Mobile Robot Obstacle Avoidance Research Articles

Minimum-Time Trajectory Planning for a Differential Drive Mobile Robot Considering Non-slipping Constraints

We propose a real-time minimum-time trajectory planning strategy with obstacle avoidance for a differential-drive mobile robot in the context of robot soccer. The method considers constraints important to maximize the system’s performance, such as the actuator limits and non-slipping conditions. We also present a novel friction model that regards the imbalance of normal forces on the wheels due to the acceleration of the robot. Theoretical guarantees on how to obtain a minimum-time velocity profile on a predetermined parametrized curve considering the modeled constraints are also presented. Then, we introduce a nonlinear, non-convex, local optimization using a version of the Resilient Propagation algorithm that minimizes the time of the curve while avoiding obstacles and respecting system constraints. Finally, employing a new proposed benchmark, we verified that the presented strategy allows the robot to traverse a cluttered field (with dimensions of 1.5 m $$\times $$ 1.3 m) in 2.8 s in 95% of the cases, while the optimization success rate was 85%. We also demonstrated the possibility of running the optimization in real-time, since it takes less than 13.8 ms in 95% of the cases.

Review of mobile robots obstacle avoidance, localization, motion planning, and wheels

<span>The main objective of this research is to study the obstacle avoidance, Monte Carlo Localization (MCL) method, motion planning in dynamic networks for mobile robots, and mobile robots wheels depending on the previous published researches. The researchers had done their experiments on different mobile robots and had validated them. This research helps the readers to learn how the robot changes its directions to prevent itself from collisions depending on three ultrasonic sensors. Also, they will learn the localization of the mobile robots depending on the recorded data from RHINO and MINERVA robots. In addition to learning the obstacle avoiding and the localization of mobile robots, the readers will learn new planning framework. Furthermore, they will get knowledge in types of mobile robots wheels.</span>

State Dependent Riccati Equation (SDRE) controller design for moving obstacle avoidance in mobile robot

In this paper, a new navigating method for a non-holonomic wheeled moving robot in a dynamic environment with the moving obstacles is proposed. This method is indeed the combination of the nonlinear optimal controller based on State-Dependent Riccati Equation (SDRE) and an obstacle avoidance algorithm named artificial potential field (APF). The corresponding cost function of the SDRE is obtained of APF algorithm. APF algorithm forces the robot to approach the target as an attracting (low-potential) point and to get away from the obstacle as a repulsing (high-potential) point. This method calculates the best path from origin to destination which also implicitly guaranties the stability. The obtained path is the best according to the both amount of traveled distance and input energy. Moreover, this approach not only avoid both fixed and moving obstacle, but also create a smooth path in presence of them. Keeping the nonlinear structure of the system instead of eliminating them during the linearization process is the advantage of SDRE method. Here, the robot navigation is done in the presence of the three different movements of obstacle: (1) fixed speed, (2) fixed acceleration and (3) non-uniform circular. The represented simulation results indicate a suitable performance of the proposed algorithm.

Research on Trajectory Tracking and Obstacle Avoidance of Nonholonomic Mobile Robots in a Dynamic Environment

This paper presents an obstacle-avoidance trajectory tracking method based on a nonlinear model prediction, with a dynamic environment considered in the trajectory tracking of nonholonomic mobile robots for obstacle avoidance. In this method, collision avoidance is embedded into the trajectory tracking control problem as a nonlinear constraint of the position state, which changes with time to solve the obstacle-avoidance problem in dynamic environments. The CasADi toolkit was used in MATLAB to generate a real-time, efficient C++ code with inequality constraints to avoid collisions. Trajectory tracking and obstacle avoidance in dynamic and static environments are trialed using MATLAB and CasADi simulations, and the effectiveness of the proposed control algorithm is verified.

Coupled fractional-order sliding mode control and obstacle avoidance of a four-wheeled steerable mobile robot

Recently, four-wheeled steerable mobile robots (FSMR) have attracted increasing attention in industrial fields, however the collision-free trajectory tracking control is still challenging in dynamic environments. This paper studies a new coupled fractional-order sliding mode control (CFSMC) and obstacle avoidance scheme, which has superior capacities of providing more control flexibilities and achieving high-accuracy. Instead of exploring traditional integer-order solutions, novel fractional-order sliding surfaces are proposed to handle the nonlinear interconnected states in a coupled structure. To accomplish non-oscillating avoidance of both stationary and moving entities within an uncertain workspace, a modified near-time-optimal potential function is subsequently presented with improved efficiency and reduced collision-resolving distances. By utilizing fuzzy rules, proper adaption gains of the reaching laws are designed to degenerate the effect of undesired chattering. The asymptotic stability and convergence can be guaranteed for the resultant closed-loop system. Three experiments are implemented on a real-time FSMR system. The results validate the reliability of the presented CFSMC scheme in terms of significantly mitigated following errors, faster disturbance rejection and smooth transition as compared to conventional methods.

Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.

Crash course learning: an automated approach to simulation-driven LiDAR-basedtraining of neural networks for obstacle avoidance in mobile robotics

This paper proposes and implements a self-supervised simulation-driven approach to data collection used for training of perception-based shallow neural networks for mobile robot obstacle avoidance. In the approach, a 2D LiDAR sensor was used as an information source for training neural networks. The paper analyzes neural network performance in terms of numbers of layers and neurons, as well as the amount of data needed for reliable robot operation. Once the best architecture is identified, it is trained using only data obtained in simulation and then implemented and tested on a real robot (Turtlebot 2) in several simulations and real-world scenarios. Based on obtained results it is shown that this fast and simple approach is very powerful with good results in a variety of challenging environments, with both static and dynamic obstacles.

Machine Vision-based Obstacle Avoidance for Mobile Robot

Obstacle avoidance for mobile robots, especially humanoid robot, is an essential ability for the robot to perform in its environment. This ability based on the colour recognition capability of the barrier or obstacle and the field, as well as the ability to perform movements avoiding the barrier, detected when the robot detects an obstacle in its path. This research develops a detection system of barrier objects and a field with a colour range in HSV format and extracts the edges of barrier objects with the FindContoure method at a threshold filter value. The filter results are then processed using the Bounding Rect method so that the results are obtained from the object detection coordinate extraction. The test results detect the colour of the barrier object with OpenCV is 100%, the movement test uses the processing of the object's colour image and robot direction based on the contour area value> 12500 Pixels, the percentage of the robot making edging motion through the red barrier object is 80% and the contour area testing <12500 pixel is 70% of the movement of the robot forward approaching the barrier object.

A Mobile Robot Path Planning Scheme for Dynamic Environments

This paper proposes a four-direction search method for obstacle avoidance of mobile robots, and the collision energy of obstacles is modeled based on the neural network. For comparison and discussion purposes, the different invariant step lengths are tested in the static environment. To take the advantages of the large and small step lengths effectively, a variable step length method is adopted to improve the performance, such as less iteration number and lower total energy. The variable step length method is also applied to the dynamic environment to explore the real-time performance for path planning. Simulation results demonstrate the effectiveness and practicability of the presented scheme.

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.

Targeting Posture Control With Dynamic Obstacle Avoidance of Constrained Uncertain Wheeled Mobile Robots Including Unknown Skidding and Slipping

This article proposes a targeting posture control approach with dynamic obstacle avoidance of differential-drive wheeled mobile robot (WMR) systems in the presence of unknown skidding, slipping, input disturbances, model uncertainties, and torque saturation. First, a nonlinear model predictive control (NMPC) scheme is presented to generate a feasible trajectory from a starting posture to a targeting posture where dynamic obstacles in the environment and physical constraints of the robots are considered. Second, a robust virtual control law at the kinematic level is introduced for the robots to follow the trajectory. Third, taking the consideration of the unknown skidding, slipping, input disturbances, and model uncertainties in the dynamic model being lumped as a total disturbance, which is estimated by the linear extended state observer (LESO), a disturbance compensation-based saturation controller is designed to make the real velocity of the robot converge to the virtual velocity command. Finally, the effectiveness of the proposed control strategy is verified by simulation results.

An Experience Aggregative Reinforcement Learning With Multi-Attribute Decision-Making for Obstacle Avoidance of Wheeled Mobile Robot

A variety of reinforcement learning (RL) methods are developed to achieve the motion control for the robotic systems, which has been a hot issue. However, the performance of the conventional RL methods often encounters a bottleneck, because the robots have difficulty in choosing an appropriate action in the control task due to the exploration-exploitation dilemma. To address this problem and improve the learning performance, this work introduces an experience aggregative reinforcement learning method with a Multi-Attribute Decision-Making (MADM) to achieve the real-time obstacle avoidance of wheeled mobile robot (WMR). The proposed method employs an experience aggregation method to cluster experiential samples and it can achieve more effective experience storage. Moreover, to achieve the effective action selection using the prior experience, an action selection policy based on a Multi-Attribute Decision-Making is proposed. Inspired by the hierarchical decision-making, this work decomposes the original obstacle avoidance task into two sub-tasks using a divide-and-conquer approach. Each sub-task is trained individually by a double Q-learning using a simple reward function. Each sub-task learns an action policy, which enables the sub-task to selects an appropriate action to achieve a single goal. The standardized rewards of sub-tasks are calculated when fusing these sub-tasks to eliminate differences in rewards for sub-tasks. Then, the proposed method integrates the prior experience of three trained sub-tasks via an action policy based on a MADM to complete the source task. Simulation results show that the proposed method outperforms competitors.

A sEMG-Based Shared Control System With No-Target Obstacle Avoidance for Omnidirectional Mobile Robots

We propose a novel shared control strategy for mobile robots in a human-robot interaction manner based on surface eletromyography (sEMG) signals. For security reasons, an obstacle avoidance scheme is introduced to the shared control system as collision avoidance guidance. The motion of the mobile robot is a resultant of compliant motion control and obstacle avoidance. In the mode of compliant motion, the sEMG signals obtained from the operator's forearms are transformed into human commands to control the moving direction and linear velocity of the mobile robot, respectively. When the mobile robot is blocked by obstacles, the motion mode is converted into obstacle avoidance. Aimed at the obstacle avoidance problem without a specific target, we develop a no-target Bug (NT-Bug) algorithm to guide the mobile robot to avoid obstacles and return to the command line. Besides, the command moving direction given by the operator is taken into consideration in the obstacle avoidance process to plan a smoother and safer path for the mobile robot. A model predictive controller is exploited to minimize the tracking errors. Experiments have been implemented to demonstrate the effectiveness of the proposed shared control strategy and the NT-Bug algorithm.

Research on intelligent obstacle avoidance control method for mobile robot in multi-barrier environment

In a current multi-obstacle environment, mobile robots are needed to control obstacle avoidance ability intelligently. Therefore, a method of mobile robot obstacle avoidance control in multi-obstacle environment is proposed based on the minimum risk index. This method first segments the path of obstacle environment according to certain rules. Then, robots are constrained through the constrained time according to requirements of robot obstacle avoidance. Therefore, calculating motion obtains necessary and sufficient conditions with no collision avoidance relationship. Then, impact danger level of each movement stage for the robot is evaluated by considering the spatial distance, inertia, and relative movement speed of multiple obstacle environments. The global safety path is planned, the intelligent avoidance function is calculated, and intelligent avoidance control is implemented. Finally, simulation results show that our proposed method is a common method in intelligent obstacle avoidance, timely tracking of moving target, and makes full use of space.

Distributed Cooperative Obstacle Avoidance for Mobile Robots Using Independent Virtual Center Points

This paper addresses the obstacle avoidance problem of multiple mobile robots. We propose a method to address this problem, using a distributed robotic cooperative obstacle avoidance method with independent virtual center points which are set based on the current state of nearby robots and itself. Mobile robots use two different control modes: an obstacle-free mode, and an obstacle-avoidance mode. The control mode is switched on-line, based on the robot’s states which are shared information. Moreover, there is no limit to the number of mobile robots in the system of the proposed method. A control law is designed to guide the mobile robots to away from potential collisions and avoid congestion with other robots. The stability of the system is proved using a Lyapunov function. The effectiveness of the proposed method is evaluated using simulation studies and also real-world experimental verification. Experimental results show that the proposed method enables mobile robots to not only avoid collision with each other, but also significantly reduces the travel time and travel distance for situations with irregular distributions of multiple robots.

종단 간 학습 방법을 이용한 이동로봇의 장애물 회피

종단 간 학습 방법을 이용한 이동로봇의 장애물 회피

A review of monocular visual odometry

Monocular visual odometry provides more robust functions on navigation and obstacle avoidance for mobile robots than other visual odometries, such as binocular visual odometry, RGB-D visual odometry and basic odometry. This paper describes the problem of visual odometry and also determines the relationships between visual odometry and visual simultaneous localization and mapping (SLAM). The basic principle of visual odometry is expressed in the form of mathematics, specifically by incrementally solving the pose changes of two series of frames and further improving the odometry through global optimization. After analyzing the three main ways of implementing visual odometry, the state-of-the-art monocular visual odometries, including ORB-SLAM2, DSO and SVO, are also analyzed and compared in detail. The issues of robustness and real-time operations, which are generally of interest in the current visual odometry research, are discussed from the future development of the directions and trends. Furthermore, we present a novel framework for the implementation of next-generation visual odometry based on additional high-dimensional features, which have not been implemented in the relevant applications.

Obstacle Avoidance of Mobile Robot using Fuzzy Logic and Hybrid Obstacle Avoidance Algorithm

The road accidents due to traffic problems and human erroneous driving are the major challenges for researches. The self-driving car or mobile robot is the solution to avoid such mishaps. In this paper, an attempted has been made to develop obstacle avoidance algorithms for bicycle vehicle model of mobile robots. The hybrid obstacle avoidance algorithm is proposed on the merits of line, wall following and tangent bug algorithm. The trajectory generated from the hybrid obstacle avoidance algorithm is fed into the overwhelming controller of the bicycle vehicle model of mobile robot for avoiding obstacles. Then, the fuzzy logic (FL) based obstacle avoidance controller is proposed. Twenty-three set of rules are proposed for fuzzy logic approach. Both the obstacle avoidance algorithms are implemented on the bicycle vehicle model of the mobile robot. The dynamic model of the mobile robot is developed using bond graph theory and is converted into Simulink block using S-function directly from the library of SYMBOLS Shakti software. The vehicle model is equipped with three ultrasonic sensors to measure the distance from the obstacles. Three input membership functions and one output membership function are considered in fuzzy logic controller. During avoiding two static obstacles and reaching the target, the comparison of obstacle free paths traced by both obstacle avoidance algorithms is done in this paper.

Obstacle Avoidance of Mobile Robot Based on HyperOmni Vision

Obstacle Avoidance of Mobile Robot Based on HyperOmni Vision

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.