∗ algorithm.

Horizon-based lazy optimal RRT for fast, efficient replanning in dynamic environment

Planning of collision-free trajectory for robot motion under hard constraints and unpredictable environment is a difficult issue. To cope with this problem, this paper presents a novel replanning method based on receding horizon control and asymptotically optimal single-query motion planning. The approach, called the horizon-based lazy optimal rapidly-exploring random tree algorithm, enables real-time replanning in both static and dynamic environment with updated information. Previous feasible solutions are fully considered to generate new plans. Contributions include lazy steering and lazy collision checking search tree, forward tree pruning and sampling distribution online learning. The techniques are proven to be efficient, near optimal and fast responsive to change. Moreover, three experiments are performed to test the properties of the proposed algorithm numerically.

Kinodynamic Motion Planning With Continuous-Time Q-Learning: An Online, Model-Free, and Safe Navigation Framework.

This paper presents an online kinodynamic motion planning algorithmic framework using asymptotically optimal rapidly-exploring random tree (RRT*) and continuous-time Q-learning, which we term as RRT-Q⋆. We formulate a model-free Q-based advantage function and we utilize integral reinforcement learning to develop tuning laws for the online approximation of the optimal cost and the optimal policy of continuous-time linear systems. Moreover, we provide rigorous Lyapunov-based proofs for the stability of the equilibrium point, which results in asymptotic convergence properties. A terminal state evaluation procedure is introduced to facilitate the online implementation. We propose a static obstacle augmentation and a local replanning framework, which are based on topological connectedness, to locally recompute the robot's path and ensure collision-free navigation. We perform simulations and a qualitative comparison to evaluate the efficacy of the proposed methodology.

Quick-RRT*: Triangular inequality-based implementation of RRT* with improved initial solution and convergence rate

The Rapidly-exploring Random Tree (RRT) algorithm is a popular algorithm in motion planning problems. The optimal RRT (RRT*) is an extended algorithm of RRT, which provides asymptotic optimality. This paper proposes Quick-RRT* (Q-RRT*), a modified RRT* algorithm that generates a better initial solution and converges to the optimal faster than RRT*. Q-RRT* enlarges the set of possible parent vertices by considering not only a set of vertices contained in a hypersphere, as in RRT*, but also their ancestry up to a user-defined parameter, thus, resulting in paths with less cost than those of RRT*. It also applies a similar technique to the rewiring procedure resulting in acceleration of the tendency that near vertices share common parents. Since the algorithm proposed in this paper is a tree extending algorithm, it can be combined with other sampling strategies and graph-pruning algorithms. The effectiveness of Q-RRT* is demonstrated by comparing the algorithm with existing algorithms through numerical simulations. It is also verified that the performance can be further enhanced when combined with other sampling strategies.

Guidance-Control System of a Quadrotor for Optimal Coverage in Cluttered Environment with a Limited Onboard Energy: Complete Software

In this paper, a Guidance-Control System (GCS) for optimal coverage planning, using a quadrotor, in damaged area is considered. The quadrotor is assumed to visit a set of reachable points, defined manually by the user or automatically generated, following the shortest path while avoiding the no-fly zones. The problem is solved by using a two-stage proposed algorithm. In the first stage, a novel tool for cluttered environments based on optimal Rapidly-exploring Random Trees (RRT) approach, called Multi-RRT* Fixed Node (RRT*FN), is developed to define the shortest paths from each point to its neighbors. By means of the pair-wise costs between points provided by the first-stage algorithm, in the second stage, the overall shortest path is obtained by solving a Traveling Salesman Problem (TSP) using Genetic Algorithms (GA). Taking into consideration the limited onboard energy, multi-rounds for the coverage planning are assumed as an alternative by adapting our problem as a Vehicle Routing Problem (VRP). This latter is solved using the savings heuristic approach. The guidance module is supported by an efficient controller that minimizes the consumed energy and allows a damped response (i.e. without overshoot). It is a reference model based control strategy called Interconnection Damping Assignment-Passivity Based Control (IDA-PBC). The effectiveness of the overall system is demonstrated via numerical simulations and confirmed experimentally with very promising results.

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.

An Innovative Algorithm to Estimate Risk Optimum Path for Unmanned Aerial Vehicles in Urban Environments

Abstract The diffusion of the Unmanned Aerial Vehicles (UAVs) requires a suitable approach to define safe flight operations. In this paper, an innovative algorithm able to quantify the risk to the population and to search for the minimum risk path is proposed. The method has two main phases: in the former, a risk-map is generated quantifying the risk of a specific area, in the latter, a path planning algorithm seeks for the optimal path minimizing the risk. The risk-map is generated with a risk assessment method combining layers related to the population density, the sheltering factor, no-fly zones and obstacles. The risk-aware path planning is based on the well-known Optimal Rapidly-exploring Random Tree, with the minimization of the risk cost with respect to the flight time. Simulation results corroborate the validity of the approach.

Teaching Robot Navigation Behaviors to Optimal RRT Planners

This work presents an approach for learning navigation behaviors for robots using Optimal Rapidly-exploring Random Trees (RRT $$^{*}$$ ) as the main planner. A new learning algorithm combining both Inverse Reinforcement Learning and RRT $$^{*}$$ is developed to learn the RRT $$^{*}$$ ’s cost function from demonstrations. A comparison with other state-of-the-art algorithms shows how the method can recover the behavior from the demonstrations. Finally, a learned cost function for social navigation is tested in real experiments with a robot in the laboratory.

Traversability-Based RRT*for Planetary Rover Path Planning in Rough Terrain with LIDAR Point Cloud Data

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.

Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments

The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently its bidirectional version has also been introduced in the literature known as Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT* and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed for complex cluttered environments. IB-RRT* utilizes the bidirectional trees approach and introduces intelligent sample insertion heuristic for fast convergence to the optimal path solution using uniform sampling heuristics. The proposed algorithm is evaluated theoretically and experimental results are presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental results demonstrate the superior efficiency of IB-RRT* in comparison with RRT* and B-RRT in complex cluttered environments.

Efficient collision-free trajectory planning for WSN data collection with Unmanned Aerial Vehicles

This paper presents a collision-free trajectory planner to perform applications of WSN (Wireless Sensor Network) data collection in dynamic environments with Unmanned Aerial Vehicles. The missions are given by a set of waypoints which define WSN collection zones and each UAV should pass through these zones to collect the data. Therefore, two planners based on RRT (Rapidly-exploring Random Trees) and RRT* (Optimal Rapidly-exploring Random Trees) planning algorithms have been implemented. These planners compute trajectories between WSN collection zones to ensure that the UAV passes through the WSN collection zones. Simulations and experiments carried out in the airfield of Utrera (Seville, Spain) show the performance and advantages of the proposed algorithm.