This paper proposes a new kinematic model for wheeled skid-steer vehicles moving with low inertia on firm slopes that is based on the variation of the Instantaneous Center of Rotation (ICR) of its lateral treads. To this end, the ICR-based model for horizontal surfaces, where constant tread ICRs were considered, has been extended with two additional parameters to account for the sliding down phenomenon on inclined terrains that the former is unable to predict. The current pitch and roll angles of the vehicle together with the speeds of the treads are employed to calculate the changing positions of tread ICRs during turnings. This kinematic approach shows promising results when applied to the heavy robotic rover J8 on a slanted surface of smooth concrete.

For mobile robots to operate in an autonomous and safe manner they must be able to adequately perceive their environment despite challenging or unpredictable conditions in their sensory apparatus. Usually, this is addressed through ad-hoc, not easily generalizable Fault Detection and Diagnosis (FDD) approaches. In this work, we leverage Bayesian Networks (BNs) to propose a novel probabilistic inference architecture that provides generality, rigorous inferences and real-time performance for the detection, diagnosis and recovery of diverse and multiple sensory failures in robotic systems. Our proposal achieves all these goals by structuring a BN in a multidimensional setting that up to our knowledge deals coherently and rigorously for the first time with the following issues: modeling of complex interactions among the components of the system, including sensors, anomaly detection and recovery; representation of sensory information and other kinds of knowledge at different levels of cognitive abstraction; and management of the temporal evolution of sensory behavior. Real-time performance is achieved through the compilation of these BNs into feedforward neural networks. Our proposal has been implemented and tested for mobile robot navigation in environments with human presence, a complex task that involves diverse sensor anomalies. The results obtained from both simulated and real experiments prove that our architecture enhances the safety and robustness of robotic operation: among others, the minimum distance to pedestrians, the tracking time and the navigation time all improve statistically in the presence of anomalies, with a diversity of changes in medians ranging from ≃20% to ≃500%.

Planning safe and effective trajectories for off-road unmanned ground vehicles (UGV) is a critical Artificial Intelligence (AI) challenge that can benefit from recent advances in digital elevation models (DEM) for readily capturing accurate terrain geometry. Considering path slopes is crucial to preserve stability and assess terrain traversability at feasible speeds to optimize travel time, which is highly dependent on direction (i.e., pitch and roll). In this article, we propose a new DEM-based asymmetric inclination-aware (DEM-AIA) trajectory planner for ground vehicles. The planner is an any-angle variant of the A⋆ algorithm that computes pitch and roll estimations for each segment crossing cell triangles in the line-of-sight. Furthermore, we define a non-linear velocity constraints function that integrates information about tip-over safety limitations, maximum uphill and downhill slopes for the vehicle, and asymmetric modulation of nominal flat-ground velocity for all pitch and roll combinations. The planner produces a time sub-optimal trajectory with feasible speed references for each segment crossing a cell triangle. Moreover, we provide an extensive experimental analysis of inclination-aware performance on simulated and real-world DEMs as well as a comparison with state-of-the-art path planners adapted to travel-time optimization. An executable version of the planner with parameterizable variations is publicly available.

Autonomous round trip missions arise as an interesting topic since ESA and NASA agreed to bring soil samples from Mars. This work proposes a new method to improve autonomous rover guidance for this kind of missions. It is focused on the use of dynamically updated cost maps that are used to plan the rover path for a round-trip. The main advantage of the proposed method is the use of gathered information by the rover during the traverse. So, the cost map is updated in two ways, the first one, related to the encountered obstacles, which are included within the cost map; and the second one that uses terrain features to assign different costs to different patches of a segmented terrain. The generated cost map is then used to plan the return path based on the previously obtained information from the rover. The proposed method has been validated first in a simulation environment with Software in the Loop. The implementation is finally validated by in-field testing in a Mars-like environment. Results show the return traverse is improved by means of the proposed method.

The emergence of new robotic technologies such as compliant control and soft robotics, has contributed to safe physical Human-Robot Interaction (pHRI) mainly for assistive applications. However, a robot capable of directly manipulating the human body, which is key for the implementation of autonomous rescue robots, has not been developed so far. In this paper, the development of a gripper and methods for the robotic manipulation of a laying victim's forearm, initiated by the robot is addressed, and validated based on experimental results. An underactuated gripper with added proprioceptive sensors has been designed, with environment sensing and tactile recognition capabilities. This method provides a stable grasping of a human forearm that lays on a surface and is capable of estimating the roll angle of the grasped arm for precise location and safe manipulation. The roll-angle estimation method is based on Machine Learning and has been trained with experimental data obtained from experiments with human volunteers. The resulting method provides robust and precise grasping, tolerant to location inaccuracy with inexpensive sensors. This is one of the very first works on the robotic human-body manipulation.

Search and rescue operations in the context of emergency response to human or natural disasters have the major goal of finding potential victims in the shortest possible time. Multi-agent teams, which can include specialized human respondents, robots and canine units, complement the strengths and weaknesses of each agent, like all-terrain mobility or capability to locate human beings. However, efficient coordination of heterogeneous agents requires specific means to locate the agents, and to provide them with the information they require to complete their mission. The major contribution of this work is an application of Wireless Sensor Networks (WSN) to gather information from a multi-agent team and to make it available to the rest of the agents while keeping coverage. In particular, a canine agent has been equipped with a mobile node installed on a harness, providing information about the dog's location as well as gas levels. The configuration of the mobile node allows for flexible arrangement of the system, being able to integrate static as well as mobile nodes. The gathered information is available at an external database, so that the rest of the agents and the control center can use it in real time. The proposed scheme has been tested in realistic scenarios during search and rescue exercises.

A new robotic system for Search And Rescue (SAR) operations based on the automatic wristband placement on the victims' arm, which may provide identification, beaconing and remote sensor readings for continuous health monitoring. This paper focuses on the development of the automatic target localization and the device placement using an unmanned aerial manipulator. The automatic wrist detection and localization system uses an RGB-D camera and a convolutional neural network based on the region faster method (Faster R-CNN). A lightweight parallel delta manipulator with a large workspace has been built, and a new design of a wristband in the form of a passive detachable gripper, is presented, which, under contact, automatically attaches to the human, while disengages from the manipulator. A new trajectory planning method has been used to minimize the torques caused by the external forces during contact, which cause attitude perturbations. Experiments have been done to evaluate the machine learning method for detection and location, and for the assessment of the performance of the trajectory planning method. The results show how the VGG-16 neural network provides a detection accuracy of 67.99%. Moreover, simulation experiments have been done to show that the new trajectories minimize the perturbations to the aerial platform.

Multi-beam lidar (MBL) scanners are compact, light, and accessible 3D sensors with high data rates, but they offer limited vertical resolution and field of view (FOV). Some recent robotics research has profited from the addition of a degree-of-freedom (DOF) to an MBL to build rotating multibeam lidars (RMBL) that can achieve high-resolution scans with full spherical FOV. In a previous work, we offered a methodology to analyze the complex 3D scan measurement distributions produced by RMBLs with a rolling DOF and no pitching. In this paper, we investigate the effect of introducing constant pitch angles in the construction of the RMBLs with the purpose of finding a kinematic configuration that optimizes scan homogeneity with a spherical FOV. To this end, we propose a scalar index of 3D sensor homogeneity that is based on the spherical formulation of Ripley's K function. The optimization is performed for the widely used Puck (VLP-16) and HDL-32 sensors by Velodyne.

This paper addresses education on scientific publication skills for post-graduate engineering students. In particular, a project-based learning strategy is proposed to lead students through the preparation a research paper. Expected learning outcomes are related to finding and evaluating the quality of references, editing and formatting text in LATEX, writing scientific papers with appropriate style and structure, peer reviewing, and making technical presentations. This approach has been developed to increase the internationalization and visibility of young researchers by improving the quality standards of their published works. The purpose of this innovative practice is that students confront the major challenges of the publication process in the classroom, which is in contrast with the traditional self-taught approach experienced by the authors and many senior researchers. The proposed strategy is being applied as a required course in the Master in Mechatronics Engineering of the University of Malaga. This paper offers preliminary results from this experience.

Exploiting the huge amount of real time range data provided by new multi-beam three-dimensional (3D) laser scanners is challenging for vehicle and mobile robot applications. The Coarse Binary Cube (CBC) method was proposed to achieve fast and accurate scene registration by maximizing the number of coincident cubes between a pair of scans. The aim of this paper is speeding up CBC with a fast spatial subsampling strategy for raw point clouds that employs the same type of efficient data structures as CBC. Experimental results have been obtained with the Velodyne HDL-32E sensor mounted on the Quadriga mobile robot on irregular terrain. The influence of the subsampling rate has been analyzed. Preliminary results show a relevant gain in computation time without losing matching accuracy.

Mobile robot navigation through natural terrains is a challenging issue with applications such as planetary exploration or search and rescue. This paper proposes navigability assessment of natural terrains scanned from ground-based 3D laser rangefinders. A continuous model of the terrain is obtained as a fuzzy elevation map (FEM). Based on this model, the proposed solution incorporates terrain navigability both in terms of uncertainties of the 3D input data and slope of the fuzzy surface. Moreover, the paper discusses the application of this method for local path planning. For this purpose, the Bug algorithm has been adapted to compute local paths on the navigable region of the FEM. The method has been applied to actual 3D point clouds on two different experimental sites.

Active suspension systems are crucial to improve vehicle performance and passenger comfort. Fuzzy logic control can cope with suspension system nonlinearities using heuristic knowledge rules, but tuning the control parameters is not straightforward. In this paper, we propose tuning fuzzy scaling factors for active suspension control by particle swarm optimization (PSO). PSO performs an off-line stochastic global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize sprung mass acceleration, and is evaluated by simulating the transient response to road perturbations. The paper offers a case study based on the quarter car suspension model. Results offer a good performance of chassis acceleration against bumps and road roughness.

Drivers of vehicles with one or several passive trailers, like truck-and-trailer or articulated luggage carriers, have difficulties in backward maneuvers due to jackknife and lack of visibility. Advanced driver assistance systems (ADAS) can be helpful to improve both safety and driver comfort in these complex operations. In this paper, we propose an ADAS that adopts our curvature limitation method for backward multi-trailer vehicles, where the last trailer is considered a virtual tractor and steering limits are established to avoid jackknife and inter-unit collisions. In the proposed solution, when the driver puts the vehicle in reverse, the steering wheel and pedals can be used as if the vehicle was driven from the back of the last trailer with visual feedback from a camera. This system can be implemented in drive-by-wire vehicles, where the steering-wheel feedback force can be customized for the curvature limitation of a given combination of one or several trailers. The system has been tested to tele-operate a mobile robot with two off-axle trailers.

Autonomous mobile robots have limited energy sources. This work studies power consumption of the locomotion system of wheeled skid-steer vehicles on hard horizontal terrain at walking speeds. This issue is very important for this kind of vehicles due to relevant power losses associated to dynamic friction during turnings. The paper adopts a kinematics approach to provide a simplified power model. This static model estimates motor power consumption as a function of the left- and right-side wheels' speeds. The model is defined through three constant parameters: the x-coordinate of the treads' instantaneous center of rotation on the ground plane, a traction resistance constant, and the ground-wheel friction coefficient. Furthermore, a simple experimental identification procedure is proposed to obtain the model parameters. A power analysis of the four-wheel skid-steer mobile robot Quadriga has been performed with different loads on concrete and marble floorings.

Power consumption is a key element in outdoor mobile robot autonomy. This issue is very relevant in skid-steer tracked vehicles on account of their large ground contact area. In this paper, the power losses due to dynamic friction have been modeled from two different perspectives: 1) the power drawn by the rigid terrain and 2) the power supplied by the motors. Comparison of both approaches has provided new insight on skid steering on hard flat terrains at walking speeds. Experimental power models, which also include traction resistance and other power losses, have been obtained for two different track widths over marble flooring and asphalt with Auriga- beta, which is a full-size mobile robot. To this end, various internal probes have been set at different points of the power stream. Furthermore, new energy implications for navigation of these kinds of vehicles have been deduced and tested.

This work aims at improving real-time motion control and dead-reckoning of wheeled skid-steer vehicles by considering the effects of slippage, but without introducing the complexity of dynamics computations in the loop. This traction scheme is found both in many off-the-shelf mobile robots due to its mechanical simplicity and in outdoor applications due to its maneuverability. In previous works, we reported a method to experimentally obtain an optimized kinematic model for skid-steer tracked vehicles based on the boundedness of the instantaneous centers of rotation (ICRs) of treads on the motion plane. This paper provides further insight on this method, which is now proposed for wheeled skid-steer vehicles. It has been successfully applied to a popular research robotic platform, pioneer P3-AT, with different kinds of tires and terrain types.

This paper proposes a Cartesian control scheme applied to a robotic assistant for laparoscopic surgery. This system's main characteristic is that it emulates the movements of a human assistant, guiding the laparoscopic camera with precision to focus on the area in question inside the patient. Furthermore this control scheme requires adjustment of certain parameters in order to prevent saturation of the manipulator's actuators, and therefore the robot has been studied in terms of manipulability. The proposed movement control scheme has been implanted in the ERM robot used to carry out trials on thirty two patients

This work presents an approach to the evaluation of two interfaces for the control of a robotic surgical assistant. Each interface is associated to a different mode of controlling the system: by the surgeon (voice commands) or by the assistant (tactile interface). This two modes are allowed by the design of the manipulator, developed to occupy a small volume in the operating room. To evaluate the interfaces, a rating scale for teleoperated systems is proposed.

This paper presents an adaptive trajectory planning method concerning to the robotic assistant ERM (endoscopic robotic manipulator), designed and developed by the authors for handling the camera in laparoscopic surgery. In order to emulate the human assistant, camera movements must be defined relative to the fulcrum point, where the optic passes through the patient skin and enters inside the abdominal cavity. Since the robot has a passive wrist, and it is not fixed to the operating table, the relative position between the robot camera holder and the insertion point is unknown. In this way, the proposed approach keeps the camera orientation according to the motion references in spite of this uncertainty, and compensates other unexpected disturbances about the relative robot-patient position. This motion planner is based on a schema of a cartesian motion controller with inner joint position-velocity loop, and has been tested by means of experimentation with alive animals.

The paper proposes a kinematic approach for tracked vehicles in order to improve motion control and pose estimation. Complex dynamics due to slippage and soil shearing make it difficult to predict the exact motion of the vehicle from the velocity of the two tracks. Nevertheless, reliable geometric approximations are necessary to perform onboard real-time computations for autonomous navigation. The presented solution is based on the kinematic similarities between tracked vehicles and wheeled differential drive vehicles. Particularly, the approximate position of wheel contact points for an equivalent vehicle can be optimized for a particular terrain at moderate speeds. This is achieved off-line by feeding a genetic algorithm with raw trajectory data and reliable localization estimations based on external sensors. The method has been successfully tested for online odometric computations and low-level motion control with the Auriga-/spl alpha/ mobile robot. Moreover, the identified parameters are similar to those obtained from the simulated stationary response of a complex dynamic model of this vehicle.

Fuzzy modeling has been widely applied as a powerful methodology for the identification of nonlinear systems from process measurements. In particular, the design of black-box approaches based on fuzzy models has been recognized as an alternative to mathematical methods. This article deals with the application of modeling and identification techniques for obtaining two fuzzy models of a solar domestic hot water system. The models have been generated in order to estimate the energy supplied by a thermal solar system and the output temperature of the water, respectively. The methods have been applied by using only the experimental input/output data taken from the process. © 2002 Wiley Periodicals, Inc.

A stabilization method based on the input-output conicity criterion is presented. Conventional learning algorithms are applied to adjust the controller dynamics, and robust stability of the closed-loop system is guaranteed by modifying the training patterns which yield unstable behavior. The methodology developed expands the class of nonlinear systems to be controlled using neural control schemes, so that the stabilization of a broad class of neural-network-based control systems, even with unknown dynamics, is assured. Straightforwardness in the application of this method is evident in contrast to the Lyapunov function approach.

Presents a robotic assistant for helping surgeons in minimally invasive surgery. This prototype does not require any modification of a standard operation theatre (furniture or surgery tools) for its installation and its putting into operation. Due to its modular design, the robotic assistant is defined as a multi-purpose tool that has been successfully used in laparoscopic surgery, cystoscopy diagnostic and transurethral resection. The system has been tested by using both patient simulators and animals for experimentation purposes.

This paper presents a speed planning and generation algorithm for mobile robots that work under some kind of speed limitations. The proposed method takes the information about these speed constraints and the moving obstacles in the environment, and provides a safe speed profile which allows to build a trajectory that bypasses such obstacles. The method has been successfully tested in the RAM-2 mobile robot.

This paper considers the employment of two low-cost GPS receivers for calculating positions based on a new satellite selection criterion. The parameters that decide the set of satellites used for positioning must take into account the coordinated work of both receivers. Thus, they are very different, or even contradictory to those conventionally used when the GPS receiver is working in single mode or in conjunction with a specialized fixed receiver. The method proposes the calculation of positions in the plane using the three highest satellites. The precision obtained with this technique is better than that provided when the receiver uses the conventional correction data.

The paper deals with speed control for autonomous mobile robots. First a trajectory planner attaches a speed component to the postures of a path by considering speed limitations introduced by the vehicle and by task specifications. In order to provide robustness during task execution in the real world, environment feedback is used to dynamically adjust the speed of the vehicle to the presence of unexpected obstacles, both static and mobile. This is accomplished by combining the planned speed profile with the outputs of two reactive controllers. The system has been successfully implemented within the control architecture of the RAM-2 mobile robot.

The stability of rule-based expert control systems is studied. A control structure with a nonlinear plant and a nonlinear controller, designed on the basis of logical rules incorporating the experience of designers and human operators, is considered. Two classes of indices are presented: one related to the relative stability of the equilibrium point at the origin, and the other to the global stability of the system. These indices can be used for the analysis and design of expert control systems. Some examples are included to illustrate the approach.>