Navigation Framework For Robots Research Articles

A Context-Aware Navigation Framework for Ground Robots in Horticultural Environments.

Environmental mapping and robot navigation are the basis for realizing robot automation in modern agricultural production. This study proposes a new autonomous mapping and navigation method for gardening scene robots. First, a new LiDAR slam-based semantic mapping algorithm is proposed to enable the robots to analyze structural information from point cloud images and generate roadmaps from them. Secondly, a general robot navigation framework is proposed to enable the robot to generate the shortest global path according to the road map, and consider the local terrain information to find the optimal local path to achieve safe and efficient trajectory tracking; this method is equipped in apple orchards. The LiDAR was evaluated on a differential drive robotic platform. Experimental results show that this method can effectively process orchard environmental information. Compared with vnf and pointnet++, the semantic information extraction efficiency and time are greatly improved. The map feature extraction time can be reduced to 0.1681 s, and its MIoU is 0.812. The resulting global path planning achieved a 100% success rate, with an average run time of 4ms. At the same time, the local path planning algorithm can effectively generate safe and smooth trajectories to execute the global path, with an average running time of 36 ms.

Modeling variability in self-adapting robotic systems

Autonomous robots operating in everyday environments, such as hospitals, private houses, and public roads, are context-aware self-adaptive systems, i.e. they exploit knowledge about their resources and the environment to trigger runtime adaptation, so that they exhibit a behavior adequate to the current context. For these systems, context-aware self-adaptation requires to design the robot control application as a dynamically reconfigurable software architecture and to specify the adaptation logic for reconfiguring its variable aspects (e.g. the modules that implement various obstacle detection algorithms or control different distance sensors) according to specific criteria (e.g. enhancing robustness against variable illumination conditions). Despite self-adaptation is an intrinsic capability of autonomous robots, ad-hoc approaches are used in practice to design reconfigurable robot architectures. In order to enhance system maintainability, the control logic and the adaptation logic should be loosely coupled. For this purpose, the adaptation logic should be defined against an explicit representation of software variability in the robot control architecture. In this paper we propose a modeling approach, which consists in explicitly representing robot software variability with the MARTE::ARM-Variability metamodel, which has been designed as an extension of the UML MARTE profile. We evaluate the applicability of the proposed approach by exemplifying the software architecture design of a robot navigation framework and by analyzing the support provided by the ROS infrastructure for runtime reconfiguration of its variable aspects.

Socially Aware Robot Navigation Framework: Where and How to Approach People in Dynamic Social Environments

This paper proposes a human approaching robot navigation framework that enables a mobile service robot to (i) estimate a socially optimal approaching pose, and (ii) navigate safely and socially to the estimated approaching pose. In the first stage, the robot estimates potential approaching poses of a human or a human group, which the robot can safely and socially approach, using the dynamic social zone model. In the second stage, the proposed framework selects a socially optimal approaching pose, then estimate a socially optimal trajectory of the robot using the proposed goal-oriented timed elastic band (GTEB) model. The developed GTEB model takes into account the current robot’s states, robot dynamics, dynamic social zone, regular obstacles and potential approaching poses to generate the socially optimal robot trajectory from the robot’s current pose to the selected optimal approaching pose. The motion control command extracted from the socially optimal trajectory is then utilized to drive the mobile robot to approach the individual humans or human groups, while safely and socially avoiding regular obstacles, human and human groups during the navigation process. The proposed approaching human framework is verified in the both simulation and real robots. The results illustrate that, the mobile robot equipped with our developed GTEB model is able to safely and socially approach and avoid individual humans and human groups, while guaranteeing the comfortable safety for the humans and socially acceptable behaviors for the robot. Note to Practitioners—Although our proposed GTEB model is capable of estimating a socially optimal approaching pose and social robot trajectory, driving the robot to approach a human and a human group, and providing the safety and comfort for humans and socially acceptable behaviors of the robot, there exists a few drawbacks if we wish to apply the proposed approaching human framework in dynamic social environments. First, the optimizer for the GTEB model should be improved in terms of computational time and accuracy to avoid generating unpredictable robot trajectories, especially in dynamic social environments. Second, the socio-spatio-temporal characteristics of the humans including human position, motion and orientation, and human group and human–object interactions play an important role in the proposed GTEB model. However, the existing techniques are only suitable in quasi-dynamic social environments. Hence, highly accurate, robust and real-time algorithms for human detection and tracking, and social interaction detection are necessary. Third, social interactive intentions such as human–robot, human–human and human–object interactive intentions should be predicted and incorporated into the proposed framework to improve the performance of the developed framework in the dynamic social environments. Last but not least, human and human group identification algorithms should be proposed to enable the robot to identify the humans, whose the mobile robot is requested to approach. In the future, the social interactive intentions, the human’s future states and its trajectories and will be predicted using deep learning algorithms and incorporated into the approaching human framework to improve the performance of the proposed framework.

A Unified Multiple-Motion-Mode Framework for Socially Compliant Navigation in Dense Crowds

Mobile robots are expected to move safely and efficiently in socially-compliant ways in dense crowds. In this paper, a unified multiple-motion-mode framework is proposed to achieve socially-compliant navigation in dense crowds. The proposed framework consists of three successive phases, the identification of pedestrian groups and prediction of their trajectories, the generation of candidate local trajectories of the robot, and the determination of an optimal local trajectory. The pedestrians are grouped according to distances among pedestrians and their velocities and a predictor predicts the groups’ trajectories. Three robot motion modes, moving-solo, pedestrian-following, and courteous-stopping, are proposed based on a systematic analysis and classification of dense crowds and they are utilized to generate candidate local trajectories. A composite metric is proposed to determine the best local trajectory by simultaneously considering the robot motion safety, efficiency, and stability of the trajectory. The proposed robot navigation framework is evaluated both in simulations and real-world experiments with various scenarios. The results show that the proposed framework surpasses the state-of-the-art methods in terms of efficiency and social compliance. Note to Practitioners—This paper is motivated by the socially-compliant navigation problem of mobile robots in densely crowded environments such as hospitals and airports. Robots navigation in these scenes need to follow collective social conventions to ensure ordered and efficient public pedestrian traffics, and individual social conventions for acceptance by human society. Existing approaches generally consider only one type of social conventions cannot accomplish a truly socially-compliant navigation and cannot be naturally accepted in the human pedestrian environment. This paper proposes to imitate the pedestrian’s walking behavior for robot navigation by naturally choosing one of the three walking modes, moving-solo, following, and courteous-stopping. We construct the candidate trajectory sampling strategies for different motion modes based on the global path and the two types of social conventions. Experimental results show that the proposed framework can be applied in large-scale, densely crowded, unstructured, and human-robot coexisting environments for socially-compliant navigation. However, the proposed navigation framework has limited improvement in navigation efficiency in a highly crowded environment due to the limited feasible space. Even pedestrians are difficult to walk in these highly crowded environments. In future work, we will study how to auto-adjust the weights to steer the robot to efficient navigation in highly crowded environments.

AN INTEGRATED AVOIDING AND APPROACHING HUMAN SYSTEMS FOR MOBILE SERVICE ROBOTS IN DYNAMIC SOCIAL ENVIRONMENTS

In this study, we present a socially aware navigation framework for mobile service robots by integrating the systems proposed in our previous studies into a completed mobile robot navigation system, including the human detection and tracking system that is used to detect and track humans; the encoder and laser-based localization and mapping system that is used to determine the position of the robot in the environment; the approaching pose estimation system that is used to estimate the approaching pose of the robot to the human; and the social timed elastic band-based motion planning system that is used to socially navigate the mobile robots to approach the human. In addition, in the paper we describe in detail the motor control system and the design of our mobile robot platform, which is then utilized to conduct experiments in the real-world environments. We verify the feasibility and usefulness of the proposed socially aware robot navigation framework through a series of experiments in a corridor-like environment. The experimental results show that our proposed framework is able to drive the mobile robots to both avoid and approach the humans, providing the safety and comfort for the humans and socially acceptable behaviors for the mobile service robots in the dynamic social environments.

A new human-aware robot navigation framework based on time-dependent social interaction spaces: An application to assistive robots in caregiving centers

One of the most critical problems to be addressed by future generation socially-assistive robots working in semi-organized social environments, such as shopping centers, nursing homes, airports, hospitals, or assisted living centers, is the capability of human-aware navigation. Autonomous navigation in a complex environment with people, staff with different roles, timetables, and restrictions to access, among others, requires adapting to socially accepted rules. Consequently, the path-planner must consider concepts related to proxemics and personal spaces of interaction that include human–human, human–robot, or human–object combinations. Likewise, the speed of approaching people, both to initiate communication or to navigate nearby, must be adapted to social conventions. Some of these situations have already been studied in the literature with varying degrees of success. However, the concept of time dependency or chronemics in the robot social navigation has been poorly explored. Current algorithms do not take into account the social complexity of real environments and their relationship with the time of day or the activities performed in these scenarios. This article presents a new framework for robot social navigation in human environments, introducing the concept of time-dependent social mapping. The main novelty is that the social route planned by the robot considers variables that depend on the time and the scheduled center activities. The article describes how the areas of interaction vary over time and how they affect human-aware navigation. To this end, the proposed navigation stack defines a new function for time-dependent social interaction space that takes continuous values and is configurable by the center’s staff. The global path-planner uses this function to choose dynamically a socially accepted path to the target. Then, the framework uses an elastic band path optimizer as a local planner, adapting the robot’s navigation to possible changes during the trajectory. Several use cases in simulated caregiving centers have been explored to validate the robot’s social navigation improvements using these temporal variables.

Development of Mobile Robot Navigation Framework using Operation Based Middleware Model

Development of Mobile Robot Navigation Framework using Operation Based Middleware Model

Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images.

Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category of robot-oriented lane-detection/trajectory tracking. These methods suffer from high computational cost and require stringent labelling and calibration efforts. To address these challenges, this paper proposes a lightweight robot navigation framework based purely on uncalibrated spherical images. To simplify the orientation estimation, path prediction and improve computational efficiency, the navigation problem is decomposed into a series of classification tasks. To mitigate the adverse effects of insufficient negative samples in the “navigation via classification” task, we introduce the spherical camera for scene capturing, which enables 360° fisheye panorama as training samples and generation of sufficient positive and negative heading directions. The classification is implemented as an end-to-end Convolutional Neural Network (CNN), trained on our proposed Spherical-Navi image dataset, whose category labels can be efficiently collected. This CNN is capable of predicting potential path directions with high confidence levels based on a single, uncalibrated spherical image. Experimental results demonstrate that the proposed framework outperforms competing ones in realistic applications.

Goal-directed affordance prediction at the subtask level

Purpose – This paper aims to present a hybrid control approach that combines learning-based reactive control and affordance-based deliberate control for autonomous mobile robot navigation. Unlike many current navigation approaches which only use learning-based paradigms, the authors focus on how to utilize the machine learning methods for reactive control together with the affordance knowledge that is simultaneously inherent in natural environments to gain advantages from both local and global optimization. Design/methodology/approach – The idea is to decompose the complex and large-scale robot navigation task into multiple sub-tasks and use the hierarchical reinforcement learning (HRL) algorithm, which is well-studied in the learning and control algorithm domains, to decompose the overall task into sub-tasks and learn a grid-topological map of the environment. An affordance-based deliberate controller is used to inspect the affordance knowledge of the obstacles in the environment. The hybrid control architecture is then designed to integrate the learning-based reactive control and affordance-based deliberate control based on the grid-topological and affordance knowledge. Findings – Experiments with computer simulation and an actual humanoid NAO robot have demonstrated the effectiveness of the proposed hybrid approach for mobile robot navigation. Originality/value – The main contributions of this paper are a new robot navigation framework that decomposes a complex navigation task into multiple sub-tasks using the HRL approach, and hybrid control architecture development that integrates learning-based and affordance-based paradigms for autonomous mobile robot navigation.

Curvilinear Image Regions Detection: Applications to Mobile Robotics

This paper proposes a novel approach for visual features detection, which is based on the presence of objects whose shape can be modelled using cylinders or generalized cylinders. These specific structures are commonly found on indoor and outdoor scenarios, and their image representations, the so-called curvilinear regions, automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. The method is based on Marr's visual theory that proposes that visual objects can be decomposed in generalized cylinders. Also, part of the method can be compared to the behavior of AOS neurons, placed in the caudal intraparietal sulcus, that respond when an elongated object is visualized. Our detector reliably finds the same curvilinear regions under different viewing conditions. Evaluation results are given to demonstrate the performance of the approach and its ability to be applied for visual features detection in a mobile robot navigation framework.