This paper presents an approach to planning under uncertainty in resource-constrained environments. We describe our novel method for online plan modification and execution monitoring, which augments an existing plan with pre-computed plan fragments in response to observed resource availability. Our plan merging algorithm uses causal structure to interleave actions, creating solutions online using observations

Balancing two-wheeled autonomous vehicles at low forward speeds is one of the primary challenges in the development of such vehicles. Gyrostabilizers can be used as actuators to make the balance; however, conventional gyros are not typically able to maintain constant moments and directions to stabilize against constant ‘heel’. In this paper, we present an innovative gyrostabilizer including a twin-flywheel

Robots still cannot perform everyday manipulation tasks, such as grasping, with the same dexterity as humans do. In order to explore the potential of supervised deep learning for robotic grasping in unstructured and dynamic environments, this work addresses the visual perception phase involved in the task. This phase involves the processing of visual data to obtain the location of the object to be

The paper at hand introduces a novel system for producing an enhanced semantic map that leverages a reconstruction approach of street-view scenes using computer vision and machine learning techniques. Focusing on the recognition and localization of objects/entities, the composed map combines semantic information from publicly available, yet of lower accuracy, satellite images, with more detailed data

Swarms of autonomous agents are useful in many applications due to their ability to accomplish tasks in a decentralized manner, making them more robust to failures. Due to the difficulty in running experiments with large numbers of hardware agents, researchers often make simplifying assumptions and remove constraints that might be present in a real swarm deployment. While simplifying away some constraints

In this study, a new convex optimization (CO) approach to time-optimal trajectory planning (TOTP) is described, which considers both torque and jerk limits. The key insight of the approach is that the non-convex jerk limits are transformed to linear acceleration constraints and indirectly introduced into CO as the linear acceleration constraints. In this way, the convexity of CO will not be destroyed

Point clouds registration is a fundamental step of many point clouds processing pipelines; however, most algorithms are tested on data that are collected ad-hoc and not shared with the research community. These data often cover only a very limited set of use cases; therefore, the results cannot be generalized. Public datasets proposed until now, taken individually, cover only a few kinds of environment

Human-joint-position estimation is crucial for patient-transfer robots. However, high accuracy and real-time property are difficult to achieve simultaneously. To tackle the problem, we develop a new convolutional neural network (CNN), containing two levels of subnetworks, to fuse the information in color and depth images. The first-level subnetwork generates two-dimensional (2D) human joint positions

Object manipulation planning in clutter suffers from perception uncertainties due to occlusion, as well as action constraints required by collision avoidance. Partially observable Markov decision process (POMDP) provides a general model for planning under uncertainties. But a manipulation task usually have a large action space, which not only makes task planning intractable but also brings significant

Triangle mesh maps for robotic applications are becoming increasingly popular, but are not yet effectively supported in the Robot Operating System (ROS). We introduce the Mesh Tools package consisting of message definitions, RViz plugins and tools, as well as a persistence layer. These tools make annotated triangle maps available in ROS and allow to publish, edit and inspect such maps within the existing

Inspired by the abilities of amoeba to alter their shape, a continuous-track robot called Ourobot has been developed that is able to adapt its shape to the environment. Using tactile sensors at the outer hull of the robot, the outline of the terrain and collisions with obstacles can be detected. Thus, the robot is able to locomote in uneven terrain and climb steep slopes. Since the shape adaption is

The manipulation of an object into a desired location in a cluttered and restricted environment requires reasoning over the long-term consequences of an action while reacting locally to the multiple physics-based interactions. We present Visual Receding Horizon Planning (VisualRHP) in a framework which interleaves real-world execution with look-ahead planning to efficiently solve a short-horizon approximation

In this paper, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. We train a deep neural network for estimating the robot’s position in the environment using ground truth information provided by a classical localization and mapping approach. The second simpler multi-goal Q-function

We consider the problem of distributed goal conflict resolution in multi-robot systems while remaining resilient to intermittent communication losses between robots. Our proposed approach uses a spatial approximation technique called α-shape to represent the regions that have been explored by robots followed by a O(logn) algorithm that incrementally combines and shares the α-shape information between

Autonomous driving and driver assistance require a continuous and reliable perception of the road boundaries, namely curbs and berms, including also other minor, or not so minor, obstacles in the neighborhood of the car. This paper proposes to use a 4-layer LIDAR placed close to the ground to capture measurements of the road ahead of the car and allow the detection of the boundaries. This setup provides

With the advancement of the autonomous mobile robots applied to Warehouses and the creation of the Robotic Mobile Fulfillment System after the market implementation of the Kiva Robots, it is necessary to carry out a deeper approach of the researches carried out to this date. The objective of this survey is to provide a unified and accessible presentation of the basic concepts of a warehouse system

Developing safe autonomous robotic applications for outdoor agricultural environments is a research field that still presents many challenges. Simultaneous Localization and Mapping can be crucial to endow the robot to localize itself with accuracy and, consequently, perform tasks such as crop monitoring and harvesting autonomously. In these environments, the robotic localization and mapping systems

This paper presents a state-of-the-art filter that reduces the complexity in object detection, tracking and mapping applications. Existing edge detection and tracking methods are proposed to create suitable autonomy for mobile robots, however, many of them face overconfidence and large computations at the entrance to scenarios with an immense number of landmarks. The method in this work, the Line–Circle–Square

Multi-UAV system is widely used in surveillance, search and rescue, and industrial inspection. Multi-UAV trajectory planning is crucial for the multi-UAV system, but multi-UAV trajectory planning often needs to consider many constraints, such as trajectory smoothness, obstacle collisions, mutual collisions, dynamic limits, time-consuming, and trajectory length. It is a challenge to balance these constraints

Inferring the depth of images is a fundamental inverse problem within the field of Computer Vision since depth information is obtained through 2D images, which can be generated from infinite possibilities of observed real scenes. Benefiting from the progress of Convolutional Neural Networks (CNNs) to explore structural features and spatial image information, Single Image Depth Estimation (SIDE) is

Robots are increasingly exploited in production plants. Within the Industry 4.0 paradigm, the robot complements the human’s capabilities, learning new tasks and adapting itself to compensate for uncertainties. With this aim, the presented paper focuses on the investigation of machine learning techniques to make a sensorless robot able to learn and optimize an industrial assembly task. Relying on sensorless

In this paper, a Gaussian process-based nonlinear model predictive control (GP-based NMPC) algorithm is presented to deal with the visual servoing problem for constrained mobile robots. Firstly, a GP-enhanced model is established by incorporating a GP model and a visual servoing kinematic model where the GP-model is used to capture the robot dynamics with on-line updating. Then, a nonlinear model predictive

Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described

Programming a robot to deal with open-ended tasks remains a challenge, in particular if the robot has to manipulate objects. Launching, grasping, pushing or any other object interaction can be simulated but the corresponding models are not reversible and the robot behavior thus cannot be directly deduced. These behaviors are hard to learn without a demonstration as the search space is large and the

This paper presents a planning framework for jumping over obstacles with quadruped robots. The framework accomplishes planning via a structured predictive control strategy that combines the use of heterogeneous simplified models over different prediction time scales. A receding multi-horizon predictive controller coordinates the approach before the jump using a kinematic point-mass model. Consideration

This paper takes a novel look at formation control by comparing control setups based on two very different frameworks. These are applied to the distributed control of communicating omnidirectional mobile robots. One framework, which is possibly the most common approach to formation control, is based on algebraic graph theory, whereas the other, namely distributed model predictive control (DMPC), is

A great amount of effort has been devoted to the study on self-separation assurance approach for civil aviation in the airspace with increasing density. In this article, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is modified to make it work for autonomous and decentralized collision avoidance for civil aircraft. Without considering the direction selectivity of collision-free maneuver

Accurate robot localization represents a challenge inside pipes due to the particular conditions that characterize this type of environment. Outdoor techniques (GPS in particular) do not work at all inside metal pipes, while traditional indoor localization methods based on camera or laser sensors do not perform well mainly due to a lack of external illumination and distinctive features along pipes

This paper deals with an innovative autonomous bus navigation and parking system in a bus depot, in order to optimize their movements in a confined area. The kinematic model of the vehicle is defined. Considering its dimensions and weight as well as the centimetric accuracy required, a predictive controller is designed, based on its model linearized around the changing path curvature value, to perform

In the recent years, many studies claim that humans have a unique driving behavior style that could be used as a fingerprint in recognizing the identity of the driver. With the rising evolution of Machine Learning (ML), the research efforts aiming to take advantage of the human driving style identifiers have been increasing exponentially. For Advanced Driver Assistance Systems (ADAS), this attribute

Robotic simulators are often used to speed up the Evolutionary Robotics (ER) process. Most simulation approaches are based on physics modelling. However, physics-based simulators can become complex to develop and require prior knowledge of the robotic system. Robotics simulators can be constructed using Machine Learning techniques, such as Artificial Neural Networks (ANNs). ANN-based simulator development

Trajectory planning through dynamical systems (DS) provides robust control for robots and has found numerous applications from locomotion to manipulation. However, to date, DS for controlling rhythmic patterns are distinct from DS used to control point to point motion and current approaches switch at run time across these to enable multiple behaviors. This switching can be brittle and subject to instabilities

This paper deals with the formation flying control problem for a team of nonlinear uncertain quadrotors in presence of noisy measurements and environmental disturbances. A novel distributed output-feedback nonlinear robust algorithm is proposed to solve the problem. The algorithm leads to a series of combined estimation-control local policies with minimum communicated information by decomposing the

Pouring is one of the most commonly executed tasks in humans’ daily lives, whose accuracy is affected by multiple factors, including the type of material to be poured and the geometry of the source and receiving containers. In this work, we propose a self-supervised learning approach that learns the pouring dynamics, pouring motion, and outcomes from unsupervised demonstrations for accurate pouring

Aimed at the negative effect of dynamics characteristics of leg hydraulic drive system (LHDS) on the accuracy of motion control of the hydraulic drive legged robot, a dynamics compensation control method is proposed. First, according to the mechanical structure of LHDS, the kinematics and statics models of LHDS are analyzed and obtained respectively. Based on the principle of force-based impedance

The risk toward human lives in situations involving chemical, biological, radiological, and nuclear (CBRN) threats can be mitigated or even neutralized by deploying carrying a suite of suitable sensors. Furthermore, mobile robots open up the possibility for automated radiological field surveys and monitoring operations, which have important applications in scenarios with CBRN threats. A path planner

Efficient robotic behaviors require robustness and adaptation to dynamic changes of the environment, whose characteristics rapidly vary during robot operation. To generate effective robot action policies, planning and learning techniques have shown the most promising results. However, if considered individually, they present different limitations. Planning techniques lack generalization among similar

We present an experimental investigation of a multi-sensor fusion-learning system for detecting pedestrians in foggy weather conditions. The method combines two pipelines for people detection running on two different sensors commonly found on moving vehicles: lidar and radar. The two pipelines are not only combined by sensor fusion, but information from one pipeline is used to train the other. We build

Just like humans, robots can improve their performance by practicing, i. e. by performing the desired behavior many times and updating the underlying skill representation using the newly gathered data. In this paper, we propose to implement robot practicing by applying statistical and reinforcement learning (RL) in a latent space of the selected skill representation. The latent space is computed by

Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled to achieve long-term robot autonomy. In vision-based localization and navigation methods, one of the major issues is the scene dynamics. The autonomous operation of the robot may become unreliable if the changes occurring

Search and find mission in ocean environment is a none trivial operation given the amount of random parameters associated with it. The uncertain and dynamic aspects related to ocean current movement make the trajectory prediction of drifting lost object onto sea water a very complicated task. In this work we present a novel lost target searching algorithm based on Recursive Area Clustering and target

Magnetic actuation techniques and microrobots have attracted great interest since they have potential in biomedicine applications. Interventional techniques have emerged as a tool to handle a wide range of minimally invasive surgeries (MIS). However, current MIS procedures are constrained by the limitation of manual operation by surgeon. Thus, various microrobotic solutions including magnetic navigation

This paper presents an efficient multi-fingered grasping force optimization (GFO) method based on generalized penalty-function concepts. In view of the fact that the mainstream multi-fingered GFO method often treats the second-order cone programming (SOCP) problem as a semi-definite programming (SDP) problem, whose computational complexity is high, we hereby use the barrier function to construct the

It is one of the great challenges for a robot to learn compliant movements in interaction tasks. The robot can easily acquire motion skills from a human tutor by kinematics demonstration, however, this becomes much more difficult when it comes to the compliant skills. This paper aims to provide a possible solution to address this problem by proposing a two-stage approach. In the first stage, the human

Research on unmanned aerial vehicles is growing as they are becoming less expensive and more available than before. The applications span a large number of areas and include border security, search and rescue, wildlife surveying, firefighting, precision agriculture, structure inspection, surveying and mapping, aerial photography, and recreative applications. These applications can require autonomous

Animals learn to master their capabilities by trial and error, and with out having any knowledge about their dynamics model and mathematical or physical rules. They use their maximum capabilities in an optimized way. This is the result of millions of years of evolution where the best of different possibilities are kept, and makes us rethink How does the nature perform things?, particularly when natural

Dynamics performance is very important for a manipulator used for high-speed machining. In this paper, the dynamic performance evaluation method of the 2UPU/SP parallel mechanism in a hybrid robot for aerospace composite machining is studied. The dynamic model is obtained by the virtual work principle, and a dynamic performance index considering gravity is proposed. Based on the given performance index

Robustly estimating the orientations of people is a crucial precondition for a wide range of applications. Especially for autonomous systems operating in populated environments, the orientation of a person can give valuable information to increase their acceptance. Given people’s orientations, mobile systems can apply navigation strategies which take people’s proxemics into account or approach them

An environment cannot be effectively described with a single perception form in skill learning for robotic assembly. The visual perception may provide the object’s apparent characteristics and the softness or stiffness of the object could be detected using the contact force/torque information during the assembly process. In the process of inserting assembly strategy learning, most of the work takes

This report describes an automatic control system that allows an assistive robot pushing a wheelchair to climb steps. The robot is equipped with a wheeled mechanism and dual manipulators. The wheelchair is a commercially available model that has been equipped with sensors, circuits, and batteries. The robot and wheelchair are connected when the vehicles climb a step. In that operation, the front wheels

For effective verbal communication in collaborative tasks, robots need to account for the different perspectives of their human partners when referring to objects in a shared space. For example, when a robot helps its partner find correct pieces while assembling furniture, it needs to understand how its collaborator perceives the world and refer to objects accordingly. In this work, we propose a method

The collision-free navigation of a mobile robot in clutter environments is challenging. Global Positioning System (GPS) and adaptive neuro-fuzzy inference system (ANFIS) are well-known techniques widely used for navigation and control, respectively. This paper proposes a hybrid GPS-ANFIS based method for collision-free navigation of autonomous mobile robots. The GPS-based controller keeps the navigation

Anticipation of human movements is of great importance for service robots, as it is necessary to avoid interferences and predict areas where human–robot collaboration may be needed. In indoor scenarios, human movements often depend on objects with which they interacted before. For example, if a human interacts with a cup the probability that a table or coffee machine might be the next navigation goal

One of the problems that service robotics deals with is to bring mobile manipulators to work in semi-structured human scenarios, which requires an efficient and flexible way to execute every-day tasks, like serve a cup in a cluttered environment. Usually, for those tasks, the combination of symbolic and geometric levels of planning is necessary, as well as the integration of perception models with

The achievement of adaptive, stable, and robust locomotion and dealing with asymmetrical conditions for bipedal robots remain a challenging problem. To address the problem, this paper introduces adaptive parallel reflex- and decoupled central pattern generator (CPG)-based control for a planar bipedal robot. The control has modular structure consisting of two parallel modules that work together. Firstly

We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar. To increase the resolution of the point cloud captured by a sparse 3D lidar, we convert this problem from 3D Euclidean space into an image super-resolution problem in 2D image space, which

The FastSLAM is a typical tracking algorithm for SLAM, but it often suffers from the low tracking accuracy. To mitigate the problem, an improved H-Infinity unscented FastSLAM (IHUFastSLAM) with adaptive genetic resampling is proposed in this paper. Specifically, the H-Infinity unscented Kalman filter algorithm is improved using an adaptive factor and is employed as importance sampling in particle filter

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous