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Mobile robots have a large application potential in everyday life. To build those applications some common and basic behaviors should be initially consolidated, including a people following behavior. In this paper a system able to follow a person based on information provided by a laser scan and a mono and stereo camera is presented. In order to accomplish this goal, a real-time particle filter system able to merge the information provided by the sensors (laser and 2D and 3D images) and calculate the position of the target is proposed, using probabilistic leg patterns, image features and optical flow to this end. The experiments carried out show promising results, allowing a real-time particle filtering based on two different information sources.

In this paper, we present the implementation of an autonomous mobile robot controller developed according to the principle of a multi-layered hybrid architecture. This architecture is composed of four layers: sensori-motor, behavioral, sequencing, and strategic. The paper describes its general structure and the function of its main elements. It further analyses the development of an example task presenting the advantages of the hybrid architecture.

A lot of recent research papers focus on the challenging problem of the combination of genetic algorithms and artificial neural networks. Developmental and molecular biology may be a source of inspiration for designing powerful artificial neurogenesis systems allowing the generation of complex modular structures. This paper describes in details such a neurogenesis model associated with an evolutionary process and its applications to the cart-pole problem and to the control of a mobile robot. Early results demonstrate the surprising efficiency of this methodology and give hints to continue the research towards the generation of more complex adaptive neural networks.

For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

In this work, an explicitly task-oriented approach to the active vision problem is presented. The system tries to reduce the most relevant components of the uncertainty in the world model, for the task the robot is currently performing. It is task oriented in the sense that it explicitly considers a task-specific value function. As test-bed for the presented active vision approach, we selected a robot soccer attention problem: goal-covering by a goalie player. The proposed system is compared with information-based approaches. Experimental results show that it surpasses them in the tested application. We conclude that, when the goal is not the uncertainty reduction itself, the minimization of the belief entropy is not a useful optimality criterion, and that for such cases, task-oriented optimality criteria are better suited.

We show how event extraction can be used for handling delayed response tasks with arbitrary delay periods between the stimulus and the cue for response. We use a simple recurrent network for solving the task. Our approach is based on a number of information processing levels, where the lowest level works on raw time-step based sensory data. This data is classified using an unsupervised clustering mechanism. The second level works on this classified data, but still on the individual time-step basis. An event extraction mechanism detects and signals transitions between classes; this forms the basis for the third level. As this level only is updated when events occur, it is independent of the time-scale of the lower level interaction. We also sketch how an event filtering mechanism could be constructed which discards irrelevant data from the event stream. Such a mechanism would output a fourth level representation which could be used for delayed response tasks where irrelevant, or distracting, events could occur during the delay.

For many application areas such as autonomous navigation, the ability to accurately perceive the environment is essential. For this purpose, a wide variety of well-researched sensor systems are available that can be used to detect obstacles or navigation targets. Stereo cameras have emerged as a very versatile sensing technology in this regard due to their low hardware cost and high fidelity. Consequently, much work has been done to integrate them into mobile robots. However, the existing literature focuses on presenting the concepts and algorithms used to implement the desired robot functions on top of a given camera setup. As a result, the rationale and impact of choosing this camera setup are usually neither discussed nor described. Thus, when designing the stereo camera system for a mobile robot, there is not much general guidance beyond isolated setups that worked for a specific robot. To close the gap, this paper studies the impact of the physical setup of a stereo camera system in indoor environments. To do this, we present the results of an experimental analysis in which we use a given software setup to estimate the distance to an object while systematically changing the camera setup. Thereby, we vary the three main parameters of the physical camera setup, namely the angle and distance between the cameras as well as the field of view and a rather soft parameter, the resolution. Based on the results, we derive several guidelines on how to choose the parameters for an application.

Multirotors are well suited for application tasks such as surveillance and exploration of otherwise inaccessible areas. Standard quadrotors have limitations in their possible configurations due to their underactuation. For this reason, some spatial configurations are not possible, such as hovering while maintaining a nonhorizontal orientation. This paper presents an overactuated quadrotor platform with double axes tilting propellers. The peculiarity of the proposed platform is that, beside the usual control on the four propellers, it allows to tilt each arm where motors are mounted along two independent axis. The resulting number of control inputs is 12, allowing a higher number of stable configurations with respect to traditional quadrotors. As a result, it can assume spatial orientations that are not possible for traditional quadcopters, enabling the possibility to deal with obstacles that would generally impede the motion of normal quadcopters. This feature allows to potentially explore a larger space. This paper presents the design and modeling of the quadrotor. Numerical simulations are carried out to show the effectiveness of the proposed solution.

Self-navigation, referred as the capability of automatically reaching the goal while avoiding collisions with obstacles, is a fundamental skill required for mobile robots. Recently, deep reinforcement learning (DRL) has shown great potential in the development of robot navigation algorithms. However, it is still difficult to train the robot to learn goal-reaching and obstacle-avoidance skills simultaneously. On the other hand, although many DRL-based obstacle-avoidance algorithms are proposed, few of them are reused for more complex navigation tasks. In this paper, a novel danger-aware adaptive composition (DAAC) framework is proposed to combine two individually DRL-trained agents, obstacle-avoidance and goal-reaching, to construct a navigation agent without any redesigning and retraining. The key to this adaptive composition approach is that the value function outputted by the obstacle-avoidance agent serves as an indicator for evaluating the risk level of the current situation, which in turn determines the contribution of these two agents for the next move. Simulation and real-world testing results show that the composed Navigation network can control the robot to accomplish difficult navigation tasks, e.g. reaching a series of successive goals in an unknown and complex environment safely and quickly.

Over the past decade, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this paper systematically reviews and analyzes the use of 3D ToF LiDARs for mobile robotics under harsh conditions such as adverse weather, GPS-denied, and highly dynamic environments for both research and industrial applications. The former include LiDAR data processing in adverse weather, object detection, and autonomous navigation. The latter encompasses autonomous driving, service robotics, and public health crises applications. We hope that our efforts can effectively provide readers with a reference based on our hands-on experiences and promote the deployment of existing mature technologies in real-world systems.

The bio-inspired robotics use functional elements of natures for inspiration. The development of the TB-Horse II prototype is the main target of this work. It is a bio-inspired quadruped robot with biological features in horse of the breed Mangalarga Marchador. In future, the robot can be used to rescue injured people, to carry fragile loads, among others applications. With the study of horse biodynamic, it was possible to propose the TB-Horse II. The gait marcha was implemented and validated using the Virtual Robot Experimentation Platform (V-Rep). Finally, the robot prototype was developed and the experimental validation was realized on a flat ground without obstacles.

This article presents a gait synthesis methodology for a three-legged robot that considers multi-objetives, where the designer must assign a degree of relevance of each performance measurement available (velocity, smoothness of the locomotion, maximum actuator torque and energy consumption) in the gait characteristics. To this end, the position in time of each leg actuator is described by a periodic function that is found by using a reinforcement learning technique called Learning Automata. During the training, the SimMechanics Toolbox of MATLAB/Simulink is used to simulate the robot built using the Bioloid Comprehensive Kit, an educational robot kit manufactured by Robotis. After the training fase, the solution is applied to the real robot and the response is then evaluated and compared with the simulated robot response. In the case studies, two gait synthesis were performed with different desired characteristics. It is shown that proposed solution generates a quite satisfactory gait for the real robot in both cases.

Two important aspects of today's production are energy and time consumption, a typical production process requires parts to be constantly moved between locations. The aim of this research is to minimize the energy and time consumption during the assembly and transport phases by finding the optimal path for the products to be moved. This paper studies trajectory planning and minimization for mobile robots. The trajectory is generated using function approximations and the robot behavior is studied among different trajectories to find the best algorithms to minimize time and energy consumption. The methods discussed in this paper could be repeated automatically several times for better minimization results. The focus of this paper is mobile robotics usage in inspection, industrial and agricultural environments.