Path planning and control are two main issues in mobile robotics. In this field, the use of video cameras plays an essential role in newer systems. This article introduces a new method for path control by means of a ceiling-mounted camera which observes the robot’s work space. The control method is based on capturing an image at defined time intervals and evaluating it to find out whether the mobile robot meets the planned route or not. If the robot does not meet the planned route, a correction path in shape of a spline curve will be executed to return the robot back to the given route. The properties of the spline curve, which vary depending on the robot dynamics and the degree of deviation from the given route, is determined by a fuzzy system.
In this paper, the possible advantages of using grounded running, a running gait without aerial phases, for fast robot locomotion are discussed. Extended fields of fixed points for grounded running, obtained using dimensionless numerical modeling, are presented. They can be used to find global parameters for the design and operation of robots. The use of self-stable (for steady-state locomotion) or weak unstable (to improve maneuverability-stability trade-off) gait-modes as a design parameter could represent a strategy to reduce the still existing gap between animals and mimicking machines with respect to elegance and performance.
Safe navigation a great challenge for wall-climbing robots which adhere to the surface via negative pressure. Especially wheeled systems which are able to drive on vertical concrete structures like bridge pylons or dams need special measures to enhance safety. This paper presents the advanced motion control system of the climbing robot cromsci which uses a negative pressure adhesion system in combination with driven wheels for propulsion. The main demands to this motion control system related to robot safety are to enhance the transferable force in driving direction, reduce the wear of wheels and to minimize the chance of robot slip. This can be achieved via special traction control components and additional elements as presented in this paper. Experimental results prove the operability of the described measures.
When mobile robots operate in home environments, a robot should consider the inhabitants while moving around. In this work, an approach is presented, which at the one hand predicts the movements of a person in a very simple way, and on the other hand uses the predicted movement to plan a motion path of the robot. We deploy a potential field approach to predict the person’s movement trajectory and use an modified Fast Marching planner to access a time-variable cost function for the planning process. The goal of our development is an early avoiding behavior of the robot, when the robot passes a person. This should increase the acceptance of the robot, and signal a “busy”-behavior. We show the feasibility of the presented approach in some first simulation results.
This paper presents a mapping approach for inland waters using a noisy radar sensor installed on a boat. The vessel’s position is acquired from GPS, thus this is a pure mapping problem. For the actual mapping the probabilistic open-source mapping framework octomap as presented by [8] is used. Exactly one polygon is extracted from a binary radar image, the so-called Water Enclosing Polygon. This discards inland echos and multi-path measurements. Additionally, an approach to detect bridges and dolphins is presented. The runtime of the mapping algorithm is less then 2.5 s. Thus, each new radar scan is integrated into the octomap.
Service robots operate in unstructured environments where precise and reliable calibration of vision and manipulation components is essential for successful and safe operation. So far the calibration of the service robot Care-O-bot® 3 covering only the camera sensors is not precise enough for manipulation of small objects and involves many manual error-prone steps. In an evolving research context there is constant need for recalibration and therefore a complete and precise yet quick and easy to use calibration procedure is desired. We propose an automated calibration process covering the cameras and kinematic components of a service robot and present an open-source implementation for Care-O-bot® 3.
In the field of humanoid robotics, developing a walking system capable of adapting to new situations is driven by the prospect of employing humanoid robots in unknown environments. By evaluating sensor data, the control system may acquire information about the environment and utilize that information for adaptation. In this paper, we focus on the reaction of a control system when encountering an earlier than expected ground contact of the swing leg. A method of planning a \(C_2\)-continuous foot trajectory is presented. The boundary conditions of that trajectory are defined by position, velocity and acceleration of the originally planned trajectory at the time of replanning and a prescribed position that is reached after an arbitrarily chosen time interval with zero velocity and acceleration. We require this trajectory to be \(C_2\)-continuous and free from overshoot. The function we propose contains a parameter utilized for optimization and to prevent overshoot. We show that the global optimum of our acceleration-based objective function can be obtained analytically.
RoboEarth aims at providing a distributed cloud-based web platform from robots for robots that is publicly accessible and enables robots to autonomously share knowledge among each other and to generate new knowledge from previously stored data. As a result robots don’t have to gain the same knowledge over and over again, but can build upon it right from the start. Currently, shareable data are abstract task descriptions, object models and environment maps. In this paper we describe RoboEarth’s approach to automatically and transparently generate 2D maps for localization and navigation, which are extracted from shared 3D maps and suited for a specific robot configuration. The parameters of the map generation process get inferred from a robot’s semantic self-description. Using RoboEarth for knowledge generation enables simple platforms with low computational power to execute complex tasks in complex environments. Furthermore the approach effectively simplifies the time consuming process of generating new maps every time a new robot platform with different specifications is used.
Reacting to the rapidly changing application requirements during a robot mission requires an adaptation capability of the real-time system. In this paper we focus on real-time image processing to support navigation, collision avoidance and object recognition. Due to the data dependent run-times of image processing algorithms, classic real-time approaches are not feasible. To exemplary solve this problem the SURF algorithm was adapted to the AnyTime concept and used together with a TaskPair scheduler. To make a real-time system capable of self-adaptation useful parameters need to be found. Based on our research we suggest strategies on how to change parameters appropriately.
Terrain classification is a fundamental task in outdoor robot navigation to detect and avoid impassable terrain. Camera-based approaches are well-studied and provide good results. A drawback of these approaches, however, is that the quality of the classification varies with the prevailing lighting conditions. 3D laser scanners, on the other hand, are largely illumination-invariant. In this work we present easy to compute features for 3D point clouds using range and intensity values. We compare the classification results obtained using only the laser-based features with the results of camera-based classification and study the influence of different lighting conditions.
This work aims to give an overview about the current research concerning mobile robots in smart environments. It is part of the motivation of a paradigm that aims to shift complexity away from mobile machines into the (smart) environment without sacrificing the safety of the overall system. Several results of current and ongoing research will be presented, including a powerful simulation environment that allows to simulate the overall system including ambient sensors, communication within a wireless sensor network (WSN) and mobile robots. Also some experiments about the localization of mobile robots in smart environments and results will be presented.
We present a quad-rotor micro aerial vehicle (MAV) that is capable to fly and navigate autonomously in an unknown environment. The only sensory input used by the MAV are the imagery from two cameras in a stereo configuration, and data from an inertial measurement unit. We apply a fast sparse stereo matching algorithm in combination with a visual odometry method based on PTAM to estimate the current MAV pose, which we require for autonomous control. All processing is performed on a single board computer on-board the MAV. To our knowledge, this is the first MAV that uses stereo vision for navigation, and does not rely on visual markers or off-board processing. In a flight experiment, the MAV was capable to hover autonomously, and it was able to estimate its current position at a rate of 29 Hz and with an average error of only 2.8 cm.
Cooperative robots are more efficient than a robot when the assigned task is complex and the workspace is large. To meet with the task efficiency and successful control of Cooperative robots, sensing and networking of the robot are necessities. To ensure stable communication, the cooperative routing robot system is implemented. Our system is an autonomous reactive router that responds to change in strength of a signal and a quality of link among cooperative robots. The routing robot is driven toward a safety region of network using virtual force model or change into a router. A result of our approach was shown as an experiment. A tank robot was continuously teleoperated from ROC (remote operate center); thereby condition of network was proven to be stable. In addition, the ROC continuously acquired a remote video and positions. As a result, the cooperative routing robot system help you teleoperate a robot about long distance.
HANSE is a low-cost Autonomous Underwater Vehicle (AUV) capable of solving many common underwater challenges. In this paper we will present HANSE’s modular and expandable hardware and software design, the underwater simulator MARS, as well as robust and efficient sonar-based localization and vision-based object detection algorithms, with which we have successfully participated in the Student Autonomous Underwater Vehicle Challenge in Europe (SAUC-E) 2011.
We introduce a novel approach for real-time decision making with limited memory bounds based on an adaption of the well-known RETE algorithm together with static priorities for conflict resolution. This guarantees fixed execution times independent from the number of rules and facts. Experiments on an unmanned aerial system confirm the adequacy in applications with real-time requirements. The proposed decision system is especially interesting to be used in time-triggered embedded software architectures and for establishing a technical base for building certifiable autonomous unmanned systems.
This paper presents a framework for motion planning of autonomous vehicles, it is characterized by its efficient computation and its safety guarantees. An optimal control based approach generates comfortable and physically feasible maneuvers of the vehicle. Therefore, a combined optimization of the lateral and longitudinal movements in street-relative coordinates with carefully chosen cost functionals and terminal state sets is performed. The collision checking of the trajectories during the planning horizon is also performed in street-relative coordinates. It provides continuous collision checking, which covers nearly all situations based on an algebraic solution and has a constant response time. Finally, the problem of safety assessment for partial trajectories beyond the planning horizon is addressed. Therefore, the Inevitable Collision States (ICS) are used, extending the safety assessment to an infinite time horizon. To solve the ICS computation nonlinear programming is applied. An example implementation of the proposed framework is applied to simulation scenarios that demonstrates its efficiency and safety capabilities.
This paper presents an overview of the hard- and software architecture of a mobile visitor information system for the Konrad Zuse building of the School of Computer Science and Automation at the Ilmenau University of Technology. Two mobile robots serve as mobile information terminals with capabilities for generating way descriptions and guiding the visitor to the points of interest (labs, meeting rooms, offices, employees) in the building. The paper focuses on the constraints resulting from the challenging environment in this multi-floor building, as well as on the integration aspects of various skills for navigation and human–robot interaction. Besides first experience with the system, the further development is outlined as well.
Decision making is an essential part of Autonomous Mobile Systems. Research shows that emotion is an important factor in human decision making. Therefore an increasing number of approaches using modelled emotions for decision making are developed for artificial intelligent systems. Often those approaches are only evaluated in simulated environments in which dummies are used to represent actions. However, the realisation of a real robot application also requires the handling of problems which may not occur in a simulated environment, such as long execution times. Furthermore, the adaption of existing approaches to variant applications often includes several time-consuming adjustments to the system. In this paper the implementation of an emotional decision making algorithm for the Nao robot is presented. The implementation design is based on the human brain structure and models different brain parts which are included in the decision making process. Beside the fact that the chosen structure is closer to the human model, the modular architecture allows an easy implementation of enhancements or different approaches. A key point is the easy adaption of the approach to different applications, suitable even for users without technical expertise or programming skills. As an example, a possible real life scenario is used, in which the robot is embedded in a social environment.
In this paper we describe an approach for validating and adapting a reference trajectory of an autonomous vehicle. It is assumed that the generation of the reference trajectory guarantees the approximation to be collision free if it stays within a tolerance area. We employ a precomputed set of motion primitives as used in state-of-the-art path planning libraries. The generated path is smooth and feasible. Two applications are presented, which have motivated the development of the approach.
A gas fuel-actuated hopping mechanism with a magnetic latching mechanism and a double-piston structure is designed and implemented. The magnetic force between two cylindrical permanent magnets is analyzed by Finite element method and validated by experiment. Both results show magnetic force decays rapidly with the increase in air gap, which is desirable for latching mechanism. The inner space of hollow piston rod is used as combustion chamber, in which a small piston is inserted to expel exhaust gas as much as possible. This kind of double-piston structure can take advantage of the inner space of the mechanism to a great extent. Experiments show the hopping mechanism has an obstacle-overcoming ability of the hopping height of 1.5 m and the hopping distance of 1.5 m at a relatively low fuel pressure, which validates the feasibility of the design of the hopping mechanism.
