Humanoid robots generated by inspiring by human appearances and abilities have became essential in human society to improve the quality of their life. All over the world, there have been many researchers who have focused on humanoid robots to develop the capabilities of humanoid robots. Generally, humanoid robot systems include mechanisms of decision making and information processing. Because of the

In this paper, we study Emergency Landing Aware Surveillance Planning (ELASP) to determine a cost-efficient trajectory to visit a given set of target locations such that a safe emergency landing is possible at any point of the multi-goal trajectory. The problem is motivated to guarantee a safe mission plan in a case of loss of thrust for which it is desirable to have a safe gliding trajectory to a

Building 3D maps of various terrains is a necessary approach to gain environmental information for mobile robots when they are exploring in unknown territories. In this paper, we propose a method to construct a point cloud map with laser-measured data as the robot moves around. A terrain-adaptive density mapping technique is used to balance the demands of small data size and high terrain accuracy by

This paper presents a multi-task instance segmentation neural network able to provide both road lane and road participants detection. The multi-task approach, ERFNet-based, allows feature sharing and reduces the computational requirements of the overall detection architecture, allowing real time performance even in configurations with limited hardware. The proposed method includes an ad-hoc training

Pattern formation for multi-robot systems has received increasing attention in different scenarios. However, existing methods cannot efficiently optimize pattern formation in the obstacle environment. To address this limitation, this paper proposes a new planning method that assigns the optimal goals to the robots and iteratively computes collision-free paths to reach goal positions. Firstly, according

This paper continues the previous effort on the development of a trajectory generation platform that assures minimum-control expenditure and collision-free manoeuvre of a torpedo-shaped autonomous underwater vehicle (AUV) into a funnel-shaped stationary docking station (DS). The earlier-developed guidance system was based on the Inverse Dynamics in the Virtual Domain (IDVD) method accounting for AUV’s

The recognition of previously visited places, known as Loop Closure Detection (LCD), composes one of the problems widely studied in robotics: simultaneous localization and mapping (SLAM). In this paper we propose a three level hierarchy based LCD method. In our serialized approach, in the First Level, a sequence of the most recently visited places is used as query to search for candidate sequences

Exploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. We consider the problem of exploration for map building, in which a robot explores an indoor environment in order to build a metric map. Most of the current exploration strategies used to select the next best locations to visit ignore prior knowledge about the environments

This paper presents a rapid (real time) solution to the minimum-time path planning problem for Dubins vehicles under environmental currents (wind or ocean currents). Real-time solutions are essential in time-critical situations (such as replanning under dynamically changing environments or tracking fast moving targets). Typically, Dubins problem requires to solve for six path types; however, due to

In crowded multi-agent navigation, the motion of the agents is significantly constrained by the motion of the nearby agents. This makes planning paths very difficult and leads to inefficient global motion. To address this problem, we propose a distributed approach, which we call C-Nav, that introduces politeness into multi agent navigation. With our approach, agents take into account the velocities

The daily working hours of mobile robots are limited primarily by battery life. Most systems use a combination of thresholds and fixed periods to decide when to charge. This produces charging behaviour that ignores high-value tasks that must be performed within time-windows or by deadlines. Instead the robot should schedule charging adaptively, taking into account the times of day when it is expected

Assist-as-needed control strategy is an emerging approach to improve the effectiveness of gait rehabilitation training. We have proposed a pneumatic muscle (PM) driven Gait Rehabilitation Exoskeleton (GAREX) implemented with a multi-input–multi-output (MIMO) sliding mode control system to actively adjust the assistance level provided during gait rehabilitation. To realize the assist-as-needed control

Combining model-based and model-free learning systems has been shown to improve the sample efficiency of learning to perform complex robotic tasks. However, dual-system approaches fail to consider the reliability of the learned model when it is applied to make multiple-step predictions, resulting in a compounding of prediction errors and performance degradation. In this paper, we present a novel dual-system

This paper presents three fault-tolerant control (FTC) strategies for a coaxial octorotor unmanned aerial vehicle (UAV) regarding motor failures. The first FTC is based on a control mixing strategy which consists of a set of control laws designed offline, each one dedicated to a specific fault situation. The second FTC, a robust adaptive sliding mode control allocation is presented, where the control

We present an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D instance-aware semantic reconstruction. Our framework couples convolutional neural networks (CNNs) and a state-of-the-art dense Simultaneous Localization and Mapping (SLAM) system, ElasticFusion (Whelan et al., 2016), to achieve both high-quality semantic reconstruction as well

We present a Visual Place Recognition (VPR) pipeline that achieves substantially improved precision as compared with approaches commonly appearing in the literature. It is based on a standard image retrieval configuration, with an initial stage that retrieves the closest candidates to a query from a database and a second stage where the list of candidates is re-ranked. The latter is realized by the

High dimensional robot motion planning has recently been approached with trajectory optimization methods that efficiently minimize a suitable objective function in order to generate robot trajectories that are both optimal and feasible. However, finding a globally optimal solution is often an insurmountable problem in practice and state-of-the-art trajectory optimization methods are thus prone to local

In this study, we present a novel concept of a multi tethered drone system. The system includes an arbitrary number of drones connected serially to an active ground station. The considered drones are of quadrotor type. Utilizing a unique pulley–gimbal mechanism, each drone can freely move along the tether, and its state is measured with respect to the ground station without the use of standard onboard

In this paper, we propose a new formulation to consider visual–spatial attention in order to improve the comfort of a human standing or operating near a collaborative robot. This formulation is based on the principle of having the robot’s end-effector in the human visual–spatial attention as much as possible. The integration of this new constraint into the Inverse Kinematics (IK) problem is thoroughly

Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping

Advanced Driver Assistance Systems (ADAS) and Automated and Autonomous Vehicles (AV) are cooperative systems and processes that use: artificial intelligence, cognitive methods, cloud technologies, cooperative vehicle-to-everything-communications (V2X), software–hardwareplatforms, sensor platforms and incipient intelligent transport infrastructures, to get self-driving systems and smart connected mobility

This paper introduces a mobile robot with a new type of transformable wheel legs that can be used for flat and rough terrain. It integrates the stability and maneuverability of a wheeled robot and the legged robot’s obstacle climbing capacity using a transformable mechanism with wheel legs. With a transformation structure based on a four-bar mechanism, these two modes can be easily changed. This paper

This paper presents a novel challenging dataset that offers a new landscape of testing material for mobile robotics, autonomous driving research, and forestry operation. In contrast to common urban structures, we explore an unregulated natural environment to exemplify sub-urban and forest environment. The sequences provide two-natured data where each place is visited in summer and winter conditions

Recently, there have been several studies on the research and development of service robots, such as reception or waiter robots for facilities and companion robots for support of baggage transportation or guidance in public spaces. Several experimental results in real environments have been reported. To realize socially acceptable human–robot interaction for service robots, human recognition, including

So far, gas leakage caused by natural or human factors has led to serious consequences in terms of social security. Previous strategies for locating the odor sources appear to be either defective or incomplete. For enhancing the success rate and rapidity, this paper aims to present a novel and complete strategy in search of lurking gas sources. Particle filtering and information entropy are both employed

Visual localization is a challenging problem, especially over the long run, since places can exhibit significant variation due to dynamic environmental and seasonal changes. To tackle this problem, we propose a visual place recognition method based on directed acyclic graph matching and feature maps extracted from deep convolutional neural networks (DCNN). Furthermore, in order to find the best subset

The paper presents a novel path planning methodology based on the directional graph search and the geometry curve for the Automated Valet Parking (AVP) system. The whole path planning methodology is divided into three parts including the global path planning, the path coordination strategy and the parking path planning. Firstly, the global path planning is triggered to find a path from the parking

We address the general problem of multiple target localization and pursuit using measurements of the ranges from the targets to a set of autonomous pursuing vehicles, referred to as trackers. We develop a general framework for targets with models exhibiting uncertainty in the initial state, process, and measurement noise. The main objective is to compute optimal motions for the trackers that maximize

The consensus of multi-agent dynamic systems is a metaphor for many different tasks involving group agreement. However, ensuring consensus in real-world scenarios, with non-convex obstacles and kinodynamic motion constraints, proves to be a hard task, since it is quite difficult to model such a problem analytically. Therefore, this paper studies the problem of state agreement for Multi-Robot Systems

The navigation of autonomous, mobile multi-robot systems in changing environments is a challenging problem investigated over the past years. Cooperative, multiple robots are employed for many different tasks to increase the efficiency and success of a mission. However, many of the existing collective path planning approaches do not guarantee a reliable escape in environments with complex, non-convex

In autonomous driving, many intelligent perception technologies have been put in use. However, visual SLAM still has problems with robustness, which limits its application, although it has been developed for a long time. We propose a feature-aided semi-direct approach to combine the direct and indirect methods in visual SLAM to allow robust localization under various situations, including large-baseline

Within the wide field of self-assembly, the self-folding chain has the unique capability to pass through narrow openings, too small for the assembled structure, yet consists in one connected body. This paper presents a novel analytical framework and corresponding experimental setup to quantify the results of a self-folding process using magnetic forces at the centimetre-scale, with the aim to put experimental

This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. Nine high-resolution cameras and two 32-beam 3D Lidars were used along with a professional, static 3D scanner for ground truth map collection. With all the sensors calibrated on the mapping robot, three datasets are collected to

The design of high-level decision-making systems is a topical problem in the field of autonomous driving. In this paper, we combine traditional rule-based strategies and reinforcement learning (RL) with the goal of achieving transparency and robustness. On the one hand, the use of handcrafted rule-based controllers allows for transparency, i.e., it is always possible to determine why a given decision

This paper addresses the problem of human grasp position estimation in a physical human–robot object handling scenario. The problem is formulated as a linear regression by considering the human grasp position and their exerted torque as unknown parameters. We propose a modified least-squares algorithm to estimate the parameters by evaluating the quality of the estimates based on the assumption that

Vehicle tracking is an attractive problem in the field of public transportation with several research projects conducted using Kalman filter (KF) to tackle this. While a driver may act on his own decision, there exist parameters affecting his behavior so called situation assessment such as neighboring drivers, possible obstacles, or alternative routes changing over time. In this paper, utilizing online

This paper proposes a visual teleoperation with human–robot posture-consistent based on deep neural network. A multi-stage structure of visual teleoperation network, in which the angles of robotic joints are obtained from human, is deduced. Furthermore, a novel human–robot posture-consistent mapping method is developed to generate dataset of the visual teleoperation network by solving constrained nonlinear

Novel applications of soft pneumatic actuation in minimally invasive surgery (MIS) are proposed due to its relatively safe robot–environment interactions. Although the inherent compliance of soft robots makes them suitable for surgery, their low force output and complicated system response and behavior may limit their potential as practical MIS instruments. In this paper, three lobster-inspired antagonistic

A methodology for planning the sequence of tasks for a harvesting robot is presented. The fruit targets are situated at unknown locations and must be detected by the robot through a sequence of sensing tasks. Once the targets are detected, the robot must execute a harvest action at each target location. The traveling salesman paradigm (TSP) is used to plan the sequence of sensing and harvesting tasks

This paper is concentrated on path planning for robots working in a dynamic environment to satisfy real-time needs. An efficient bias-goal factor RRT (EBG-RRT), which is multiple-query sampling-based replanning algorithm, is proposed with rapid response and high success rate. Specifically, a relay node method is proposed to get a position where the robot and dynamic obstacles will be no-collision and

Teach and Repeat (T&R) refers to the technology that allows a robot to autonomously follow a previously traversed route, in a natural scene and using only its onboard sensors. In this paper we present a Visual-Inertial Teach and Repeat (VI-T&R) algorithm that uses stereo and inertial data and targets Unmanned Aerial Vehicles with limited on-board computational resources. We propose a tightly-coupled

To achieve temporal and spatial correspondence between multiple robotic manipulators, the system must correctly analyze the coordinated path required for a specific task. Based on manipulator kinematics analysis, we first studied the kinematic constraints between the end-effectors of cooperative manipulators, and deduced the multi-manipulator cooperative kinematics constraint equations in the Cartesian

This paper addresses multi-robot task scheduling for two robot types arising from heterogeneous robotic order fulfillment systems. The heterogeneous multi-robot system comprises two types of robots with specialized and complementary capabilities to achieve long-cycle and multi-station order fulfillment tasks on a logistic network. This problem is extremely challenging because of innate complex-schedule

Autonomous navigation of mobile robot via classic neural network (NN) models are no more valid in terms of efficiency and accuracy due to the development of new advanced techniques. However, the necessity of finding an implementable Recursive Neural Network (RNN) model to predict the motor control of the robot with both speed and accuracy constraints still remains stagnant because of the nonlinearity

Automated image completion and masking have been emerged as a subject of keen interest due to their impact on image modification and interpretation. The current state-of-the-art approaches require a fixed format of missing parts and are ineffective for handling corrupted images. Besides, they focus exclusively on the image completion without taking into consideration the image masking as an inverse

Mobile service robots possess high potential of providing numerous assistances in the working areas. In an attempt to develop a mobile service robot which is dynamically balanced for faster movement and taller manipulation capability, we designed and prototyped J4.alpha, which is intended for swift navigation and nimble manipulation. Previously, we devised a pure visual method based on a supervised

Optics-based systems may provide high spatial and temporal resolution for close range object detection in underwater environments. By using a monocular camera on a low cost underwater vehicle manipulator system, objects can be tracked by the vehicle and handled by the manipulator. In this paper, a monocular camera is used to detect an object of interest through object detection. Spatial features of

Fixed-Wing UAVs (Unmanned Aerial Vehicles) flocking is still a challenging problem due to the kinematics complexity and environmental dynamics. In this paper, we solve the leader–followers flocking problem using a novel deep reinforcement learning algorithm that can generate roll angle and velocity commands by training an end-to-end controller in continuous state and action spaces. Specifically, we

This work considers the problem of locating a single robot given a set of squared noisy range difference measurements to a set of points (anchors) whose positions are known. In the sequel, localization problem is solved in the Least-Squares (LS) sense by writing the robot position in polar/spherical coordinates. This representation transforms the original nonconvex/multimodal cost function into the

In a typical operation mode, a wheel loader frequently accelerates and decelerates, and the curvature of the driving path is inconsistent. In the past, autonomous vehicle trajectory planning has not considered the related changes in the velocity of the vehicle. Therefore, the trajectory tracking control process has seldom considered the impact of curving paths on the trajectory tracking performance

Due to its vast applicability, the semantic interpretation of regions or entities increasingly attracts the attention of scholars within the robotics community. The paper at hand introduces a novel unsupervised technique to semantically identify the position of an autonomous agent in unknown environments. When the robot explores a certain path for the first time, community detection is achieved through

Rapidly-exploring Randomized Trees (RRT) is a kind of probabilistically complete exploration algorithm based on the tree structure. It has been widely used in the robotic navigation since it guarantees the complete discovery and the exploration of environment maps through robots. In the present study, the RRT algorithm is extended to propose an optimization-based map exploration strategy for multiple

Robotic obstacle avoidance is an important issue in robotic navigation for unknown or partially known, dynamic environments. A good number of techniques have already been proposed to navigate obstacles in this kind of environment. They include a series of velocity space methods that have been successful implemented in several applications. They formulate the problem as one of constrained optimization

The interaction between robots and humans is of great relevance for the field of neurorobotics as it can provide insights on how humans perform motor control and sensor processing and on how it can be applied to robotics. We propose a spiking neural network (SNN) to trigger finger motion reflexes on a robotic hand based on human surface Electromyography (sEMG) data. The first part of the network takes

Place recognition is an essential component to address the problem of visual navigation and SLAM. The long-term place recognition is challenging as the environment exhibits significant variations across different times of the days, months, and seasons. In this paper, we view appearance changes as multiple domains and propose a Feature Disentanglement Network (FDNet) based on a convolutional auto-encoder

A fuel-efficient control strategy for a manipulator-equipped spacecraft is presented. The strategy uses the thrusters, the reaction wheels, and the arm drives in a coordinated way to limit the use of the thrusters and achieve ideally zero fuel consumption in contact-free maneuvering. The thrusters are activated automatically only after contact, to stabilize the inertial motion of the system. The controller

In human–robot cooperation, the information interaction plays a key role. Most of the information interaction rely on Border Gateway Protocol (BGP), which is a vital route protocol on networks. However, the BGP is susceptible to the prefix interception attacks because the rightful origin of each prefix cannot be verified in BGP. For this reason, we propose a novel and effective route selection method