Safe Mobile Robot Navigation Research Articles

Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

Unsupervised Pedestrian Pose Prediction: A Deep Predictive Coding Network-Based Approach for Autonomous Vehicle Perception

Pedestrian pose prediction is an important topic, related closely to robotics and automation. Accurate predictions of human poses and motion can facilitate a more thorough understanding and analysis of human behavior, which benefits real-world applications such as human-robot interaction, humanoid and bipedal robot design, and safe navigation of mobile robots and autonomous vehicles. This article describes a deep predictive coding network (PredNet)-based approach for unsupervised pedestrian pose prediction from 2D camera imagery and provides experimental results of two real-world autonomous vehicle data sets. The article also discusses topics for future work in unsupervised and semisupervised pedestrian pose prediction and its potential applications in robotics and automation systems.

Safe mobile robot navigation in human-centered environments using a heat map-based path planner

Safe robot navigation in human-centered environments is important to avoid collisions. A major limitation of the traditional path planning algorithms is that the global path is planned only with the knowledge of static obstacles in the map. This paper presents a novel ‘HMRP (heat map-based robot path planner)’ which uses fixed external cameras to generate a heat map of different passages based on congestion, so that robots can generate congestion-free paths at the global planning stage itself. The congestion values are maintained in a database and the paths are classified into hot and cold regions. Robot navigation is affected by the direction of movement of people. Hence, in this work, the HMRP-based planner also considers the direction of movement of people in passages which improves robot navigation. The proposed HMRP is compared with traditional path planning algorithms in real environment. Results show that the proposed HMRP algorithm generates congestion-free paths for safe robot navigation.

Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

Being trajectory tracking key for safe mobile robot navigation, Fuzzy Logic (FL) has been useful in tackling uncertainty and imprecision to realize robust and smooth trajectory tracking. In this paper, we present the Z-number based Fuzzy Logic control for trajectory tracking of differential wheeled mobile robots. The unique point of our approach lies in the ability to encode constraint and reliability in multi-input and multi-output rules, whose antecedent universe considers only the instantaneous measurements of distance and the orientation gaps, and whose consequent universe is computed by the interpolative reasoning and the graded mean integration approach. As a consequence, not only our approach avoids the complexity of encoding error gradients, but also is advantageous to model versatile control rules able to cope with missing observations and noisy inducements on actuators. Our experiments using both physics-based simulations and real-world tests based on a Pioneer 3DX robot architecture have elucidated the superior efficacy and the feasibility of the proposed controller regarding accuracy, robustness, and smoothness compared to other well-known related frameworks such as Fuzzy Logic Type 1, Fuzzy Logic Type 2 and Fuzzy Logic with PID. Our results provide unique insights to realize generalizable algorithms aided by FL and Z-number towards robust trajectory tracking.

Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments

Complex environments pose great challenges for autonomous mobile robot navigation. In this study, we address the problem of autonomous navigation in 3D environments with staircases and slopes. An integrated system for safe mobile robot navigation in 3D complex environments is presented and both the perception and navigation capabilities are incorporated into the modular and reusable framework. Firstly, to distinguish the slope from the staircase in the environment, the robot builds a 3D OctoMap of the environment with a novel Simultaneously Localization and Mapping (SLAM) framework using the information of wheel odometry, a 2D laser scanner, and an RGB-D camera. Then, we introduce the traversable map, which is generated by the multi-layer 2D maps extracted from the 3D OctoMap. This traversable map serves as the input for autonomous navigation when the robot faces slopes and staircases. Moreover, to enable robust robot navigation in 3D environments, a novel camera re-localization method based on regression forest towards stable 3D localization is incorporated into this framework. In addition, we utilize a variable step size Rapidly-exploring Random Tree (RRT) method which can adjust the exploring step size automatically without tuning this parameter manually according to the environment, so that the navigation efficiency is improved. The experiments are conducted in different kinds of environments and the output results demonstrate that the proposed system enables the robot to navigate efficiently and robustly in complex 3D environments.

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Abstract Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.

A reactive approach for mobile robot navigation in static and dynamic environment using fuzzy logic control

This paper presents a new control method for a safe navigation of mobile robot. The proposed control strategy adopts the principle of safe sector in the vector field histogram (VFH) method. It consists of directing the robot heading towards a safe vacant interval if there are obstacles, or to the target direction when the environment does not contain obstacles. A fuzzy controller is used for the robot guidance to the desired direction. The proposed navigation algorithm is available for static and dynamic environments. It is quite simple to understand and to implement. The efficiency of the proposed control approach is proved through simulation results using MATLAB and the virtual environment Webots.

A reactive approach for mobile robot navigation in static and dynamic environment using fuzzy logic control

This paper presents a new control method for a safe navigation of mobile robot. The proposed control strategy adopts the principle of safe sector in the vector field histogram (VFH) method. It consists of directing the robot heading towards a safe vacant interval if there are obstacles, or to the target direction when the environment does not contain obstacles. A fuzzy controller is used for the robot guidance to the desired direction. The proposed navigation algorithm is available for static and dynamic environments. It is quite simple to understand and to implement. The efficiency of the proposed control approach is proved through simulation results using MATLAB and the virtual environment Webots.

Path Planning for Autonomous Mobile Robot Based on Safe Space

Path planning is essential for the autonomous navigation of mobile robots. Researchers have been working on ensuring the safe navigation of mobile robots; however, it is impossible to secure the absolute safety of a mobile robot without environmental information. Nevertheless, passive safety of a mobile robot can be secured. With the aim of ensuring safe path planning of a mobile robot, a safety space is proposed in this work by using the parameters of stopping distance and hazard point. Mobile robots should formulate path plans to bypass crossroads or corner areas where their field of view is limited, and they should also be capable of reducing their movement speed to secure safe driving. We demonstrate through extensive simulations that the developed SGPP (safe global path planning) method outperforms the classical A * and PRM (probabilistic roadmap) algorithms. It improves the navigation time and length of the robot path in comparison to the PRM algorithm and reduces the navigation time by up to 26% compared to the classical A * algorithm for safe navigation. In addition, results of an experiment conducted on a real robot show that the SGPP method finds a safe path with limited velocity for safe navigation.

Mobile Robot Navigation in Cluttered Environment using Reactive Elliptic Trajectories

AbstractReactive navigation in very cluttered environment while insuring maximum safety and task efficiency is a challenging subject. This paper proposes online and adaptive elliptic trajectories to perform smooth and safe mobile robot navigation. These trajectories use limit-cycle principle already applied in the literature but with the difference that the applied limit-cycles are now elliptic (not circular) and are more generic and flexible to perform navigation in environments with different kinds of obstacles shape. The set points given to the robot are generated while following reactive obstacle avoidance algorithm embedded in a multi-controller architecture (Obstacle avoidance and Attraction to the target controllers). This algorithm uses specific reference frame which gives accurate indication of robot situation. The robot knows thus if it must avoid the obstacle in clockwise or counter-clockwise direction and prevent robot from local minima, dead ends and oscillations. The stability of the proposed bottom-up control architecture is proved according to Lyapunov synthesis. Simulations and experiments in different environments are performed to demonstrate the efficiency and the reliability of the proposed control architecture.

Indoor localization system in a multi-block workspace

SUMMARYThis paper proposes methodologies and techniques for multi-block navigation of an indoor localization system with active beacon sensors. As service robots and ubiquitous technology have evolved, there is an increasing need for autonomous indoor navigation of mobile robots. In a large number of indoor localization schemes, the absolute position estimation method, relying on navigation beacons or landmarks, has been widely used due to its low cost and high accuracy. However, few of these schemes have managed to expand the applications for use in complicated workspaces involving many rooms or blocks that cover a wide region, such as airports and stations. Since the precise and safe navigation of mobile robots in complicated workspaces is vital for the ubiquitous technology, it is necessary to develop a multi-block navigation scheme. This new design of an indoor localization system includes ultrasonic attenuation compensation, dilution of both the precision analysis and fault detection, and an isolation algorithm using redundant measurements. These ideas are implemented on actual mobile robot platforms and beacon sensors, and experimental results are presented to test and demonstrate the new methods.

The (In)Accuracy of Novice Rover Operators' Perception of Obstacle Height From Monoscopic Images

Researchers have previously described a mobile robot, or rover, operator's difficulty in accurately perceiving the rover's tilt and roll, which can lead to rollover accidents. Safe mobile robot navigation and effective mission planning also require an operator to accurately interpret and understand the geometry and scale of features in the rover's environment. This work presents an experiment that measures an observer's ability to estimate height of distant (5-15 m) obstacles given an accurate local model (e.g., within 0-5 m of the rover), a panoramic image, and a physical mock-up of the local terrain. The experimental conditions were intended to represent a best-case scenario for a stopped rover equipped with short base-line stereoscopic cameras. The participants' task was to extrapolate the well-modeled local geometry to monoscopic images of the more distant terrain. The experiment compared two estimation techniques. With the first technique, each observer physically indicated his or her direct estimates of the obstacle distance and height. With the second estimation technique, which we call horizon analysis, the observer indicated the position of the top and bottom of each rock on an image and the height was calculated by measuring the visual angle between the theoretical horizon and the points indicated by the observer. The direct estimation technique overestimated the height of the rocks by an average of 190%; the horizon analysis technique overestimated by 80%. The results suggest that even when provided with a rich set of supplementary and context information, rover operators have significant difficulty in vertically perceiving the scale of distant terrain. The results also suggest that horizon analysis is a more accurate method for determining the height of distant rover navigation obstacles, when the local terrain is nearly level.