This paper describes the development of a tilting locomotion system based on a compliant tensegrity structure with multiple stable equilibrium configurations. A tensegrity structure featuring 4 stable equilibrium states is considered. The mechanical model of the structure is presented and the according equations of motion are derived. The variation of the length of selected structural members allows to influence the prestress state and the corresponding shape of the tensegrity structure. Based on bifurcation analyses a reliable actuation strategy to control the current equilibrium state is designed. In this work, the tensegrity structure is assumed to be in contact with a horizontal plane due to gravity. The derived actuation strategy is utilized to generate tilting locomotion by successively changing the equilibrium state. Numerical simulations are evaluated considering the locomotion characteristics. In order to validate this theoretical approach a prototype is developed. Experiments regarding to the equilibrium configurations, the actuation strategy and the locomotion characteristics are evaluated using image processing tools and motion capturing. The results verify the theoretical data and confirm the working principle of the investigated tilting locomotion system. This approach represents a feasible actuation strategy to realize a reliable tilting locomotion utilizing the multistability of compliant tensegrity structures.

This paper describes a new concept for locomotion of mobile robots based on single actuated tensegrity structures. To discuss the working principle, two vibration-driven locomotion systems are considered. Due to the complex dynamics of the applied tensegrity structures with pronounced mechanical compliance, the movement performance of both systems is highly dependent on the driving frequency. By using single-actuation, the system design and also their control can be kept simple. The movement of the robots is depending on their configuration uniaxial bidirectional or planar. The working principle of both systems is discussed with the help of transient dynamic analyses and verified with experimental tests for a selected prototype.

3D Time-of-Flight cameras are able to deliver robust depth maps of dynamic scenes. The frame rate, however, is limited because today's systems utilizing two-tap sensors need to acquire the required raw images in multiple instances in order to compute one depth map. These multiple raw images allow canceling out systematic errors introduced by asymmetries in the two taps, which otherwise would distort the reconstructed depth map. This work presents a method to implicitly calibrate these asymmetries of multi-tap 3D Time-of-Flight sensors. The calibration data are gathered from arbitrary live acquisitions possibly in real-time. The proposed correction of raw data supersedes the commonly used averaging technique. Thus it is possible to compute multiple depth maps from a single set of raw images. This increases the frame rate by at least a factor of two. The method is verified using real camera data and is evaluated quantitatively.

In this paper we present a method to calibrate the extrinsic parameters of a monocular camera on a moving vehicle. The method is based on a homography between two camera shots. Therefore, only the road surface has to be visible in the pair of images. A reasonable definition of the vehicle coordinate system in combination with the use of epipolar geometry reduces the complexity to parameterize the underlying homography matrix. The extrinsic parameters are determined analytically by two correctly matched feature points located on the road surface. The final parameter set is determined by a recursive filter which considers various estimates over time. Results with a real-world video sequence indicate that the method is comparable to classical offline calibration techniques using objects of known geometry.

In this paper we present a method to compensate the image motion of a monocular camera on a moving vehicle in order to detect obstacles. Due to the camera motion, the road surface induces a characteristic image motion between two camera shots. The motion of the camera is determined by the use of odometric data received from the CAN-bus, and the position and orientation of the road is continuously estimated with camera self-calibration. This all leads to a motion field which is predicted based on homography. To prevent the drawbacks of the real camera perspective, different virtual camera perspectives are presented in combination with motion compensation. Possible virtual perspectives are the bird's eye view and image rectification. In addition, a non-linear camera model is used which does not limit the range of obstacle detection to a certain distance and efficiently uses the available image information.