A new active wave computing based real time mobile robot navigation algorithm for dynamic environment

Research in autonomous mobile robots is gaining much more attention in recent years, particularly in coordinating rescue missions and inspections of affected structures within disaster zones. It is the aim of this paper to contribute towards such advancements by introducing mobile robots navigation in virtually generated rescue mission environments. The randomly generated missions are mapped to real environments hosting mobile robots, which can unrestrictedly move in any open surroundings, without the need for the physical obstacles presence. To achieve this, a GUI was developed to randomly create missions of different sizes and complexities. The GUI offers the developer the choice of automatically generating such missions, edit them and/or create them. The robots may be programmed by various solving algorithms to complete the course and find a solution. The advantage of this approach is that it offers environment and robot real-time merging, robot performance tracking and rapid (on-the-fly) algorithms development. In this paper, the rescuing robot will follow an embedded Wall-Following algorithm.

Evolutionary artificial potential field (EAPF) functions are utilized for mobile robot navigation in a microrobot soccer (MiroSot) environment. In a micro-robot soccer system the robots are monitored using an overhead CCD Camera, making it suitable for real time application of the EAPF functions. The effectiveness of the EAPF functions in real time mobile robot navigation are verified through experimentation. The EAPF functions proposed were tested in different scenarios related to ball tracking and ball kicking, while facing competition from other robots.

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

In this paper , a Novel Cellular Neural Network (CNN) entitled the shortest path CNN (SP-CNN) is proposed. It has a good performance in path planning for mobile robots because of its network structure and neural dynamics. The proposed method not only can generate the best solution in static environments in real time but also generate the optional solution in dynamic environments or in unknown environments according to its currently acquired navigation map. Extensive simulations about the above mentioned aspects demonstrate the effectiveness of the proposed approach.

When a robot manipulator performs tasks in a dynamic unstructured environment, it not only should account for where and how it should move, but also ensure that the robot can effectively avoid collisions with static and moving obstacles. Planning in absolutely unstructured and dynamic environment is much more challenging, up-to-date there is no universal solution to this problem. In this paper, we believed the concept that for motion planning in real dynamic environments, usually there is only a part of a path invalidated by obstacles, and only this part needs replanning. Thus, in this paper a practical strategy was presented for motion planning in dynamic environments. First, a variant dynamic Rapidly-exploring Random Tree (RRT) planner was designed to combine global planning with local replanning. Then, the proposed dynamic RRT algorithm was implemented under Robotic Operation System (ROS) framework and applied to a 7-degree-of-freedom manipulator. Experimental results have shown that the proposed dynamic RRT planner can plan and execute movement of robot manipulator between a starting and target position while preserving task constraints and reacting to environment changes in real time.

This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target's position. We propose a gradient-based control approach that encompasses the three major optimum experimental-design criteria and relies on the Kalman filter for estimation fusion. We derive analytical lower and upper bounds on the target's position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of sensors' accuracy and target's dynamics on the steady-state performance of our coordination algorithm. Finally, in the case that the position of the vehicles is not perfectly known, we introduce a more challenging problem, termed Active Cooperative Localization and Multi-target Tracking (ACLMT). In this problem, the vehicles move in the 3-D space in order to maximize the accuracy of their own position estimate and that of multiple moving targets.

As the indoor dynamic target localization does not detect and repair the circumferential jump value in time, which leads to large position and attitude errors and low‐velocity stability, a combined Global Navigation Satellite System/Simultaneous Localization and Mapping‐ (GNSS/SLAM‐) based high‐precision indoor dynamic target localization method is proposed. The method uses Empirical Mode Decomposition (EMD) to decompose the noisy signal, obtains the noise energy as the dominant mode from the decomposed components, extracts the useful signal energy as the main dividing point, removes the high‐frequency signal, constructs the low‐frequency component to realize low‐pass filtering and denoising, selects a suitable threshold processing function to make the high‐frequency signal component retain the detailed signal effectively to realize high‐frequency component denoising, detects and fixes the circumferential jump of the observed data, and detects and fixes the circumferential jump of each frequency. The indoor dynamic target positioning method is established by combining GNSS/SLAM to achieve high accuracy target positioning. The experimental results show that the position and attitude errors are small, and the velocity is stable, which indicates that the position information is closer to the dynamic target, i.e., the target positioning is more accurate. To address the problems of scale drift and frequent initialization due to environmental factors in monocular vision SLAM, an Ultra Wideband (UWB)/vision fusion localization model that takes into account the scale factor is proposed, which can give full play to the complementary characteristics between UWB and vision; solve the problems of visual initialization, scale ambiguity, and absolute spatial reference; and improve UWB localization accuracy and localization frequency as well as reducing the number of base stations. The model can reliably achieve 0.2 m accuracy in indoor environments with sparse texture or frequent light changes.

Robot navigation in dynamic unknown environments is a challenging issue in the field of autonomous mobile robot control. This paper presents a hybrid robust method for navigating an industrial robot in an environment that contains dynamic obstacles. The objectives are to find the shortest path, to minimize the energy consumption of robot, to make the smoothness of the generated paths and to tackle dynamic obstacles. Robots employed in industrial environments demand considerable autonomy and require high level of accuracy and manoeuvrability at the same time. Besides, no collision is tolerable along the way. A single-objective optimization method based on path criteria fails to satisfy all of the requirements. This paper proposes a hybrid algorithm including the whale optimization algorithm (WOA) for path planning, a learnable function approximation network for making smoothness of the generated paths and a fuzzy logic controller to avoid obstacle collision. In this algorithm, WOA optimizes the best path to be taken from the start to goal position. Once a sequence of points is candidate and segments of path are merged, a radial basis function is trained to provide a smooth movement path in the dynamic environment while trying to maximize the safety margin. To further improve the safety of navigation, a fuzzy-based obstacle avoidance algorithm is executed when the robot is placed in the vicinity of an obstacle. Fuzzy decisions are made based on values of distance information. The proposed hybrid method for path planning and obstacle avoidance issues was implemented and evaluated in dynamic environments including specific shaped obstacles. A GUI-based simulation platform was designed in Matlab environment for testing the proposed algorithm. Implementation results indicate that the proposed algorithm has yielded in smooth non-marginal goal-directed navigation with acceptable performance metrics. Meanwhile, collisions to dynamic obstacles were adaptively and non-rigidly avoided. Such a model-free hybrid algorithm for path planning and obstacle avoidance can improve autonomy in industrial operation and decrease computational complexity.

Ego-motion estimation, as one of the core technologies of unmanned systems, is widely used in autonomous robot navigation, unmanned driving, augmented reality and other fields. With the development of computer vision, there has been considerable interest in ego-motion estimation with visual navigation. One of the core technologies in Visual navigation is using the matching feature points between consecutive image frames to estimate pose. Since the feature-based method performed under the assumption of a static environment, it susceptive to the dynamic targets. Visual navigation in the dynamic environment has become an important research issue. This paper proposed a practical and robust features selection algorithm of visual navigation which avoids using the feature points on dynamic objects. Firstly, according to the instance segmentation of deep neural network, the objects are classified into potential dynamic and static categories. Subsequently, the matching features on the potential moving objects are used to update vehicle state respectively, meanwhile, the relevant reprojection error of other feature points on the background could be calculated. Eventually, the result of whether the target is moving or not will be judged by the reprojection error, and the features on dynamic targets are removed. To illustrate the effectiveness of the features selection method in the dynamic environment, the proposed algorithm is merged into an MSCKF based on tri-focal tensor geometry, and it has been evaluated in a public dataset. Experimental results demonstrated the effectiveness of the proposed method.

Most existing vision‐based simultaneous localization and mapping systems and their variants still assume that the observation is absolutely static and cannot work well in dynamic environments. In this paper, we propose a direct geometrically constrained SLAM method based on target detection and depth image segmentation, named YGDD‐SLAM. The YGDD‐SLAM system can work robustly, accurately, and continuously in highly dynamic environments. The method first acquires static and potential dynamic feature points in the current frame through a target detection network. Then, dynamic targets are identified by combining the geometric change relationship between static and potential dynamic feature points between adjacent frames. To improve the accuracy of the dynamic judgment, the motion probability of the potential dynamic target in the past few frames is also used for judgment. Subsequently, the dynamic object regions at the pixel level are segmented out based on the double‐peak feature of the gray‐scale histogram of the dynamic target region in the depth image, which ultimately achieves the accurate deletion of all dynamic features points. Meanwhile, we validate YGDD‐SLAM on TUM data set and Bonn data set and prove that it significantly improves the localization accuracy and system stability in different types of dynamic environments.

For robots to become an efficient means for performing tasks, they need to navigate safely and effectively without supervision. This chapter is devoted to the problem of fuzzy control system design for mobile robots operating in dynamic environments with obstacles. The decision-making method, based on the transformation of radar-sensor information to fuzzy set “Obstacles” and robot’s heading course to fuzzy set “Direction to Target Point,” is considered. We also address the safety aspects of the proposed approach. The different scenarios of the working environment (with static and dynamic obstacles), the adjusted speed of the autonomous robot, and peculiarities of “robot-obstacle” interaction are the issues for discussion. The simulation results confirm the efficiency and safety of the robot’s autonomous navigation in dynamic and unknown environments.

In this paper, we propose an extended navigation framework for autonomous mobile robots in dynamic environments using a reinforcement learning algorithm. The main idea of the proposed algorithm is to provide the mobile robots the relative position and motion of the surrounding objects to the robots, and the safety constraints such as minimum distance from the robots to the obstacles, and a learning model. We then distribute the mobile robots into a dynamic environment. The mobile robots will automatically learn to adapt to the environment by their own experienced through the trial-and-error interaction with the surrounding environment. When the learning phase is completed, the mobile robots equipped with our proposed framework are able to navigate autonomously and safely in the dynamic environment. The simulation results in a simulated environment shows that, our proposed navigation framework is capable of driving the mobile robots to avoid dynamic obstacles and catch up dynamic targets, providing the safety for the surrounding objects and the mobile robots.

A two-dimensional (2-D) cellular neural network (CNN) array of resistively coupled Chua circuits which can be designed to implement some elementary aspects of spatial recognition, namely, distinguishing open curves from closed ones and locating the shortest path between two locations, is described. In the latter, two situations are analyzed: flat and wrinkled surfaces. The 2-D CNN array of Chua circuits is shown to be capable of finding the shortest path between two points on a wrinkled labyrinth. The performance of this parallel processing approach was examined using computer simulations, although this method can be implemented in real time via VLSI technology. >

SummaryIn order to enable the multi‐agents to elliptically circumnavigate the multi‐targets in more complex environment, we propose a geometric center estimator and an elliptical circumnavigation controller in two‐dimensional space by only employing bearing measurements without knowing the target's position and velocity. The stability of the algorithm is proved for both stationary targets and dynamic targets. Finally, a series of numerical simulations is presented to verify the correctness of the algorithm both in ideal networks and in networks with communication delays.

A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm. Index Terms— Real-time computer control, Mobile robot, Multi-agent system, Path planning algorithms.