During quadrupedal robot locomotion, there is pitch, yaw, and roll of the head and body due to the stepping. The head motion adversely affects visual sensors embedded in the robot's head. Mammals stabilize the head using a vestibulocollic reflex that detects linear and rotational acceleration. In this paper we describe the use of a machine learning algorithm that utilizes signals from an artificial vestibular system that has been embedded in the robot's head. Our approach can rapidly learn to compensate for the head movements that appear when no stabilization mechanism is present. The stabilization using a Sony Aibo robot occurs in only a few gait cycles.

This paper presents a novel technique to register a set of two 3D laser scans obtained from a ground robot and a wall-climbing robot which operates on the ceiling to construct a complete map of the indoor environment. Traditional laser scan registration methods like the Iterative Closest Point (ICP) algorithm will not converge to a global minimum without a good initial estimate of the transformation matrix. Our technique uses an overhead camera on the wall-climbing robot to keep line of sight with the ground robot and solves the Perspective Three Point (P3P) Problem to obtain the transformation matrix between the wall-climbing robot and the ground robot, which serves as a good initial estimate for the ICP algorithm to further refine the transformation matrix. We propose a novel particle filter algorithm to identify the real pose of the wall-climbing robot out of up to four possible solutions to P3P problem using Grunert's algorithm. The initial estimate ensures convergence of the ICP algorithm to a global minimum at all times. The simulation and experimental results indicate that the resulting composite laser map is accurate. In addition, the vision-based approach increases the efficiency by reducing the number of iterations of the ICP algorithm.

This paper presents a novel method to construct a complete 3D map that includes all surfaces (ceiling, wall, and furniture tops, etc.) in indoor environments. A team of four robots, including three ground robots and one wall-climbing robot is deployed in a tetrahedron configuration that satisfies the perspective three point (P3P) problem. P3P problem is to estimate the pose of a perspective camera on the wall-climbing robot viewing three ground robots, which will produce up to four solutions using Grunert's algorithm while only one of them is genuine. We propose a probabilistic Bayesian algorithm that identifies the unique solution of the P3P problem using the mobility of the camera. Based on this technique, we introduce an intra-robot localization method to determine the geometric relationship among four robots. Each ground robot is equipped with a rotary laser range finder (LRF), a pan-tilt-zoom camera, and a LED cluster. The wall-climbing robot is fitted with a LRF, a perspective camera, and a motion sensor. Through the vision sensors, the robots obtain their relative poses by solving the P3P problem. Through the LRF on each robot, 4 laser point cloud maps are produced from each robot's point of view. With the information of relative poses of the multiple robots and the calibration data of each LRF and camera pair, the 4 partial maps are fused to acquire a complete 3D map that is rich with information of all surfaces. Our approach outperforms the traditional range image fusion algorithms in terms of time complexity and is suitable for real-time implementation. Real experiments verified the effectiveness of the method.

The angular vestibulo-ocular and opto-kinetic reflexes (aVOR and OKR) combine to provide compensation for head rotations in space to help maintain a steady image on the retina. We previously implemented an artificial angular vestibulo-ocular reflex with a fully articulated binocular control system in a quadruped robot head. In this paper, we describe the implementation of artificial opto-kinetic and linear vestibular ocular reflexes (OKR and lVOR) that use inputs from an artificial vestibular sensor and a binocular camera system to compensate for linear movements of the head and visual motion to stabilize images on the cameras. The object tracking algorithm was able to fixate a steady object in the camera's field of view during linear perturbations of the robot's head in space at low frequencies of movement (0.2-0.6 Hz), simulating the linear VOR. We implemented an algorithm that combines the linear VOR and OKR model and computes changes in relative pose of the cameras with respect to the object being tracked. The system provides compensatory angular movements of the Ocular Servo Module (OSM) to stabilize images as the robot is moved laterally.

Studies of primate locomotion have shown that the head and eyes are stabilized in space through the vestibulo-collic and vestibulo ocular reflexes (VCR, VOR). The VOR is a reflex eye movement control system that stabilizes the image on the retina of the eye during head movements in space. This stabilization helps maintain objects of interest approximately fixed on the retina during locomotion. In this paper we present the design and implementation of an artificial vestibular system, which drives a fully articulated binocular vision system for quadrupedal robots to maintain accurate gaze. The complete robot head has 9 degrees of freedom (DOF): pitch, yaw, and roll for the head and 3 DOF for left and right cameras. The SONY AIBOreg quadruped robot has been modified with additional hardware to emulate the vestibular system and the vestibulo-ocular reflex in primates.