Real-time Path Planning Research Articles

Improved Artificial Potential Field Method Applied for AUV Path Planning

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

Trajectory Planning with Collision Avoidance for Redundant Robots Using Jacobian and Artificial Potential Field-based Real-time Inverse Kinematics

This study proposes an algorithm for combining the Jacobian-based numerical approach with a modified potential field to solve real-time inverse kinematics and path planning problems for redundant robots in unknown environments. With an increase in the degree of freedom (DOF) of the manipulator, however, the problems in realtime inverse kinematics become more difficult to solve. Although the analytical and geometrical inverse kinematics approach can obtain the exact solution, it is considerably difficult to solve as the DOF increases, and it necessitates recalculations whenever the robot arm DOF or Denavit-Hartenberg (D-H) parameters change. In contrast, the numerical method, particularly the Jacobian-based numerical method, can easily solve inverse kinematics irrespective of the aforementioned changes including those in the robot shape. The latter method, however, is not employed in path planning for collision avoidance, and it presents real-time calculation problems. This study accordingly proposes the Jacobian-based numerical approach with a modified potential field method that can realize real-time calculations of inverse kinematics and path planning with collision avoidance irrespective of whether the case is redundant or non-redundant. To achieve this goal, the use of a judgment matrix is proposed for obstacle condition identification based on the obstacle boundary definition; an approach for avoiding the local minimum is also proposed. After the obstacle avoidance path is generated, a trajectory plan that follows the path and avoids the obstacle is designed. Finally, the proposed method is evaluated by implementing a motion planning simulation of a 7-DOF manipulator, and an experiment is performed on a 7-DOF real robot.

Real-Time Optimal Trajectory Generation and Control of a Multi-Rotor With a Suspended Load for Obstacle Avoidance

This letter presents real-time optimization algorithms on trajectory generation and control for a multi-rotor with a suspended load. Since the load is suspended through a cable without any actuator, movement of the load must be controlled via maneuvers of the multi-rotor, which brings about difficulties in operating this system. Additionally, the highly nonlinear dynamics of the system exacerbates the difficulties. While trajectory generation and control are essential for safety, energy efficiency, and stability, the aforementioned characteristics of the system add challenges. With this in mind, the authors propose real-time path planning and optimal control algorithms for collision-free trajectory generation and trajectory tracking. For the dynamics, simplified dynamic models of the system are proposed by considering time delay in attitude control of the multi-rotor. For collision avoidance, the vehicle, cable, and load are considered as ellipsoids with different sizes and shapes, and collision-free constraints are expressed in an efficient and nonconservative way. The augmented Lagrangian method is applied to solve the nonlinear optimization problem with the nonlinear constraints in real-time. For control of the system, model predictive control with a sequential linear quadratic solver is used. Several simulations and experiments are conducted to validate the proposed algorithm.

The autonomous navigation and obstacle avoidance for USVs with ANOA deep reinforcement learning method

The unmanned surface vehicle (USV) has been widely used to accomplish missions in the sea or dangerous marine areas for ships with sailors, which greatly expands protective capability and detection range. When USVs perform various missions in sophisticated marine environment, autonomous navigation and obstacle avoidance will be necessary and essential. However, there are few effective navigation methods with real-time path planning and obstacle avoidance in dynamic environment. With tailored design of state and action spaces and a dueling deep Q-network, a deep reinforcement learning method ANOA (Autonomous Navigation and Obstacle Avoidance) is proposed for the autonomous navigation and obstacle avoidance of USVs. Experimental results demonstrate that ANOA outperforms deep Q-network (DQN) and Deep Sarsa in the efficiency of exploration and the speed of convergence not only in static environment but also in dynamic environment. Furthermore, the ANOA is integrated with the real control model of a USV moving in surge, sway and yaw and it achieves a higher success rate than Recast navigation method in dynamic environment.

Hybrid Path Planning Based on Safe A* Algorithm and Adaptive Window Approach for Mobile Robot in Large-Scale Dynamic Environment

When mobile robot used in large-scale dynamic environments, it face more challenging problems in real-time path planning and collision-free path tracking. This paper presents a new hybrid path planning method that combines A* algorithm with adaptive window approach to conduct global path planning, real-time tracking and obstacles avoidance for mobile robot in large-scale dynamic environments. Firstly, a safe A* algorithm is designed to simplify the calculation of risk cost function and distance cost. Secondly, key path points are extracted from the planned path which generated by the safe A* to reduce the number of the grid nodes for smooth path tracking. Finally, the real-time motion planning based on adaptive window approach is adopted to achieve the simultaneous path tracking and obstacle avoidance (SPTaOA) together the switching of the key path points. The simulation and practical experiments are conducted to verify the feasibility and performance of the proposed method. The results show that the proposed hybrid path planning method, used for global path planning, tracking and obstacles avoidance, can meet the application needs of mobile robots in complex dynamic environments.

An Improved Real-Time Path Planning Method Based on Dragonfly Algorithm for Heterogeneous Multi-Robot System

Heterogeneous multi-robot system is one of the most important research directions in the robotic field. Real-time path planning for heterogeneous multi-robot system under unknown 3D environment is a new challenging research and a hot spot in this field. In this paper, an improved real-time path planning method is proposed for a heterogeneous multi-robot system, which is composed of many unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In the proposed method, the 3D environment is modelled as a neuron topology map, based on the grid method combined with the bio-inspired neural network. Then a new 3D dynamic movement model for multi-robots is established based on an improved Dragonfly Algorithm (DA). Thus, the movements of the robots are optimized according to the activities of the neurons in the bio-inspired neural network to realize the real-time path planning. Furthermore, some simulations have been carried out. The results show that the proposed method can effectively guide the heterogeneous UAV/UGV system to the target, and has better performance than traditional methods in the real-time path planning tasks.

Enhancing Path Quality of Real-Time Path Planning Algorithms for Mobile Robots: A Sequential Linear Paths Approach

The traditional trade-off between execution speed and path quality has forced real-time robotic path planning algorithms to sacrifice path quality in order to execute in real-time. Producing a path planning algorithm that targets enhancing both, the path quality and swiftness is a challenging problem. However, this article proposes a novel path planning strategy that aims to break this traditional trade-off, by targeting both, increasing the swiftness, and enhancing the path quality represented by the path length and smoothness. The proposed strategy is based on the observation that most path planning algorithms waste the processing efforts in less critical areas of the map. Therefore, the proposed path planning strategy tends to focus on critical areas such as the areas around obstacles and areas around the goal point, and exhausts the processing power on these critical areas. This is done by neglecting all static obstacles that do not lie between the robot and the destination. For obstacles that intersect with the linear line from the robot to the destination, a basis traditional path planning algorithm such as A*, D* or the Probabilistic RoadMap (PRM) technique is only implemented around the obstacles in order to find a feasible path around each selected obstacle. This procedure would minimize the computational efforts compared to applying the basis algorithm on the whole map. Finally, the path quality is enhanced by finding any linear shortcuts between any two points in the path and fix these shortcuts as the final path from the starting point to the goal point. The proposed path planning strategy was tested on a P3-DX Pioneer mobile robot using a kinematic controller. The experimental results have proven that the path planning strategy was able to show a superior advantage over other path planning techniques in both aspects, computational time (reached up to 97.05% improvement) and path quality (reached up to 16.21% improvement for path length and 98.50% for smoothness).

Road-Condition-Aware Dynamic Path Planning for Intelligent Vehicles

Abstract Intelligent vehicles face a more complex driving environment, which requires their path planning algorithms to have the ability to respond quickly to complex environmental changes. Therefore, it is very necessary to plan the optimal path for smart vehicles in real-time. Recently, the convergence of graph dynamics and its equivalence with the shortest path solution have been proved, and the biased minimum consensus algorithm has been successfully applied to the shortest path planning problem. In this paper, considering the road conditions (degree of congestion, whether an accident occurred, etc.) and the Euclidean distance of the route, we improve biased minimum consensus algorithm to achieve optimal path planning in the complex situation of real-time updating of road conditions. In the simulation, we used the algorithm for path planning in the road topological maps of Beijing City, and compared with the RRT (Rapidly-exploring Random Trees Algorithm) algorithm and the BRRT (Bidirectional Rapidly-exploring Random Trees Algorithm) algorithm. The improved biased minimum consensus algorithm has better performance in real-time path planning problems. The research results shed new light on the real-time dynamic path planning in real traffic, which has practical significance.

A Two-Stage Real-Time Path Planning: Application to the Overtaking Manuever

This paper proposes a two-stage local path planning approach to deal with all kinds of scenarios (i.e. intersections, turns, roundabouts). The first stage carries out an off-line optimization, considering vehicle kinematics and road constraints. The second stage includes all dynamic obstacles in the scene, generating a continuous path in real-time. Human-like driving style is provided by evaluating the sharpness of the road bends and the available space among them, optimizing the drivable area. The proposed approach is validated on overtaking scenarios where real-time path planning generation plays a key role. Simulation and real results on an experimental automated platform provide encouraging results, generating real-time collision-free paths while maintaining the defined smoothness criteria.

Adaptive Artificial Potential Fields with Orientation Control Applied to Robotic Manipulators

Abstract This paper proposes the integration of an Adaptive Artificial Potential Fields algorithm with a new end effector orientation control technique for real-time robot path planning. The development of autonomous robotic systems has undergone several advances in path planning algorithms. These systems generate object collision-free paths in the robot’s workspace. In this context, the Artificial Potential Fields technique has been the focus of improvements in recent years due to its simplicity of application and efficiency in real-time systems, since it does not require a global mapping of the robot’s workspace. In spite of its efficiency, this technique is susceptible to local minimum problems of different natures, such as Goals Non-Reachable with Obstacles Nearby (GNRON). To solve this problem, we suggest the use of an improvement called Adaptive Artificial Potential Fields used in conjunction with the proposed end effector orientation control technique, which allows reaching a desired orientation of the end effector. The resulting force, generated from the Adaptive Artificial Potential Field, guides the robot end effector to the goal. The Robot Operating System (ROS) framework and a collaborative robot manipulator UR5 are used to validate the proposed method on an approaching task for an object on a 3D printer tray.

An Improved Sampling-Based Approach for Spacecraft Proximity Operation Path Planning in Near-Circular Orbit

This paper proposes an improved sampling-based approach for spacecraft proximity operation path planning under Clohessy-Wiltshire-Hill dynamics. The proposed approach is based on a modified version of the FMT* (Fast Marching Tree) algorithm with safety strategy which is divided into three parts: (1) incorporating relative ellipse to simplify the sampling state space and avoid collision with target; (2) combining internal/external ellipse-based collision detection algorithms for hovering obstacle and non-coplanar ellipse obstacle; (3) using rotating hyperplane method to handle the coplanar ellipse obstacle and uncertainty obstacle. By referring the safety strategy to simplify the state space before FMT* algorithm, the approach can reduce the complexity of path planning, especially the resampling and collision avoidance detection cyclic process in FMT*, thereby improve the planning efficiency. Two simulated scenarios, a coplanar path planning with/without coplanar ellipse obstacle, are developed to illustrate the approach. As a result, the proposed approach appears to be potential for spacecraft proximity real-time path planning as well as other complex space mission path planning generalized to different dynamics and environments, such as On-Orbit Service, and enabling a real-time computation of low-cost trajectory.

A Novel Real-Time Moving Target Tracking and Path Planning System for a Quadrotor UAV in Unknown Unstructured Outdoor Scenes

A quadrotor unmanned aerial vehicle (UAV) should have the ability to perform real-time target tracking and path planning simultaneously even when the target enters unstructured scenes, such as groves or forests. To accomplish this task, a novel system framework is designed and proposed to accomplish simultaneous moving target tracking and path planning by a quadrotor UAV with an onboard embedded computer, vision sensors, and a two-dimensional laser scanner. A support vector machine-based target screening algorithm is deployed to select the correct target from multiple candidates detected by single shot multibox detector. Furthermore, a new tracker named TLD-KCF is presented in this paper, in which a conditional scale adaptive algorithm is adopted to improve the tracking performance for a quadrotor UAV in cluttered outdoor environments. According to distance and position estimation for a moving target, our quadrotor UAV can acquire a control point to guide its fight. To reduce the computational burden, a fast path planning algorithm is proposed based on elliptical tangent model. A series of experiments are conducted on our quadrotor UAV platform DJI M100. Experimental video and comparison results among four kinds of target tracking algorithms are given to show the validity and practicality of the proposed approach.

Reverse Path Nearby Cluster (Rpnc) Query Optimization u sing Trajectory Clustering With Ensemble Learning (Tcel) In Spatial Networks

In spatial network, optimal path planning in real time is an open problem. For complex queries, Query optimization is intensively used. From given locations, most accessible locations can be found by Reverse Path nearby Cluster (R-PNC) query. This facility maximizes the commercial value. Distribution Clustering with Support Vector Machine is used to form a R-PNC clusters in the existing works. Trajectories with low positioning accuracy are used by this method. Trajectory Clustering with Ensemble Learning (TCEL) is proposed to overcome this problem. In this, Reverse Path nearby Cluster (R-PNC) query is used for effective path selection. Spatial datasets are handled using a two step process. Set of micro-clusters are maintained in the first step. Based on the input data, this micro clusters are continuously updated. Infinite data sources are compressed into a finite data set using Micro-clusters. More trajectory information can be stored using this finite dataset. In the second stage, ensemble learning is used to convert this micro-cluster into a macro-clusters of trajectories. Reverse the top k –RPNC. Based on their highest distribution they are stored and they are scanned to minimum from maximum. Improved Weight based Grey Wolf Optimization Algorithm (IWGWOA) is used to compute these values. The proposed system is implemented in MATLAB. The evaluation parameters like Normalized Mutual Information (NMI) and Rand Index (RI) are utilized for performance evaluation of proposed model.

Underwater Bioinspired Propulsion: From Inspection to Manipulation

This article presents the design of a biomimetic underwater vehicle-manipulator system (BUVMS) with bioinspired long-fin propulsion and its control methods. The kinematic analysis of bioinspired long-fin propulsion is conducted, and the maximum thrust of this propulsion derived from our thrust measurement platform can be up to 25 N. Bioinspired long-fin propulsion is introduced to construct the BUVMS for inspection and manipulation tasks, and the system architecture of the BUVMS is described. Moreover, real-time dynamic dubins-helix path planning and tracking of the biomimetic vehicle are proposed to realize the arrival of the desired pose. The tracking error is less than 0.23 body length. Finally, coordinated control between the biomimetic vehicle and the manipulator is developed to achieve autonomous manipulation. The reaction of the manipulator served as feedforward compensation is added into the closed-loop control of the biomimetic vehicle. The experimental results are provided to illustrate the validity of the proposed methods.

A software-in-the-loop implementation of adaptive formation control for fixed-wing UAVs

This paper discusses the design and software-in-the-loop implementation of adaptive formation controllers for fixed-wing unmanned aerial vehicles &#x0028 UAVs &#x0029 with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission &#x0028 e.g. depending on the payload &#x0029. Modelingg and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-in-the-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.

Cooperative Aerial Payload Transport Guided by an In Situ Human Supervisor

This paper defines and analyzes a cooperative aerial payload transport mission in which an onsite human supervisor provides haptic motion cues. While multiple small aerial robots (e.g., quadcopters) carry the weight of the payload, a human specifies traversal direction changes to the quadcopter team by exerting a pushing force on the payload. We show how the principle of linear momentum can be used for limited human intent recognition in this supervisory control application. This problem is formulated as real-time path planning of the quadcopter team using a continuum deformation cooperative control protocol that assures avoidance of interagent and obstacle collisions as well as safety of the human supervisor.

Enabling robotic adaptive behaviour capabilities for new Industry 4.0 automated quality inspection paradigms

The seamless integration of industrial robotic arms with server computers, sensors and actuators can revolutionise the way in which automated non-destructive testing (NDT) is performed and conceived. Achieving effective integration and realising the full potential of robotic systems presents significant challenges, since robots, sensors and end-effector tools are often not necessarily designed to be put together and form a holistic system. This paper presents recent breakthroughs, opening up new scenarios for the inspection of product quality in advanced manufacturing. Many years of research have brought to software platforms the ability to integrate external data acquisition instrumentation with industrial robots to improve the inspection speed, accuracy and repeatability of NDT. Robotic manipulators have typically been operated by predefined tool-paths generated through offline path-planning software applications. Recent developments pave the way to data-driven autonomous robotic inspections, enabling real-time path planning and adaptive control. This paper presents a toolbox with highly efficient algorithms and software functions, developed to be used through high-level programming language platforms (for example MATLAB, LabVIEW and Python) and/ or integrated within low-level language (for example C# and C++) applications. The use of the toolbox can speed up the development and the robust integration of new robotic NDT systems with real-time adaptive capabilities and is compatible with all KUKA robots with six degrees of freedom (DOF), which are equipped with the Robot Sensor Interface (RSI) software add-on. The paper describes the architecture of the toolbox and shows two application examples, where performance results are provided. The concepts described in the paper are aligned with the emerging Industry 4.0 paradigms and have wider applicability beyond NDT.

Speed Optimization for Incremental Updating of Grid-Based Distance Maps

In the context of robotics and game AI, grid-based Distance Maps (DMs) are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs’ application is how to improve the efficiency of updating the distance values when cell states are changed (i.e., changes caused by newly inserted or removed obstacles). To this end, this paper presents a novel algorithm to speed up the construction of DMs on planar, eight-connected grids. The novelty of our algorithm, Canonical Ordering Dynamic Brushfire (CODB), lies in two aspects: firstly, it only updates those cells which are affected by the changes; secondly, it employs the strategy of Canonical Ordering from the fast path planning community to guide the direction of the update; therefore, the construction requires much fewer cell visits and less computation costs compared to previous algorithms. Furthermore, we propose algorithms to compute DM-based subgoal graphs. Such a spatial representation can be used to provide high-level, collision-free roadmaps for agents with certain safety radius to engage fast and rational path planning tasks. We present our algorithm both intuitively and through pseudocode, compare it to competing algorithms in simulated scenarios, and demonstrate its usefulness for real-time path planning tasks.

Distributed Detect-and-Avoid for Multiple Unmanned Aerial Vehicles in National Air Space

Small unmanned aerial vehicles (UAVs) have the potential to revolutionize various applications in civilian domain such as disaster management, search and rescue operations, law enforcement, precision agriculture, and package delivery. As the number of such UAVs rise, a robust and reliable traffic management is needed for their integration in national airspace system (NAS) to enable real-time, reliable, and safe operation. Management of UAVs traffic in NAS becomes quite challenging due to issues such as real-time path planning of large number of UAVs, communication delays, operational uncertainties, failures, and noncooperating agents. In this work, we present a novel UAV traffic management (UTM) architecture that enables the integration of such UAVs in NAS. A combined A*–mixed integer linear programming (MILP)-based solution is presented for initial path planning of multiple UAVs with individual mission requirements and dynamic constraints. We also present a distributed detect-and-avoid (DAA) algorithm based on the concept of resource allocation using a market-based approach. The results demonstrate the scalability, optimality, and ability of the proposed approach to provide feasible solutions that are versatile in dynamic environments.

Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles.

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.