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One Step of the Robot (Side View): 

One Step of the Robot (Side View): 

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1 This paper presents an architecture for a test-environment for algorithms and control schemes in the field of collaborative robotics and swarm intelligence. As the foundation of the test-environment, small bionically inspired robots are presented. The robots are small (20 cm x 5 cm x 5 cm) and lightweight (< 200 g). Their design is inspired by th...

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... be done much quicker by a larger group of robots. Thus, the number of deployed robots can often be adapted dynamically depending on the size of the task. In order to develop and test the necessary algorithms for cooperative robots, it is imperative to not only rely on simulations, which are certainly a necessary tool, but also to run tests with real robots under real world conditions. The center of any such test-environment are obviously the employed robots. Their features and abilities determine the capability of the overall system. To be able to test the developed algorithms in a real world scenario, the robots have to be able to handle conditions similar to the real world, i.e., they have to be able to operate on a variety of different surfaces and have to be able to cope with small obstacles. Several of the robotic systems currently employed as the basis for test-environments in the field of cooperative robotics or swarm robotics are wheel based [1–3]. This technology, which is fairly easy to build and operate, has the disadvantage that these robots are typically not able to handle difficult surface conditions or small obstacles resulting in the fact that real life situations cannot be simulated. There are also systems that operate with rubber tracks, which increases their ability to handle difficult surfaces [4]. Still, these systems are only intended to cope with slightly uneven surfaces like rugs, but are unable to handle obstacles. Most of these robots are intended for operation on flat surfaces. A system called SWARM-BOT uses a different approach [5]. This system is intended to cope with uneven surfaces and obstacles by cooperation of different robots. This, of course, generates a significant amount of cooperation overhead just for locomotion. A typical approach to cope with uneven surfaces is to build legged robots. This leads to robots that are able to handle rough terrain and obstacles very well [6, 7]. On the other hand, these robots have such demanding mechanics and are so difficult to control that they are not suited for applications in cooperative robotics, where the individual robots must not surpass a certain level of complexity. In this paper a test-environment for cooperative robots is proposed that is based on robots, which are able to handle rough terrain with small obstacles like those that can be found in a normal household situation. Still these robots have a quite simple and robust design consisting of three cubical segments, which are connected via special joints, where each joint has three independent degrees of translatory freedom. The design is inspired by the motion of caterpillars. The remainder of this paper is structured as follows. The next section describes the objectives for the design of the test-environment. Section 3 describes the design of the robot, on which the proposed test-environment is based. Details on the technical realization of the robot are given in Section 4. The technical realization of the proposed test-environment is described in Section 5. Conclusions and some details on future investigations are given in Section 6. The main objective of this work is to build a test- environment for algorithms and control schemes in the field of collaborative robotics and swarm intelligence. The goal is to build a system that allows to stage test scenarios that are similar to real world situations. Due to this, several requirements have to be met. The employed robots, which form the basis for such a system, have to be able to cope with real world conditions like uneven surfaces or small obstacles, as they might occur in any household situation. To keep the system as a whole manageable and to reduce its production cost, the complexity of the system has to be kept to a minimum. This leads to the desire of keeping the design of the employed robots simple and cheap enough to allow their volume production. Additionally it is important for the system to be able to withstand an everyday testbed situation. The employed robots form the basis of any test- environment for algorithms and control schemes in the field of collaborative robotics and swarm intelligence. To keep the complexity of the test-environment to a minimum and to reduce production costs, multiple identical robots are employed. The proposed robot consists of three segments (figure 2). These segments are connected by a special set of joints that each have three independent translatory degrees of freedom. Therefore, the robot has a total of six independent degrees of freedom. The head and the tail element are identical, so that if the robot changes direction, the tail becomes the head and vice versa. The design of the robot permits a wide variety of motion patterns. Some basic ones are described below. The most efficient motion can be generated by a superposition of the basic motion patterns. The most basic motion pattern is walking. In this mode the robot moves its segments one by one in the desired direction (figure 5). This way the robot can move in a straight line either forward or backward. During this motion typically no slipping occurs. In order to change direction, the robot can rotate. During this motion one of the end segments (head or tail) is moved in the desired direction of rotation (figure 6 a-d). For an even faster rotation, it is also possible to move the other end segment in the opposite direction. The middle segment is raised in order to decrease friction and then the end segments that have been moved to the side are realigned with the middle segment (figure 6 f). This leads to a rotation of the robot with the end segments as anchor points (indicated by the crosses in figure 6). Besides these most basic motion patterns other step sequences, e.g. for a sideward motion or a faster running motion (head and tail are moved simultaneously), can be realized with the proposed robot design. One major advantage of this design, next to the ability to cope with uneven ground and small obstacles, is the fact that very little slip occurs during the movement. This is the case as in most situations two segments of the robot are motionless on the ground. Therefore, this design is well suited to acquire odometric data. In order to build a robot as described above, several design challenges have to be met. While maintaining extremely low weight, the whole construction still has to be strong enough to withstand an everyday testbed situation. Additionally the design has to be simple and cheap enough to allow volume production. Next to the desired small size, this leads to the need of very precise and automated manufacturing processes. A special problem is the design of the joints. These joints have to be built in such a fashion that all three independent translatory degrees of freedom can be realized. The head and the tail of the robot have the exact same design. The centre segment is slightly longer. Each segment has a width and height of 50 mm. The head and tail have a length of 50 mm as well. The centre segment has a length of 70 mm. With the additional size of the joints, this results in a contracted length of 200 mm. If the joints are extended, the total robot has a length of 240 mm. The head can be moved up to 20 mm to the front ( x 1 in figure 2), up to 17 mm to each side ( y 1 ) and it can be raised or lowered up to 17 mm ( z 1 ). The movement range of the tail is identical due to the symmetrical setup. The robot has an overall weight (including batteries, etc.) of less than 200 g. The mechanical inner structure (figure 7) of the robot is built from an epoxy resin glass laminated fabric with a thickness of 1.5 mm that is copper plated at one side. This material is also used to build printed circuit boards. Besides the high strength and the low weight of this material, it can be cut and milled very precisely with a CNC circuit board plotter. In our case we use an LPKF C60 circuit board plotter with a step resolution of 1 μ m and a repeatability of ±1 μ m [8]. Due to the fact that a computer controlled tool is used, the mechanical components can be produced automatically with very high precision. The use of the copper plated material has the added advantage that the electrical components can be built directly into the mechanical structure of the robot. Therefore, a high level of system integration can be achieved. The robot is actuated by 8 piezoelectric motors from Elliptec Resonator AG [10]. The piezoelectric motors have been chosen for their low weight (1.2 g) and their ability to generate direct linear motion without the need of a gear. Additionally their short response time (100 μs) and their relatively high speed (up to 300 mm/s) is advantageous. For the lifting of an element ( z 1 and z 2 in figure 2), two motors are used in parallel. The other degrees of freedom are actuated by a single motor. The employed motors have a very simple design that just consists of three parts (an aluminum frame, a piezoelectric stack and a spring). They are driven with a pulsed voltage of ≤ 2.5 V at two different frequencies (typically 79 kHz and 97 kHz) for forward and backward motion, respectively. The applied pulsed voltage causes the tip of the motor (figure 8 a) to vibrate in an elliptic fashion. Depending on the frequency used, the elliptic rotation of the tip of the motor is clock- or counterclockwise. This motion can be directly applied to a carbon fiber reinforced plastic rod (figure 8 b) that is moved forward or backward accordingly. This rod is fixed to the linear sliding carriage (figure 8 c) that is to be driven. This carriage is bedded on four ball bearings (figure 8 d) on a base plate. This is done in order to reduce friction, which is important because the functioning of the motors relies on friction as well. The ball bearings are realized with four pairs of milled groves on the sliding carriage and the base plate that each takes one steel ball with a diameter of 2 mm (figure 9). All six translatory ...

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... The presented test-environment is based on a group of miniature walking robots. These robots are inspired by the robots presented in [9]. Even if the basic concept is similar to some degree, the overall design has significantly changed. ...
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For the collaborative control of a team of robots, a set of well-suited high-level control algorithms, especially for path planning and measurement scheduling, is essential. The quality of these control algorithms can be significantly increased by considering uncertainties that arise, e.g. from noisy measurements or system model abstraction, by incorporating stochastic filters into the control. To develop these kinds of algorithms and to prove their effectiveness, obviously real- world experiments with real world uncertainties are mandatory. Therefore, a test-environment for evaluating algorithms for collaborative control of a team of robots is presented. This test-environment is founded on miniature walking robots with six degrees of freedom. Their novel locomotion concept not only allows them to move in a wide variety of different motion patterns far beyond the possibilities of traditionally employed wheel-based robots, but also to handle real-world conditions like uneven ground or small obstacles. These robots are embedded in a modular test-environment, comprising infrastructure and simulation modules as well as a high-level control module with submodules for pose estimation, path planning, and measurement scheduling. The interaction of the individual modules of the introduced test-environment is illustrated by an experiment from the field of cooperative localization with focus on measurement scheduling, where the robots that perform distance measurements are selected based on a novel criterion, the normalized mutual Mahalanobis distance.
... Recursive processing of random variables requires the efficient application of the so-called prediction step, i.e., a recursive transformation of a random variable over time. Fig. 1 illustrates an example application from the field of sensor networks, where the prediction step is of great importance: The localization of a robot (for the robot description see [21]) via a sensor network requires continuous position propagations. Inaccuracies in the robot's kinematic model and the imprecise odometry leads to imprecisely known positions that have to be estimated by means ...
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... Recursive processing of random variables requires the efficient application of the so-called prediction step, i.e., a recursive transformation of a random variable over time. Fig. 1 illustrates an example application from the field of sensor networks, where the prediction step is of great importance: The localization of a robot (for the robot description see [21]) via a sensor network requires continuous position propagations. Inaccuracies in the robot's kinematic model and the imprecise odometry leads to imprecisely known positions that have to be estimated by means of a predictor. ...
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For several tasks in sensor networks, such as localization, information fusion,or sensor scheduling, Bayesian estimation is of paramount importance. Due to the limited computational and memory resources of the nodes in a sensor network, evaluation of the prediction step of the Bayesian estimator has to be performed very effciently. An exact and closed-form representation of the predicted probability density function of the system state is typically impossible to obtain, since exactly solving the prediction step for non-linear discrete-time dynamic systems in closed form is unfeasible. Assuming additive noise, we propose an accurate approximation of the predicted density, that can be calculated effciently by optimally approximating the transition density using a hybrid density. A hybrid density consists of two different density types: Dirac delta functions that cover the domain of the current density of the system state, and another density type, e.g. Gaussian densities, that cover the domain of the predicted density. The freely selectable, second density type of the hybrid density depends strongly on the noise affecting the nonlinear system. So, the proposed approximation framework for nonlinear prediction is not restricted to a specific noise density. It further allows an analytical evaluation of the Chapman-Kolmogorov prediction equation and can be interpreted as a deterministic sampling estimation approach. In contrast to methods using random sampling like particle filters, a dramatic reduction in the number of components and a subsequent decrease in computation time for approximating the predicted density is gained.