Robot Navigation In Unknown Environment Research Articles

A Hardware Realization Framework for Fuzzy Inference System Optimization

Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.

2021 IEEE RAS Winter School on Simultaneous Localization and Mapping in Deformable Environments [Education

Simultaneous localization and mapping (SLAM) is an important research problem for robot navigation in unknown environments, particularly when GPS is not available. SLAM requires a robot to be able to build a map of the environment in real time and simultaneously estimate its own location within the map. In the past two decades, significant progress has been made in the research for SLAM in static environments. However, when an environment has deformations, such as when a surgical robot is navigating in internal body environments, SLAM needs to build a time-varying 3D map of the soft tissues and estimate the location of the robot/sensor within the map. This poses a challenging problem since the robot/sensor is moving while the environment is deforming.

Development of Novel Average Neuro Fuzzy Hybrid Control Technique for Robot Navigation in Unknown Environments

The current research focuses on development and analysis of novel Average Neuro-Fuzzy Controller for path planning and navigation of mobile robot in highly cluttered environment. During the investigation various researches related to robot, control and navigation have been analysed. For mapping the environments several distance sensors mounted on the robot are used. The sensors readings about the environments have been segmented into various sectors (front, left, right and back sectors). Using the sensors reading robots negotiate with the obstacles present in the environments during navigation from start to goal point. Experimental and simulation results obtained during the current research from various exercises are in agreement and are within 3%. Comparisons between results show the effectiveness of the proposed technique for robot navigation in complex environments. This technique can be used to address various engineering optimisation problems.

Mobile Robot Navigation in Unknown Environment Using Sign Symbols and Scan Data

Mobile Robot Navigation in Unknown Environment Using Sign Symbols and Scan Data

A Hybrid Artificial Immune System for Mobile Robot Navigation in Unknown Environments

To solve the problem of obstacle avoidance for mobile robot in an unknown environment, an algorithm is proposed in this article which uses both the idiotypic network theory and clonal selection theory of the artificial immune system. While the former is used for navigation in general, the latter comes into effect during local minima situations. Also, a modified version of Farmer’s equation for Jerne’s idiotypic network model has been used to determine the antibody concentrations. The simulation results for navigation using the proposed algorithm are presented. The results proved that our mechanism is capable of successfully avoiding various types of obstacles including the local minima traps. A comparative study between the proposed algorithm and various other algorithms is also presented. Finally, experimental validation of the simulation results for the proposed algorithm is carried out using a physical robot.

Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

Abstract The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

Obstacle recognition and avoidance during robot navigation in unknown environment

In this paper, firstly, a model for robot navigation in unknown environment is presented as a Simulink model. This model is applicable for obstacles avoidance during the robot navigation. However, the first model is unable to recognize the re-occurrences of the obstacles during the navigation. Secondly, an advanced algorithm, based on the standard deviations of laser scan range vectors, is proposed and implemented for robot navigation. The standard deviations of the lasers scans, robot positions and the time of scans with similar standard deviations are used to obtain the obstacle recognition feature. In addition to the obstacle avoidance, the second algorithm recognizes the re-appearances of the obstacles in the navigation path. Further, the obstacle recognition feature is used to break the repetitive path loop in the robot navigation. The experimental work is carried out on the simulated Turtlebot robot model using the Gazebo simulator.

An extended Kalman filter-simultaneous localization and mapping method with Harris-scale-invariant feature transform feature recognition and laser mapping for humanoid robot navigation in unknown environment

This article proposes a novel method that combines real-time image recognition with choosing target object real-time localization in unknown environment. The position information of the object recognized as a target is provided by laser, which avoids collecting all the objects position information in the environment. This can save the computation resource and improve the real time. Furthermore, this article realizes the autonomous back and forth navigation in unknown environment and companies voice prompt and intelligent searching of the target object. This will set up a basis on building a complete environment map. Finally, the experiments show the validness of the proposed method and improve the intelligence of the environment information collection.

Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors

Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors

Improving mobile robot navigation by combining fuzzy reasoning and virtual obstacle algorithm

In this paper, a new approach is developed for contributing in solving the problem of autonomous mobile robot navigation in unknown environments. This approach is built upon combining fuzzy reasoning and virtual obstacle algorithm to overcome the local minimum problem encountered in presence of concave obstacles by efficiently coordinating priorities between multiple reactive behaviors such as goal reaching, obstacles avoiding, wall following and emergency situations preventing. To achieve this objective, an array of ultrasonic sensors is mounted on the mobile robot providing the distance information between the robot and obstacles. This distance information is used by the virtual obstacle algorithm to calculate some sub-goals for determining the good motion direction to avoid robot trap in local region (emergency situation), since the fuzzy reasoning is used for behavior control of the mobile robot. All the reactive behaviors are mapped into one universe of discourse to guarantee a smooth transition between them especially when the robot moves through closely spaced obstacles. In this manner, the robot oscillations are significantly reduced. Some simulation results are presented to show the ability of the developed approach in performing successfully in complex and uncertain environments.

Self-learning Mobile Robot Navigation in Unknown Environment Using Evolutionary Learning

Self-learning Mobile Robot Navigation in Unknown Environment Using Evolutionary Learning

Simple Image Processing Algorithms for Robot Navigation in Unknown Environment

This article deals with simple image processing algorithms which are used for navigation of the robot in unknown environment. In the beginning of the article image processing procedures used in these algorithms are defined. The transformation of coordinate system of camera to robot’s coordinate system is introduced. The main body of the article consists of the definition of L-K optical flow method used in proposed visual odometry system. Article also contains the parameter settings of the used methods. Emphasis in these algorithms has been placed on simplicity and speed, so that they can be carried out in real-time. Algorithms have been verified on several scenarios.

Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments

Design and Implementation of Neural Network Based Controller for Mobile Robot Navigation in Unknown Environments

Obstacle avoidance for mobile robot navigation in unknown environment using geometrical information of mobile camera images

In this paper, a novel and robust algorithm for avoiding obstacles in an unknown environment is proposed that utilises the geometrical analysis of the distances present in the images. A mobile camera with pan feature is used for increasing the field of view of the robot. Low level image properties red green blue colour; hue saturation value colour and brightness gradient are used for obstacle detection. Once the positions of the obstacles are determined in the image these are transformed into actual distances by the geometrical analysis and a safe passage is determined. The algorithm has been successfully implemented to avoid obstacles during navigation of a mobile robot. It has been observed that the proposed algorithm increases the robustness of obstacle avoidance and helps in the effective path planning of an autonomous mobile robot.

Calibration of RGB-D Sensors for Robot SLAM

This paper presents a calibration procedure for a Kinect RGB-D sensor and its application to robot simultaneous localization and mapping(SLAM). The calibration procedure consists of two stages: in the first stage, the RGB image is aligned with the depth image by using the bilinear interpolation. The distorted RGB image is further corrected in the second stage. The calibrated RGB-D sensor is used as the sensing device for robot navigation in unknown environment. In SLAM tasks, the speeded-up robust features (SURF) are detected from the RGB image and used as landmarks in the environment map. The depth image could provide the stereo information of each landmark. Meanwhile, the robot estimates its own state and landmark locations by mean of the Extended Kalman filter (EKF). The EKF SLAM has been carried out in the paper and the experimental results showed that the Kinect sensors could provide the mobile robot reliable measurement information when navigating in unknown environment.

Based on A* and Q-Learning Search and Rescue Robot Navigation

For the search and rescue robot navigation in unknown environment, a bionic self-learning algorithm based on A* and Q-Learning is put forward. The algorithm utilizes the Growing Self-organizing Map (GSOM) to build the environment topology cognitive map. The heuristic search A* algorithm is used the global path planning. When the local environment changes, Q-Learning is used the local path planning. Thereby the robot can obtain the self-learning skill by studying and training like human or animal, and looks for a free path from the initial state to the target state in unknown environment. The theory proves the validity of the method. The simulation result shows the robot obtains the navigation capability. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1605 Full Text: PDF

Reactive and the shortest path navigation of a wheeled mobile robot in cluttered environments

SUMMARYWe determine the shortest (minimal in length) path on a unicycle-like mobile robot in a known environment with smooth (possibly non-convex) obstacles with a constraint on curvature of their boundaries. Furthermore, we propose a new reactive randomized algorithm of robot navigation in unknown environment and prove that the robot will avoid collisions and reach a steady target with probability 1. The performance of our algorithm is confirmed by computer simulations and outdoor experiments with a Pioneer P3-DX mobile wheeled robot.

A novel reactive navigation strategy for mobile robots based on chaotic exploration and TTM self‐construction

PurposeThe purpose of this paper is to present a novel method for integrating of chaotic exploration and thinning‐based topological mapping to deal with the “traverse targets and return” problem applied for robot navigation in unknown environments. This new strategy can guarantee the robot stronger ability of exploring unknown environments, as well as recording and selecting optimal trajectory to return.Design/methodology/approachThe chaotic dynamic evolution of controlled multi‐scroll system is linked to the multi‐sensory perception and reactive behaviors of a mobile robot. The thinning‐based topological map (TTM), as the contextual layer of the cognitive system, is adopted to achieve the environmental recording in the process of robot exploration and navigation. Once the robot arrives at the terminal target via avoiding all the obstacles, the TTM has been built in real time. Based on the records in the topological map, a short and smooth point‐to‐point path is generated to achieve the exit from target and to move back to the starting point.FindingsThe simulation results confirmed that the proposed solution is suitable to resolve the robot's tasks of obstacle avoidance, target retrieving, and return, also has better performance than traditional strategies.Originality/valueThe presented novel method focuses integration of chaotic exploration and TTM self‐construction. The chaotic perception and control technique permits the robot to explore most of the environmental information within the smallest explored area. The introduced topological map, generated by applying a thinning algorithm, guarantees a short and smooth returning trajectory for the robot.

Exploration of 2D and 3D Environments using Voronoi Transform and Fast Marching Method

Robot navigation in unknown environments requires an efficient exploration method. Exploration involves not only to determine towards the robot must to move but also motion planning, and simultaneous localization and mapping processes. The final goal of the exploration task is to build a map of the environment that previously the robot didn't know. This work proposes the Voronoi Fast Marching method, that uses a Fast Marching technique on the Logarithm of the Extended Voronoi Transform of the environment's image provided by sensors, to determine a motion plan. The Logarithm of the Extended Voronoi Transform imitates the repulsive electric potential from walls and obstacles, and the Fast Marching Method propagates a wave over that potential map. The trajectory is calculated by the gradient method. The robot is directed towards the most unexplored and free zones of the environment so as to be able to explore all the workspace. Finally, to build the environment map while the robot is carrying out the exploration task, a SLAM (Simultaneous Localization and Modelling)algorithm is implemented, the Evolutive Localization Filter (ELF) based on a differential evolution technique. The combination of these methods provide a new autonomous exploration strategy to construct consistent maps of 2D and 3D indoor environments.

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autonomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh et al., 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.