Journal of Intelligent & Robotic Systems

Ibrahim, Fady; Abouelsoud, A.; Fath El Bab, Ahmed; Ogata, Tetsuya

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0844-2

A discontinuous stabilizing control of Skid-Steering Mobile Robot (SSMR) is proposed using σ transformation introduced in Astolfi (Syst. Control Lett. 27(1), 37–45, 1996). A linear time-invariant system (LTI) is obtained which is driven by state-dependent disturbance. A linear H ∞ controller is designed to reduce the effect of this disturbance. Two control transformations are carried out in order to bring the system in a form suitable for σ transformation; one for the case of SSMR orientation around 0 and π and the other around ± π/2. The resulting two controllers for the two cases are blended using fuzzy logic. The closed-loop system is simulated using Matlab environment on point stabilization from different initial conditions. Results show that the proposed controller guarantees asymptotic stability with smooth paths. Experimental results are consistent with simulation and show that the proposed controller succeeded to stabilize the SSMR to the desired point without shuttering.

Wu, Hsiu-Ming; Karkoub, Mansour

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0886-5

In this paper, a hierarchical variable structure control (HVSC) is proposed for path following and formation maintenance of multi-agent systems (MAS) based on the one leader and one follower (1L-1F) configuration. In terms of the leader, the path following of a nonholonomic mobile robot (NMR) can be regarded as tracking of a virtual reference NMR. Then, a feedback control law is used to attain path following based on kinematics of the NMR. Subsequently, a variable structure controller (VSC) with known upper bound of the disturbances is designed to achieve velocity control based on the dynamics of the NMR. Furthermore, as far as the formation maintenance is concerned, a method of feedback linearization is utilized to exponentially stabilize the relative distance and orientation between the leader and follower. Similarly, a VSC is also designed for complete velocity control such that the aforementioned formation is maintained. The proposed method with hierarchical structure simultaneously attains the path following of the leader and maintains the formation for the MAS. In addition, the robustness of the proposed scheme is guaranteed in spite of persistent disturbances and the stability analysis of the closed-loop system is proved via Lyapunov stability criteria. Finally, computer simulations are conducted to validate the feasibility and effectiveness of the proposed control scheme.

Harder, Scott; Lauderbaugh, Leal

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0912-7

The work presented in this paper presents a general method for formation shape specification and proves convergence for a class of parametrically defined formations with closed form, nearest-point solutions. In addition to a more general specification of formation shape, the method results in a system that is robust in the presence of a variable number of agents. Each agent observes the positions of its neighbors and independently constructs a formation curve about the center of its neighborhood. The attractive point on this formation curve is found by employing a local minimization with respect to the observed center of mass of an agent’s neighborhood rather than a fixed global field. Given a common objective or goal state, this approach results in a general method to drive a time varying number of agents into a desired geometric configuration without specific individual locational or structural pre-assignment. The application of LaSalle’s invariance principal to the system’s Hamiltonian shows stability and convergence of the flock to the desired configuration. Simulation results verify convergence and robustness to instantaneous changes in the number of agents.

Lashkari, Negin; Biglarbegian, Mohammad; Yang, Simon

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0900-y

The motion planning and control of physically connected robots, e.g., docked mobile robots (DMR), is a challenging problem due to robots’ underactuated and nonlinear dynamics as well as their physical constraints. The majority of the motion planning approaches developed for DMR are not robust to robots’ mass difference and are only applicable to static environments. This paper proposes a novel motion planning methodology for a DMR, consisting of a nonholonomic circular-shape leader and N holonomic passive-wheels active-joints circular-shape followers, through developing: (i) a trajectory planner to determine the motion of docked followers for tracking a desired path, (ii) a robust motion controller to navigate DMR through the planned trajectory, and (iii) a collision avoidance strategy to provide a collision-free transit for followers in dynamic environments. We propose two novel approaches for collision avoidance: a reactive approach which is a decentralized method that utilizes robots’ on-board measurement sensors, and a cooperative approach which is a centralized approach that uses environment information (e.g. obstacle locations) to prevent imminent collisions. In the reactive approach, the collision avoidance strategy is comprised of two control laws, one for obstacle avoidance and the other for satisfying joint constraints. In the cooperative approach, the collision avoidance strategy replans a collision-free trajectory for docked followers, and then deploys our trajectory planner and motion controller to navigate the DMR through the trajectory. The performance of the proposed reactive and cooperative approaches were shown and compared through simulations as well as implementation in a virtual robot experimentation platform (V-REP). The results showed that while the reactive approach is more efficient in terms of computation time and energy consumption, the cooperative approach requires less lateral deviation for avoiding the obstacles which is beneficial for operation in confined spaces. We finally compared our motion planning methodology with other existing methods in the literature. This comparison proved that our method is applicable to complex paths in dynamic environments, scalable with the number of followers, robust to various robots’ masses, and more computationally efficient.

Klug, Christoph; Schmalstieg, Dieter; Gloor, Thomas; Arth, Clemens

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0931-4

Development and verification of real-time algorithms for robotic total stations usually require hard-ware-in-the-loop approaches, which can be complex and time-consuming. Simulator-in-the-loop can be used instead, but the design of a simulation environment and sufficient detailed modeling of the hardware are required. Typically, device specification and calibration data are provided by the device manufacturers and are used by the device drivers. However, geometric models of robotic total stations cannot be used directly with existing ro-botic simulators. Model details are often treated as company secrets, and no source code of device drivers is available to the public. In this paper, we present a complete workflow for automatic geometric model extraction of robotic total stations using the Denavit-Hartenberg convention. We provide a complete set of Denavit-Hartenberg parameters for an exemplary ro-botic total station. These parameters can be used in existing robotic simulators without modifications. Furthermore, we analyze the difference between the extracted geometric model, the calibrated model, which is used by the device drivers, and the standard spherical representation for 3D point measurements of the device.

Cho, Baek-Kyu; Ahn, DongHyun; Jun, YoungBum; Oh, Paul

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0928-z

In order to use humanoid robots in our daily lives, stable robot walking is very important. This paper proposes a posture balance controller for a humanoid robot in order to achieve stable locomotion. The robot was modeled in simplified form as an inverted pendulum having a spring and a damper and the state feedback controller based on a disturbance and a state observer estimating the angle and angular velocity of the center of mass (COM) was developed with the simple model. Since a humanoid robot has different modeling parameters according to a number of the supporting legs and/or moving direction, four controllers were designed. With considering disturbance, the robot could estimated the state exactly and maintained the posture balance while disturbance is applied to the robot. The proposed controller was applied to a humanoid robot, DRC-HUBO2, and it was verified with some experiments in the lab and success of the stair mission in the DRC Finals 2015.

Choi, Mun-Taek; Yeom, Jinseob; Shin, Yunhee; Park, Injun

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0890-9

This paper presents a novel approach to screening children with ADHD, one of the most prevalent childhood disorders. We have designed the robot-assisted, game-like test that directly reflects children’s behavior on measuring ADHD symptoms. Using the sensors in the robot system, most of the children’s behavior is automatically measured during the entire course of the test. We collected real data by carrying out tests to 326 children from 3rd to 4th grades in the field. A unified frame that classifies multiple categories of ADHD, ADHD-at-Risk and normal has been set up to investigate a wide spectrum of classifiers and their optimal hyper-parameters. The results of the data analysis show highly confident ADHD classification, up to 97% (F1 score). It could be a practical tool for clinicians and special teachers to use in the diagnosis of childhood ADHD.

Croce, Mauro; Pire, Taihú; Bergero, Federico

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0913-6

This paper presents DS-PTAM, a distributed architecture for the S-PTAM stereo SLAM system. This architecture is developed on the ROS framework, separating the localization and mapping tasks into two independent ROS nodes. The DS-PTAM system is ideal for mobile robots with low computing power because it allows to run the localization module on-board and the mapping module —which has a higher computational cost— on a remote base station, relieving the load on the on-board processor. The proposed architecture was implemented based on the original S-PTAM monolithic code and then validated through different experiments on public datasets. The results obtained show the feasibility of the proposed distributed architecture, its correct implementation and the benefits of distributing the computational load on several computers.

Wang, Jiangbei; Fei, Yanqiong

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0957-7

This paper presents design and modelling of a flex-rigid soft robot for flipping locomotion. The proposed robot is made into a strip shape and consists of three rigid limbs connected by two active flexible hinges. Its flipping locomotion is achieved by active folding and developing of the hinges. To validate its locomotion ability, we build a state-space model to simulate its dynamics, which is compared with the experimental data. The results show that the proposed flex-rigid robot can perform flipping locomotion with average velocity of 59mm/s in simulation and 60mm/s in experiment, and the model can predict its movement effectively.

Zhang, Xiwu; Wang, Lei; Zhao, Yan; Su, Yan

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0917-2

Visual place recognition is a critical and challenging problem in both robotics and computer vision communities. In this paper, we focus on place recognition for visual Simultaneous Localization and Mapping (vSLAM) systems. These systems have been limited to handcrafted feature based paradigms for a long time, which normally use local visual information of images and are not sufficiently robust against variations applied to images. In this work, we address place recognition with the features automatically learned from data. First, we propose a graph-based visual place recognition method. The graph is constructed by combining the visual features extracted from convolutional neural networks (CNNs) and the temporal information of the images in a sequence. Second, we propose to employ diffusion process to enhance the data association in the graph to achieve more accurate recognition results. Finally, to evaluate the proposed method, we experiment on four commonly used datasets. Experimental results indicate that the proposed method is able to obtain significantly better performance (e.g. 95.37% recall at 100% of precision) than that of FAB-MAP (47.16% recall at 100% of precision), a commonly used method for place recognition based on handcrafted features, especially on some challenging datasets.

Guo, Qingda; Quan, Yanming; Jiang, Changcheng

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0940-3

The accommodation space changes as flexible products are packed into it. In order to improve the automatic loading of containers, it is necessary to solve the problem of object pose estimation in accommodation space. The goal of this study is to establish a method for pose estimation of a target object in the accommodation space. Firstly, the paper introduces basic algorithms and concepts, including the quick hull (Qhull) algorithm, oriented bounding box (OBB) algorithm, and fruit fly optimization algorithm (FOA). Secondly, the constraint conditions and the objective function of pose estimation are set up according to the pose variables in three-dimensional (3D) space, and a solution method for pose estimation is established using an improved FOA. Then, the algorithms with different population parameters are simulated, and the optimal parameters are obtained. The bounding box algorithm is used for system optimization, whereas a convex hull is used to simplify the target object significantly, reducing the corresponding running time. Finally, the hardware platform of the industrial robot is established, the initial and final poses of the end-effector are obtained using the proposed method, and tests are performed for different cases. The results show that the application of convex hull algorithm can significantly simplify a target object reducing the running time, and half individuals of the population guide the entire population to search for an optimal pose (6 degrees of freedom) in accommodation space.

Huskić, Goran; Buck, Sebastian; Zell, Andreas

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0951-0

We present GeRoNa (Generic Robot Navigation), a modular navigation framework for wheeled mobile robots. This framework supports many different kinematic configurations of wheeled robots and was experimentally verified on eight different real-world robotic platforms, including Ackermann steering, bi-steerable, skid-steered, differentially-driven and omnidirectional vehicles. The real-world experiments include indoor and outdoor tests, on various terrain types, driving up to 6 m/s. The framework provides A*-based path planning algorithms, high speed obstacle avoidance (tested at speeds up to 2.5 m/s) and twelve different control algorithms for path following. In this paper, we present the whole framework, detail every controller and provide an extensive experimental evaluation of the most important components. The entire framework is already open-source available, written in C++ and based on ROS (Robot Operating System).

Batista, V.; Zampirolli, F.

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0953-y

Demand for Genetic Algorithms (GA) in research and market applications has been increasing considerably. This can be explained through big data and the necessity of interpreting them in an automatic, efficient and intelligent way. In the case of intelligent systems for unmanned vehicles there are two well-defined subsystems: autonomous and robotic navigation. Despite the present day’s good development of the latter, taking the best decisions is an essential robot’s attribute that can only be acquired with the former. This work presents a fully programmed GA for a robot to walk around a planar graph G with the highest efficiency. Each robot’s action is one among five kinds of genes, and fourteen of them build a chromosome, namely a sequence of actions for the robot to walk all around G. The efficiency of a chromosome is given by the number of visited vertices and the amount of saved energy, which are both computed by a fitness function. Our GA returns near-optimal chromosomes for the robot to clean the whole reflection pool with the least energy consumption. Our Coverage Path Planning differs from others in the literature because they consider obtaining a near-optimal sequence of vertices for the robot to follow in that order. Moreover, for such a sequence they do not allow vertex repetitions, whereas in our developed algorithm the robot can pass more than once in a same vertex, with the objective of guaranteeing a lower energy consumption. This objective already makes our best chromosomes avoid repeating vertices, as we have observed in our experiments.

Bouguerra, Mohamed; Fraichard, Thierry; Fezari, Mohamed

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0955-9

Guaranteeing safe, i.e. collision-free, motion for robotic systems is usually tackled in the Inevitable Collision State (ICS) framework. This paper explores the use of the more general Viability theory as an alternative when safe motion involves multiple motion constraints and not just collision avoidance. Central to Viability is the so-called viability kernel, i.e. the set of states of the robotic system for which there is at least one trajectory that satisfies the motion constraints forever. The paper presents an algorithm that computes off-line an approximation of the viability kernel that is both conservative and able to handle time-varying constraints such as moving obstacles. Then it demonstrates, for different robotic scenarios involving multiple motion constraints (collision avoidance, visibility, velocity), how to use the viability kernel computed off-line within an on-line reactive navigation scheme that can drive the robotic system without ever violating the motion constraints at hand.

Shen, Tao; Nelson, Carl; Bradley, Justin

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0836-2

Cross-coupled synchronization is an effective method of controlling an articulated robot especially in applications with restrictive requirements and low tolerance to error. Model-free methods of cross-coupled synchronization provide similar performance in cases where models are difficult or impossible to obtain. Here a novel model-free cross-coupled adaptive synchronization method is developed and applied to a Natural Orifice Transluminal Endoscopic Surgery (NOTES) robot - where reducing contour error has the important benefit of reducing the risk of surgical error and improving patient outcomes. To accomplish this, a baseline model-free cross coupled strategy is used, and an adaptive control gain and a balance scaling factor are used to improve the performance. Experiments are then performed validating the functionality and effectiveness of the controller using a NOTES robot. The results show significant improvement in decreasing contour error when compared with similar methods.

Gośliński, Jarosław; Kasiński, Andrzej; Giernacki, Wojciech; Owczarek, Piotr; Gardecki, Stanisław

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0857-x

The parametrized model of the Unmanned Aerial Vehicle (UAV) is a crucial part of control algorithms, estimation processes and fault diagnostic systems. Among plenty of available methods for model structure or model parameters estimation, there are a few, which are suitable for nonlinear UAV models. In this work authors propose an estimation method of parameters of the coaxial quadrotor’s orientation model, based on the Square Root Unscented Kalman Filter (SRUKF). The model structure with different aerodynamic aspects is presented. The model is enhanced with various friction types, so it reflects the real quadrotor characteristics more precisely. In order to validate the estimation method, the experiments are conducted in a special hall and essential data is gathered. The research shows that the SRUKF, can provide fast and reliable estimation of the model parameters, however the classic method may lead to serious instabilities. Necessary modifications of the estimation algorithm are included, so the approach is more robust in terms of numerical stability. The resultant method allows for dynamics of selected parameters to be changed and is proved to be adequate for on-line estimation. The studies reveals tracking properties of the algorithm, which makes the method more viable.

Sebastian, Bijo; Ben-Tzvi, Pinhas

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0851-3

This paper describes a novel physics-based path planning architecture for autonomous navigation of tracked vehicles in rough terrain conditions. Unlike conventional path planning applications for smooth and structured environments, factors such as slip, slope of the terrain, robot actuator limitations, and dynamics of robot terrain interactions must be considered for rough terrain applications. The proposed path planning method consists of a hybrid planner/simulator, which takes into account all of the above factors by simulating the closed loop motion of the robot with a low-level controller on a realistic terrain model inside a physics engine. Once a feasible path to the goal is obtained, the same low-level closed loop controller is then used to execute the proposed path on the actual robot. The proposed architecture uses the D* Lite algorithm working on a 2D grid representation of the terrain as the high-level planner, Bullet as the physics engine and a hybrid automaton as the low-level closed loop controller. The proposed method is validated both in simulation and through experiments. Inferences based on the results from simulations and experiments show that the proposed planner is more effective in providing an optimal feasible path as compared to existing methodologies, demonstrating clear advantages for rough, unstructured terrain planning. Based on the results, possible improvements to the method are proposed for future work.

Brown, Liam; Carrasco, Joaquin; Watson, Simon; Lennox, Barry

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0904-7

Nuclear decommissioning is a global challenge with high costs associated with it due to the hazardous environments created by radioactive materials. Most nuclear decommissioning sites contain significant amounts of pipework, the majority of which is uncharacterised with regards radioactive contamination. If there is any uncertainty as to the contamination status of a pipe, it must be treated as contaminated waste, which can lead to very high disposal costs. To overcome this challenge, an in-pipe autonomous robot for characterisation is being developed. One of the most significant mechatronic challenges with the development of such a robot is the detection of elbows in the unknown pipe networks to allow the robotic system to autonomously navigate around them. This paper presents a novel method of predicting the direction and radius of the corner using whisker-like sensors. Experiments have shown that the proposed system has a mean error of 4.69∘ in the direction estimation.

Santos Junior, Carlos; Carvalho, José; Souza, Fernando; Savino, Heitor

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0782-z

This paper presents an analysis method, based on linear matrix inequalities, for consensus with estimated convergence rate, in the presence of input delays. It is assumed that the delays are nonuniform, time-varying, and possibly non-differentiable. The proposed approach consists in rewriting the multi-agent system as a reduced-order delayed linear system, such that consensus can be analyzed by means of Lyapunov-Krasovskii stability theory. Finally, the efficiency of the proposed method is verified by numerical simulations.

Venkateswara Rao, D.; Yanhua, Wu

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0807-7

This paper presents the investigation of the use of position-sensing diode (PSD) - a light source direction sensor - for designing a vision-based navigation system for a perching aircraft. Aircraft perching maneuvers mimic bird’s landing by climbing for touching down with low velocity or negligible impact. They are optimized to reduce their spatial requirements, like altitude gain or trajectory length. Due to disturbances and uncertainties, real-time perching is realized by tracking the optimal trajectories. As the performance of the controllers depends on the accuracy of estimated aircraft state, the use of PSD measurements as observations in the state estimation model is proposed to achieve precise landing. The performance and the suitability of this navigation system are investigated through numerical simulations. An optimal perching trajectory is computed by minimizing the trajectory length. Accelerations, angular-rates and PSD readings are determined from this trajectory and then added with experimentally obtained noise to create simulated sensor measurements. The initial state of the optimal perching trajectory is perturbed, and by assuming zero biases, extended Kalman filter is implemented for aircraft state estimation. It is shown that the errors between estimated and actual aircraft states reduce along the trajectory, validating the proposed navigation system.

Ireland, Murray; Anderson, David

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0824-6

Unmanned aircraft such as multirotors are typically limited in endurance by the need to minimise weight, often sacrificing power plant mass and therefore output. Wireless power transmission is a method of delivering power to such aircraft from an off-vehicle transmitter, reducing weight whilst ensuring long-term endurance. However, transmission of high-powered lasers in operational scenarios carries significant risk. Station-keeping of the laser spot on the receiving surface is crucial to both ensuring the safety of the procedure and maximising efficiency. This paper explores the use of trajectory optimisation to maximise the station-keeping accuracy. A multi-agent model is presented, employing a quadrotor unmanned rotorcraft and energy transmission system, consisting of a two-axis gimbal, camera sensor and laser emitter. Trajectory is parametrised in terms of position and velocity at the extremes of the flight path. The optimisation operates on a cost function which considers target range, beam angle of incidence and laser spot location on the receiving surface. Several cases are presented for a range of variables in the trajectory and different conditions in the model and optimisation algorithm. Results demonstrate the viability of this approach in minimising station-keeping errors.

Parayil, Anjaly; Ratnoo, Ashwini

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0873-x

The problem of aerial rendezvous of Unmanned Aerial Vehicles (UAVs) is considered. Beam rider approach, wherein the follower moves along a beam directed from a ground-based tracker onto the leader is proposed as a guidance strategy. Analytic guarantee for a resulting rendezvous between two same speed vehicles is derived from the line-of-sight guidance principles. Considering an approximate variation of the follower look-ahead angle, closed-form expressions are derived for time-to-rendezvous and follower lateral acceleration. Cooperative maneuvers are proposed for the leader minimizing the rendezvous engagement time. Guidance models are extended to 3D engagements and efficacy of the proposed method is demonstrated by extensive 2D and 3D simulations. Simulation results are presented complying with the analytic findings. Robustness of the proposed approach is verified against uncompensated autopilot delays, non-identical initial speeds, and wind.

Sampedro, Carlos; Rodriguez-Ramos, Alejandro; Bavle, Hriday; Carrio, Adrian; Puente, Paloma; Campoy, Pascual

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0898-1

Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions.

Primatesta, Stefano; Guglieri, Giorgio; Rizzo, Alessandro

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0924-3

This paper presents a risk-aware path planning strategy for Unmanned Aerial Vehicles in urban environments. The aim is to compute an effective path that minimizes the risk to the population, thus enforcing safety of flight operations over inhabited areas. To quantify the risk, the proposed approach uses a risk-map that associates discretized locations of the space with a suitable risk-cost. Path planning is performed in two phases: first, a tentative path is computed off-line on the basis on the information related to static risk factors; then, using a dynamic risk-map, an on-line path planning adjusts and adapts the off-line path to dynamically arising conditions. Off-line path planning is performed using riskA*, an ad-hoc variant of the A* algorithm, which aims at minimizing the risk. While off-line path planning has no stringent time constraints for its execution, this is not the case for the on-line phase, where a fast response constitutes a critical design parameter. We propose a novel algorithm called Borderland, which uses the check and repair approach to rapidly identify and adjust only the portion of path involved by the inception of relevant dynamical changes in the risk factor. After the path planning, a smoothing process is performed using Dubins curves. Simulation results confirm the suitability of the proposed approach.

Patruno, C.; Nitti, M.; Petitti, A.; Stella, E.; D’Orazio, T.

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0933-2

In this paper we present an on-board Computer Vision System for the pose estimation of an Unmanned Aerial Vehicle (UAV) with respect to a human-made landing target. The proposed methodology is based on a coarse-to-fine approach to search the target marks starting from the recognition of the characteristics visible from long distances, up to the inner details when short distances require high precisions for the final landing phase. A sequence of steps, based on a Point-to-Line Distance method, analyzes the contour information and allows the recognition of the target also in cluttered scenarios. The proposed approach enables to fully assist the UAV during its take-off and landing on the target, as it is able to detect anomalous situations, such as the loss of the target from the image field of view, and the precise evaluation of the drone attitude when only a part of the target remains visible in the image plane. Several indoor and outdoor experiments have been carried out to demonstrate the effectiveness, robustness and accuracy of developed algorithm. The outcomes have proven that our methodology outperforms the current state of art, providing high accuracies in estimating the position and the orientation of landing target with respect to the UAV.

Khamukhin, A.

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0960-z

We verified the validity of a bio-inspired strategy for visually guided landing and its mathematical model proposed M.V. Srinivasan et al. by numerical simulation. We studied the influence of temporal discretization and the values of the supported optical flow on the landing duration and its result (from smooth to crash). An algorithm for landing simulation was developed taking into account accepted assumptions of the model. Two formulas (sine and tangent) were derived to calculate the distance and the speed of the flying robot, ensuring the constancy of the optical flow at given time steps. A limitation was found in the very value of the optical flow (threshold value), when exceeding this, the strategy leads to a hard touchdown or a crash (at near zero distance the speed is not close to zero). It was shown that the threshold value of the optical flow decreases with increasing time step in both formulas. However, calculating the distance using sine formula has a significantly lower threshold value of the optical flow than the calculation using the tangent formula. It was found that landing occurs faster if we use the sine formula at equal values of the optical flow. Nevertheless, the smooth landing ends at lower threshold values of the optical flow than using the tangent formula. As a result, using a larger value of the optical flow, a faster smooth landing can be achieved using the tangent formula.

Albert, Anders; Imsland, Lars

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0915-4

Combined searching and tracking of objects using Unmanned Aerial Vehicles (UAVs) is an important task with many applications. One way to approach this task is to formulate path-planning as a continuous optimal control problem. However, such formulations will, in general, be complex and difficult to solve with global optimality. Therefore, we propose a two-layer framework, in which the first layer uses a Traveling-Salesman-type formulation implemented using combinatorial optimization to find a near-globally-optimal path. This path is refined in the second layer using a continuous optimal control formulation that takes UAV dynamics and constraints into consideration. Searching and tracking problems usually trade-off, often in a manual or ad-hoc manner, between searching unexplored areas and keeping track of already known objects. Instead, we derive a result that enables prioritization between searching and tracking based on the probability of finding a new object weighted against the probability of losing tracked objects. Based on this result, we construct a new algorithm for searching and tracking. This algorithm is validated in simulation, where it is compared to multiple base cases as well as a case utilizing perfect knowledge of the positions of the objects. The simulations demonstrate that the algorithm performs significantly better than the base cases, with an improvement of approximately 5-15%, while it is approximately 20-25% worse than the perfect case.

Bouzid, Y.; Bestaoui, Y.; Siguerdidjane, H.

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0914-5

In this paper, a Guidance-Control System (GCS) for optimal coverage planning, using a quadrotor, in damaged area is considered. The quadrotor is assumed to visit a set of reachable points, defined manually by the user or automatically generated, following the shortest path while avoiding the no-fly zones. The problem is solved by using a two-stage proposed algorithm. In the first stage, a novel tool for cluttered environments based on optimal Rapidly-exploring Random Trees (RRT) approach, called Multi-RRT* Fixed Node (RRT*FN), is developed to define the shortest paths from each point to its neighbors. By means of the pair-wise costs between points provided by the first-stage algorithm, in the second stage, the overall shortest path is obtained by solving a Traveling Salesman Problem (TSP) using Genetic Algorithms (GA). Taking into consideration the limited onboard energy, multi-rounds for the coverage planning are assumed as an alternative by adapting our problem as a Vehicle Routing Problem (VRP). This latter is solved using the savings heuristic approach. The guidance module is supported by an efficient controller that minimizes the consumed energy and allows a damped response (i.e. without overshoot). It is a reference model based control strategy called Interconnection Damping Assignment-Passivity Based Control (IDA-PBC). The effectiveness of the overall system is demonstrated via numerical simulations and confirmed experimentally with very promising results.

Ribeiro, Tiago; Conceição, André

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0896-3

This paper proposes a novel approach to the visual path following problem based on Nonlinear Model Predictive Control. Simplified visual features are extracted from the path to be followed. Then, aiming to calculate the control actions directly from the image plane, a regulatory model is obtained in the optimal control problem scope. For this purpose a Serret-Frenet system is placed in the center of camera’s field of view and the optimal control actions generate velocity references to an inner loop embedded in the robot. Stability issues are handled through a classical method and a new approach based on constraints relaxation is proposed in order to guarantee feasibility. Experimental results with a nonholonomic platform illustrate the performance of the proposed control scheme.

Li, Bingxi; Moridian, Barzin; Kamal, Anurag; Patankar, Sharvil; Mahmoudian, Nina

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0897-2

The success of numerous long-term robotic explorations in the air, on the ground, and under the water is dependent on the ability of robots to operate for an extended time. The long-term ubiquitous operation of robots hinges on smart energy consumption and the replenishment of the robots. This paper provides a heuristic method for planning missions that extend over multiple battery lives of working robots. This method simultaneously generates energy efficient trajectories for multiple robots, and schedules energy cycling using static charging stations through the mission. The mission planning algorithm accounts for environmental obstacles, current, and can adapt to a priority search distribution. The simulation results for a scenario similar to the MH370 airplane search mission demonstrate the effectiveness of the developed algorithm in area coverage and handling environmental constraints. The robustness of the developed method is evaluated through a Monte Carlo simulation. In addition, the proposed algorithm is tested in simulation environment in Gazebo and implemented and experimentally validated for an in-lab aerial coverage scenario with an obstacle and a priority mission area.

Sánchez-Ramírez, Edwards; Rosales-Silva, Alberto; Vianney-Kinani, Jean; Alfaro-Flores, Rogelio

2018 Journal of Intelligent & Robotic Systems

doi: 10.1007/s10846-018-0926-1

In recent years, visual tracking algorithms based on state estimators have been developed in order to improve the performance during tracking tasks. However, this performance changes according to target type, object kinematics and scenario complexity. When working with high-speed maneuvering targets, tracking errors increase considerably due to low response of estimators as well as the kinematic mismatch betwen the real motion profile and the one assumed by the estimator. Some examples of objects that present this high-speed behavior are rockets, aircrafts and missiles. To overcome this visual tracking problem, this work proposes an interacting multiple model algorithm based on four kinematic models: constant velocity, constant acceleration, constant turn and thrust acceleration. We present three different scenarios with complex maneuvers for comparison study, and experimental results show that visual tracking is improved when using the proposed strategy.