Path Planning Module Research Articles

Massively parallel hybrid algorithm on embedded graphics processing unit for unmanned aerial vehicle path planning

To operate autonomously, military unmanned aerial vehicles (UAVs) must be equipped with a path planning module capable of calculating feasible trajectories. This is a highly complex and nonlinear optimisation problem that challenges state of the art methods. In this paper, we present a massively parallel hybrid algorithm to solve the path planning problem for fixed-wing military UAVs. The proposed solution combines the strengths of the genetic algorithm (GA) and the particle swarm optimisation and allows for the calculation of quasi-optimal paths in realistic 3D environments. To reduce the execution time, the proposed algorithm is parallelised on the NVIDIA Jetson TX1 embedded graphics processing unit (GPU). By exploiting the parallel architecture of the GPU, the runtime is reduced by a factor of 23.6× to just 4.3 seconds while requiring only 10 watts, making it an excellent solution for on-board path planning. The proposed system is tested in a simulation using 18 scenarios on six different terrains.

Modelling, Simulation and Mechatronics Design of a Wireless Automatic Fire Fighting Surveillance Robot

<span>The aim of this study is to design and develop an autonomous fire proof rescue robot. The robot is designed in such a way, that it can traverse through fire and hazardous situations. Further, it will sense and communicate information regarding these situations in real time with the server. The robot is fixed with multi-sensors and further, a driver circuit has been integrated for communication in these hazardous situations through Zigbee and a data acquisition system (DAQ). In mechanical design first, a 3D solid model is generated using Solid works software to understand the basic structure of robot which provides information regarding robotic platform, size and location of various components. The developed fire fighting robot is a predominately outdoor ground-based mobile robotic system with onboard subdual systems that can traverse autonomously in the hazardous environment. The robot is designed such that it can traverse into the fire and send information regarding the fire behaviour and also the images of the victim’s location by using a camera. Further, a mathematical model which describes the kinematics and dynamic behaviour of robot motion are done. V-REP is used to create the simulation of the robot in a fire simulated fire environment. Finally, for the path planning, various techniques are discussed such as V-REPs inbuilt path planning module, A*, Fuzzy logic and artificial potential fields.</span>

Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

The introduction of proportional-integral-derivative (PID) controllers into cooperative collision avoidance systems (CCASs) has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization (PSO) algorithm, and to better manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed (≤50 km/h) and high speed (≥100 km/h) cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

The fast marching method based intelligent navigation of an unmanned surface vehicle

Unmanned surface vehicles (USVs) have obtained increasing interests in recent decades. Because of the features of improved mission efficiency and decreased resource costs, applications of USVs can been seen in both civilian and naval areas. In order to efficiently and effectively achieve missions without any human intervention, a robust and intelligent navigation, guidance and control (NGC) system is vital for USV. This paper has therefore presented a novel NGC system designed for a USV named Springer. The system is developed by integrating multiple functional modules, which include a reliable navigation module that provides reliable position and heading information, a robust autopilot module enabling Springer tracking well the waypoints and an intelligent path planning module that is capable of generating feasible and practical waypoints. The path planning algorithm has been developed based upon the angle guidance fast marching square method, which is able to calculate the optimal path according to vehicle's motion constraints. The designed NGC system has been validated in both real field trials and computer based simulations proving that Springer USV is able to autonomously navigate in different maritime environments with the guidance of the NGC system.

Indoor localization and navigation using smartphone sensory data

In the cloud age, it is quite easy to collect sensory data from smartphones. With these sensory data, it is desired to provide various kinds of applications to serve the user. In this research, we aim at developing an indoor navigation system on smartphone using solely smartphone sensory data. There are many researches on indoor localization and navigation in the literature. Nevertheless, environmental sensors and/or wearable sensors are usually needed. This can be costly and inconvenient. In this paper, we propose a smartphone indoor localization system using only accelerometer and gyroscope data from the smartphone. The Pedestrian Dead Reckoning (PDR) approach is used to build this system. The PDR approach is simple and efficient though seems traditional. The major weakness of the PDR is that the estimation error would accumulate over time. Thus we propose to add so-called calibration marks which look like short arrows and are placed on both the floor plan and the ground. To use the system, the user first finds a calibration mark on the ground, stands on it and faces the right direction. He/she then moves the android icon (representing the user) on top of the calibration mark on the floor plan on the smartphone. When the user starts to move, the android icon also moves on the floor plan following the real-time estimation of step length and moving direction change for each step from accelerometer and gyroscope data. This is a prototype of an indoor navigation system that can become fully functional after an optimal path planning module is included. Experimental results of estimated walking trace tests show high accuracy. The system is promising and useful as long as a floor plan and calibration marks are built in advance.

A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments

In this study, a low cost, flexible and modular structure is proposed for mobile robot motion planning systems in an indoor environment with obstacles. In this system, the mobile robot has to follow the shortest path to the target avoiding obstacles. It is designed as three main modules including image processing, path planning and robot motion blocks. These modules are embedded on a single board computer. In the image processing module, the image of the indoor environment, including a mobile robot, obstacles and a target, having different colors is taken to the single board computer with a wireless IP camera. This image is processed to find the locations of the mobile robot, obstacles and the target in C programming language using OpenCV. In path planning module, the shortest and optimal path is generated for the mobile robot. Generated path is applied to the robot motion module to produce necessary angles and distances for the mobile robot to reach the target. Since the structure of the proposed system is designed as modular and flexible, similar or different hardware, software or methods can be applied to these three modules.

Simulation research on emergency path planning of an active collision avoidance system combined with longitudinal control for an autonomous vehicle

Autonomous vehicle technology is greatly valued nowadays, and an active collision avoidance system is one of the key parts for autonomous driving. This study presents a comprehensive architecture of an active collision avoidance system for an autonomous vehicle, which is integrated with a decision-making module, a path-planning module, a lateral-path-following module and a fuzzy adaptive following module (longitudinal motion) to deal with potential hazards on a straight road or a curved road. In order to make the planned path for overtaking manoeuvres safer, an improved harmonic velocity potential approach for path planning is presented, which innovatively enhances the effect of an obstacle potential on a road by adding a scale term, so that it can generate a smooth path for a vehicle-overtaking manoeuvre. All the potentials which are used for vehicle lane keeping or lane changing are well designed. The lateral-path-following module is based on the constrained linear model predictive control approach, which ensures that the host vehicle can follow the planned path precisely. Furthermore, when the overtaking manoeuvre is not suitable, the fuzzy adaptive following module is utilized to ensure that the host vehicle can adaptively keep a safe distance from the preceding vehicle. Tactical decisions, such as overtaking, accelerating or decelerating, are determined by the decision-making module. Finally, several typical scenarios with low traffic on a straight road or a curved road are simulated to verify the effectiveness and the feasibility of the active collision avoidance system. The simulation results show that the host vehicle can make a successful collision avoidance manoeuvre without the intervention of a human driver in different situations.

An autonomous stereovision-based navigation system (ASNS) for mobile robots

Recently, stereovision has appeared in robotics as a source of information for real-time mapping and path planning. In this paper, an intelligent motion system for mobile robots is designed and implemented using stereovision. The proposed system uses stereovision as a primary method for sensing the environment, and the system is able to navigate intelligently in an indoor environment with varying degrees of obstacle complexity. It creates noiseless and high-confidence 3D point clouds and uses these point clouds as an input for the mapping and path-planning modules. The proposed system was built by developing, enhancing, and integrating various techniques, modules and algorithms. The Stereovision-based Path-planning module is the integration of three main enhanced techniques: (1) the multi-baseline multi-view stereovision filter (MMSVF), (2) accurate floor detection and segmentation (AFDS), and (3) the intelligent gazing module (IGM). This Stereovision-based Path planning (MMSVF, IGM, and AFDS) was integrated with the Fuzzy Logic Motion Controller (FLMC). All techniques, modules and algorithms are implemented using a multi-threaded and client---server-based architecture. To prove the viability and robustness of our proposed system, we have integrated all components of the system into a fully functional mobile robot navigation system. We compared the performance of the main modules with that of similar modules in the literatures, and showed that our modules had better performance. Testing the whole system is more important than just testing each module individually. To the best of our knowledge, the literatures lack such testing. Hence, in this paper we present the performance of our complete integrated system in different environments using different parameters and different architectures.

Development and Research of Path-Planning Module for Control System of Underwater Vehicle

Development and Research of Path-Planning Module for Control System of Underwater Vehicle

Multi-agent based path planning for first responders among moving obstacles

Natural or man-made disasters can cause different kinds of moving obstacles (e.g., fires, plumes, floods), which make some parts of the road network temporarily unavailable. After such incidents occur, responders have to go to different destinations to perform their tasks in the environment affected by the disaster. Therefore they need a path planner that is capable of dealing with such moving obstacles, as well as generating and coordinating their routes quickly and efficiently. In this paper, we present a novel approach for using a multi-agent system for navigating one or multiple responders to one or multiple destinations in the presence of moving obstacles. Our navigation system supports information collection from hazard simulations, spatio-temporal data processing and analysis, connection with a geo-database, and route generation in dynamic environments affected by disasters. We design and develop a set of software geospatial agents that assist emergency actors in dealing with the spatio-temporal data required for emergency navigation, based on their roles in the disaster response. One of the key components of the system is the path planning module, which combines the modified A* algorithm, insertion heuristics, and auction algorithm to calculate obstacle-avoiding routes for multiple responders with multiple destinations. A spatial data model is designed to support the storage of information about the tasks and routes produced during the disaster response. Our system has been validated using four navigation cases. Some preliminary results are presented in this paper and show the potential of the system for solving more navigation cases.

Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning

A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially adaptive manner such as respecting comfort zones of the pedestrians. We propose a framework for socially adaptive path planning in dynamic environments, by generating human-like path trajectory. Our framework consists of three modules: a feature extraction module, inverse reinforcement learning (IRL) module, and a path planning module. The feature extraction module extracts features necessary to characterize the state information, such as density and velocity of surrounding obstacles, from a RGB-depth sensor. The inverse reinforcement learning module uses a set of demonstration trajectories generated by an expert to learn the expert’s behaviour when faced with different state features, and represent it as a cost function that respects social variables. Finally, the planning module integrates a three-layer architecture, where a global path is optimized according to a classical shortest-path objective using a global map known a priori, a local path is planned over a shorter distance using the features extracted from a RGB-D sensor and the cost function inferred from IRL module, and a low-level system handles avoidance of immediate obstacles. We evaluate our approach by deploying it on a real robotic wheelchair platform in various scenarios, and comparing the robot trajectories to human trajectories.

Autonomous Reverse Parking System Based on Robust Path Generation and Improved Sliding Mode Control

Some commercial vehicle models have been equipped with semiautonomous parking systems to a certain extent. However, gaps to fully automated solutions still exist, and cost considerations further constrain their acceptance among consumers. This paper proposes a low-cost vision-based approach to a fully self-reverse parking system. It consists of four key modules: a novel path-planning module ensures that a feasible path is available under any initial poses, which frees human intervention completely; a modified sliding mode controller on the steering wheel is designed for path following; image processing with Kalman state prediction provides consistent and real-time estimation on the vehicle pose; and a robust overall control scheme ensures that the vehicle can accurately park along the slot center line without intrusion into adjacent slots. Experimental results based on 216 on-field tests under different illumination conditions showed that the proposed system was able to accurately and consistently park the vehicle in all cases with a 4.71-cm RMS offset distance from the center line and a 1.24 $^{\circ} $ RMS orientation deviation. With its easy setup and excellent performance, this system can be practically and robustly implemented to existing vehicles with minimal additional cost.

Guidance of AUVs for Autonomous Underwater Inspection

Abstract In this paper, a task-based online path-planning and guidance system of underactuated unmanned underwater vehicles (UUVs) for inspection of underwater infrastructure is described. Whether there are harbor submerged structures, cables, pipes, hydro-technical works, they all need to be checked in relation to structural deterioration specific potential problems such as scour, erosion, and accelerated local corrosion problems. At the highest level, the mission controller handles different event types, permitting also the execution of mission tasks. The online path-planning module generates a set of waypoints based on an environmental perception module for the AUV to follow the target object with a constant distance. The guidance method actually controls the vehicle to follow the waypoints, assuming the relative position and attitude of the vehicle is precisely estimated in real-time. By combining these methods, an AUV can perform fully autonomous inspection of underwater structures. The system was evaluated with success for several harbor and pipeline inspection scenarios in a test basin and sea trials.

The Research of Path Planning Technology during Virtual Assembly Based on CREO Software

Based on CREO Parametric 2.0, programmed by Pro/TOOLKIT and used Visual Studio as a developing platform, the virtually loaded path-planning module targets the dismantlement of tooling spring pin as an investigated object, thus enhancing the efficiency of virtual device significantly.

A Hierarchical Fuzzy Control Design for Indoor Mobile Robot

This paper presents a motion control for an autonomous robot navigation using fuzzy logic motion control and stereo vision based path-planning module. This requires the capability to maneuver in a complex unknown environment. The mobile robot uses intuitive fuzzy rules and is expected to reach a specific target or follow a prespecified trajectory while moving among unforeseen obstacles. The robot's mission depends on the choice of the task. In this paper, behavioral-based control architecture is adopted, and each local navigational task is analyzed in terms of primitive behaviors. Our approach is systematic and original in the sense that some of the fuzzy rules are not triggered in face of critical situations for which the stereo vision camera can intervene to unblock the mobile robot.

Geospatial analysis based on GIS integrated with LADAR

In this work, we describe multi-layered analyses of a high-resolution broad-area LADAR data set in support of expeditionary activities. High-level features are extracted from the LADAR data, such as the presence and location of buildings and cars, and then these features are used to populate a GIS (geographic information system) tool. We also apply line-of-sight (LOS) analysis to develop a path-planning module. Finally, visualization is addressed and enhanced with a gesture-based control system that allows the user to navigate through the enhanced data set in a virtual immersive experience. This work has operational applications including military, security, disaster relief, and task-based robotic path planning.

Designing and Realizing of UAV Simulation System

This paper discusses an Unmanned Aerial Vehicle simulation system design and implementation, which focuses on the system design, system management, software development, and the information flow management of the whole system. To simulate battlefield environments, this system provides a clear and visual expression way for complex system design. Besides, it provides services for the design, integration testing, experiment, verification and evaluation of complex system. The main tasks of the system include: establish flight control simulation model; construct three-dimensional visual environment model and three-dimensional entity model; In the case of aircraft loading, drive various kinds of simulation modules, use related model of aircraft task / path planning module; display the typical use of aircraft system visually, as combining with three-dimensional visual simulation environment. Based on VC++ and MultiGen Creator/Vega, this simulation system probes into three-dimensional modeling technology and visual simulation-driven. It proposes general techniques route, improves work efficiency and has a wide range of application.

Towards a Stable Robotic Object Manipulation Through 2D-3D Features Tracking

In this paper, a new object tracking system is proposed to improve the object manipulation capabilities of service robots. The goal is to continuously track the state of the visualized environment in order to send visual information in real time to the path planning and decision modules of the robot; that is, to adapt the movement of the robotic system according to the state variations appearing in the imaged scene. The tracking approach is based on a probabilistic collaborative tracking framework developed around a 2D patch-based tracking system and a 2D-3D point features tracker. The real-time visual information is composed of RGB-D data streams acquired from state-of-the-art structured light sensors. For performance evaluation, the accuracy of the developed tracker is compared to a traditional marker-based tracking system which delivers 3D information with respect to the position of the marker.

A Digital System Implement for Aircraft Sheet Metal Cutting

For aircraft sheet metal parts digital cutting, this paper uses IDEF0 to structure a digital system integrated with unfolding module, nesting module, cutting path planning module and NC cutting module, then analyzes data organization and process of the system application. In the system, an algorithm for the development of a doubly curved surface onto a plane is adopted, NC cutting process-oriented nesting algorithm is presented and rules of cutter path planning is developed. Furthermore, application examples of key technologies (unfolding, nesting, path cutter planning) are demonstrated and indicate that the system can realize digital cutting for aircraft sheet metal parts quickly and precisely.

Navegación Autónoma Asistida Basada en SLAM para una Silla de Ruedas Robotizada en Entornos Restringidos

In this work, an interface specially designed for a robotic wheelchair’s navigation within confined spaces is presented. The interface performance is based on two modus: autonomous and non-autonomous. The non-autonomous execution of the robotic wheelchair interface is performed by means of a joystick. The joystick is adapted to the wheelchair’s patient capabilities and it governs the motion of the vehicle within the environment. The autonomous modus of the robotic wheelchair is executed when the user has to turn a given angle within the environment. The turning strategy is performed by means of both: a maneuverability algorithm which is compatible with the wheelchair’s kinematics and the SLAM (Simultaneous Localization and Mapping) algorithm. In addition, the autonomous modus is composed by two modules: a path planning module and a control module. The path planning module uses the map information provided by the SLAM algorithm to generate a safe path compatible with the robotic wheelchair. Such path will allow the vehicle to reach the orientation -angle of turning- given by the user. The control module governs the motion of the robotic wheelchair by means of a trajectory controller when following the path generated by the path planning algorithm. The controller references are updated by the SLAM estimation of the wheelchair’s pose within the environment. Experimental results using a real robotic wheelchair are also shown in this work.