Relative Orientation Measurements Research Articles

Bearing‐based formation control of second‐order multiagent systems with bounded disturbances

AbstractThis article investigates the bearing‐based formation control problem for second‐order multiagent systems (SMS) in the presence of the bounded disturbances in their models. The main contributions of this article are listed as follows: (1) We extend the bearing formation control to SMS. (2) We propose two novel robust distributed bearing formation control laws. In the first control law, the bearing measurement in the global inertial frame is required. This control law guarantees that the inter‐agent bearings converge to the desired bearings. The second control law requires obtaining the local bearing measurements and relative orientation measurements. This control law guarantees that the inter‐agent bearings converge to the desired bearings and the orientation of each agent converges to a common orientation. Some simulations are conducted, and simulation results verify the effectiveness of the proposed control laws.

A ROS-based distributed multi-robot localization and orientation strategy for heterogeneous robots

The problem of estimating and tracking the location and orientation of a mobile robot by another in heterogeneous distributed multi-robots is studied in this paper. We propose a distributed multi-robot localization strategy (DMLS) that is Robotic Operating System (ROS) based. It consists of an algorithm that fuses data of diverse sensors from 2 heterogeneous robots that are not connected within their transform trees to localize and measure the relative position and orientation.The method exploits the robust detection of the Convolutional Neural Networks (CNN) and the accurate relative position measurements from the local costmap. The algorithm is composed of two parts: The localization part and the relative orientation measurement part. Localization is done by optimization and alignment calibration of the CNN output with the costmap in an individual robot. The relative orientation measurement is done by a collaborative multi-robot fusing of diverse sensor data to align and synchronize the transform frames of both robots in their costmaps. To illustrate the performance of this strategy, the proposed method is compared with a conventional object localization and orientation measuring method that uses computer vision and QR codes. The results show that this proposed method is robust and accurate while maintaining a degree of simplicity and efficiency in costs. The paper also presents various application experiments in laboratory and simulation environments. By using the proposed method, distributed multi-robots collaborate to achieve collective intelligence from individuals, which increases team performance.

Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks

The trajectory planning and control of multi-agent systems requires accurate localization, which may not be possible when GPS signals and fixed features required for SLAM are not available. Cooperative Localization (CL) in multi-agent systems offers a short-term solution that may significantly improve vehicle pose estimation. CL algorithms have been mainly developed and assessed for planar mobile robot networks due to complexities and singularities in three-dimensional (3D) motion. In this paper, we develop the required singularity-free equations and apply and assess an EKF-based CL for 3D vehicle networks. We assess the performance of CL with respect to the number of simultaneous and redundant measurements. We further assess CL performance with only relative position measurements available. Finally, experiments are performed to validate the proposed algorithms. We further investigate the effect of absolute position measurements in CL. Conclusions: Cooperative localization is an effective method when applied to 3D vehicle networks. However, CL does not improve localization with only relative position measurements, and thus previously reported results for 2D vehicle models were only effective due to relative orientation measurements. Absolute measurement reduces the overall localization errors much more significantly when there has been CL with prior relative position measurements.

Mice Can Learn a Stimulus-Invariant Orientation Discrimination Task

Understanding how the brain computes choice from sensory information is a central question of perceptual decision-making. Relevant behavioral tasks condition choice on abstract or invariant properties of the stimuli, thus decoupling stimulus-specific information from the decision variable. Among visual tasks, orientation discrimination is a gold standard; however, it is not clear if a mouse – a recently popular animal model in visual decision-making research – can learn an invariant orientation discrimination task and what choice strategies it would use. Here we show that mice can solve a discrimination task where choices are decoupled from the orientation of individual stimuli, depending instead on a measure of relative orientation. Mice learned this task, reaching an upper bound for discrimination acuity of 6 degrees and relying on decision-making strategies that balanced cognitive resources with history-dependent biases. We analyzed behavioral data from n=40 animals with the help of a novel probabilistic choice model that we used to interpret individual biases and behavioral strategies. The model explained variation in performance with task difficulty and identified unreported dimensions of variation associated with the circularity of the stimulus space. Furthermore, it showed a larger effect of history biases on animals’ choices during periods of lower engagement. Our results demonstrate that mice can learn invariant perceptual representations by combining decision-relevant stimulus information decoupled from low-level visual features, with the computation of the decision variable dependent on the cognitive state.

Measurement Accuracy of an Upper Limb Tracking System Based on Two Hillcrest Labs BNO080 IMU Sensors: An Environmental Assessment

This paper presents an analysis on the accuracy of a two-IMU (Inertial Measurement Units) device. The device consists in two identical wired BNO080 sensors connected to a Central Control Unit, with the aim of developing a remote tracking tool for upper limb movements. The control unit collects the data from the sensors, ensuring the time synchronization, and retransmits the information to an external device. From the quaternions representing the orientation of the two sensors, the relative orientation can be calculated. A series of tests has been carried on using a robotic arm, allowing to perform repeated movements. Using this setup, the repeatability of the measurement, the accuracy and the measurement drift were then evaluated. The parameters were evaluated in conditions of variation of speed, position and acceleration compatible with those of a person. In a second stage, the sensors have been inserted into a wearable garment and used by a human in a real scenario, allowing the evaluation of possible sources of disturbance due to the environment, e.g. magnetic fields generated by electronic devices. The results show a good reliability of the relative orientation measurement, with an uncertainty generally smaller than 6 degrees per eulerian angle.

Pose localization of leader–follower networks with direction measurements

A distributed pose localization framework based on direction measurements is proposed for a type of leader–follower multi-agent system in R3. The novelty of the proposed localization method lies in the elimination of the need for using distance measurements and relative orientation measurements for the network pose localization problem. In particular, a network localization scheme is developed based directly on the measured directions between an agent and its neighboring agents in the network. The proposed position and orientation localization algorithms are implemented through differential equations which simultaneously compute poses of all followers by using locally measured directional vectors and angular velocities, and actual pose knowledge of some leader agents, allowing some tracking of time-varying orientations. Further, we establish almost global asymptotic convergence of the estimated positions and orientations of the agents to the actual poses in the stationary case.

A fast and accurate approximation for planar pose graph optimization

This work investigates the pose graph optimization problem, which arises in maximum likelihood approaches to simultaneous localization and mapping (SLAM). State-of-the-art approaches have been demonstrated to be very efficient in medium- and large-sized scenarios; however, their convergence to the maximum likelihood estimate heavily relies on the quality of the initial guess. We show that, in planar scenarios, pose graph optimization has a very peculiar structure. The problem of estimating robot orientations from relative orientation measurements is a quadratic optimization problem (after computing suitable regularization terms); moreover, given robot orientations, the overall optimization problem becomes quadratic. We exploit these observations to design an approximation of the maximum likelihood estimate, which does not require the availability of an initial guess. The approximation, named LAGO (Linear Approximation for pose Graph Optimization), can be used as a stand-alone tool or can bootstrap state-of-the-art techniques, reducing the risk of being trapped in local minima. We provide analytical results on existence and sub-optimality of LAGO, and we discuss the factors influencing its quality. Experimental results demonstrate that LAGO is accurate in common SLAM problems. Moreover, it is remarkably faster than state-of-the-art techniques, and is able to solve very large-scale problems in a few seconds.

A modified resolved acceleration controller for position-based visual servoing

This paper presents modifications to the resolved acceleration controller design to develop a Cartesian controller suitable for use with position-based visual servoing. The development of any Cartesian control for relative robot end-point position control with respect to an object, requires the solution to the problem of relative orientation measurement and control. This problem is due to the nonlinear nature of orientation and the complex transformations used for its representation. The solution proposed in this paper, is to use the orientation representation of roll-pitch-yaw angles in a vector form, and calculate the difference between the current vector and the desired vector to represent the orientation error. The Jacobian matrices which relate the roll-pitch-yaw rates to the joint velocities are developed. The resulting relationships are then used in the development of a modified resolved acceleration controller that is applicable to position-based visual servo control.