This paper deals with the design and development of a new perception system for controlling a set of ultrasonic transducers working at two different frequencies generated by a digital signal processor (DSP). Data gathering speed is increased by the simultaneous firing of sonars without crosstalk problems. The software and hardware implementation are presented as well as the results from the calibration process and the integration on board the mobile robot RAM-2. Finally, a demonstration of the use of the proposed equipment to wall-following via a fuzzy controller is described.

Real-time responses for multiple robots motion planning demand heuristic algorithms. This paper presents a method to evaluate the efficiency of these algorithms in order to compute coordinated trajectories for multiple car-like robots on a segment of a highway. The idea is to compare the results of these algorithms with the optimal result obtained by a proposed mixed-integer linear programming (MILP) optimization model. The MILP model considers the main elements of a traffic system, such as topography of lanes, traffic rules and individual capacity of acceleration. Moreover, new indexes for microscopic traffic assessment are proposed a well. Several tests have been carried out to validate both the MILP model and the algorithm used.

ADVANCES IN BlOROBOTIC VlSlON

The Australian National University Biorobotic Vision Group has described its innovative researches and developments in the field of robotic vision. The group has been researching the principles by which insects see, control flights and navigate, in order to transfer their findings to their work in robotic vision. They believe this work will help them in their aim to devise algorithms for machine vision and autonomous, visually guided robots. In particular, this work is in reference to using optic flow to measure image motion.

The researchers have already successfully developed a hover controller that can hover over an arbitary set of landmarks with no manual intervention.They have also designed an autonomous helicopter that will hover and fly without using GPS.

In scenarios such as environmental data collection and traffic monitoring, timely responses to real-time situations are facilitated by persistently accessing nodes with revisiting constraints using unmanned aerial vehicles (UAVs). However, imbalanced task allocation may pose risks to the safety of UAVs and potentially lead to failures in monitoring tasks. For instance, continuous visits to nodes without replenishment may damage UAV batteries, while delays in recharging could result in missing task deadlines, ultimately causing task failures. Therefore, this study investigates the problem of achieving balanced multi-UAV path planning for persistent monitoring tasks, which has not been previously researched according to the authors’ knowledge. The main contribution of this study is the proposal of two novel indicators to assist in balancing task allocation regarding multi-UAV path planning for persistent monitoring. One of the indicators is namely the waiting factor, which reflects the urgency of a task node waiting to be accessed, and the other is the difficulty level which is introduced to measure the difficulty of tasks undertaken by a UAV. By minimizing differences in difficulty level among UAVs, we can ensure equilibrium in task allocation. For a single UAV, the ant colony initialized genetic algorithm (ACIGA) has been proposed to plan its path and obtain its difficulty level. For multiple UAVs, the K-means clustering algorithm has been improved based on difficulty levels to achieve balanced task allocation. Simulation experiments demonstrated that the difficulty level could effectively reflect the difficulty of tasks and that the proposed algorithms could enable UAVs to achieve balanced task allocation.

Treating inertial measurement unit (IMU) measurements as inputs to a motion model and then preintegrating these measurements have almost become a de facto standard in many robotics applications. However, this approach has a few shortcomings. First, it conflates the IMU measurement noise with the underlying process noise. Second, it is unclear how the state will be propagated in the case of IMU measurement dropout. Third, it does not lend itself well to dealing with multiple high-rate sensors such as a lidar and an IMU or multiple asynchronous IMUs. In this paper, we compare treating an IMU as an input to a motion model against treating it as a measurement of the state in a continuous-time state estimation framework. We methodically compare the performance of these two approaches on a 1D simulation and show that they perform identically, assuming that each method’s hyperparameters have been tuned on a training set. We also provide results for our continuous-time lidar-inertial odometry in simulation and on the Newer College Dataset. In simulation, our approach exceeds the performance of an imu-as-input baseline during highly aggressive motion. On the Newer College Dataset, we demonstrate state-of-the art results. These results show that continuous-time techniques and the treatment of the IMU as a measurement of the state are promising areas of further research. Code for our lidar-inertial odometry can be found at: https://github.com/utiasASRL/steam_icp.

Recent improvements in robotic arms have increased their interest in many areas such as the industry and biomedical sectors. Path planning is an essential part of the robotic arm, since most automated factories seek to move things from one place to another with obstacles providing the shortest route. This paper presents a novel optimal path planning algorithm based on the 3D cubic Bézier curve with three shape parameters and its geometric properties and hierarchical clustering. The proposed method utilizes a feature vector which is obtained from curvature, torsion, and path length of candidate curves. A hierarchical clustering is applied to determine curve pairs. Then, a multi-objective function is used to determine the best curve pair, which gives the best curve for the robotic arm. Besides forming the optimal 3D cubic Bézier path, the optimal ruled and developable path surfaces are obtained. In addition to presenting theoretical results, this work also demonstrates the proposed method on several Kinova Gen3 robotic arm cases.

The manufacturing of the X-shaped tip of prestressed centrifugal concrete piles is nowadays done half automatically by combining the manual worker and the automatic welding robot. To make this welding process full automatically, the welding seam tracking algorithm is considered. There are many types of sensors that can be used to detect the welding seam such as vision sensor, laser vision sensor, arc sensor, or touch sensor. Each type of sensor has its advantages and disadvantages. In this paper, an algorithm for welding seam tracking using laser distance sensor is proposed. Firstly, the fundamental mathematics theory of the algorithm is presented. Next, the positioning table system supports the procedure is designed and manufactured. The object of this research is the fillet joint because of the characteristics of the X-shaped tip of the concrete piles. This paper proposes a new method to determine the welding trajectory of the tip using laser distance sensor. After that, the experimental results are received to verify the proposed idea. Finally, the improved proposal of the algorithm is considered to increase the accuracy of the suggested algorithm.

This work proposes a novel grasp detection method, the Efficient Grasp Aware Network (EGA-Net), for robotic visual grasp detection. Our method obtains semantic information for grasping through feature extraction. It efficiently obtains feature channel weights related to grasping tasks through the constructed ECA-ResNet module, which can smooth the network’s learning. Meanwhile, we use concatenation to obtain low-level features with rich spatial information. Our method inputs an RGB-D image and outputs the grasp poses and their quality score. The EGA-Net is trained and tested on the Cornell and Jacquard datasets, and we achieve 98.9% and 95.8% accuracy, respectively. The proposed method only takes 24 ms for real-time performance to process an RGB-D image. Moreover, our method achieved better results in the comparison experiment. In the real-world grasp experiments, we use a 6-degree of freedom (DOF) UR-5 robotic arm to demonstrate its robust grasping of unseen objects in various scenes. We also demonstrate that our model can successfully grasp different types of objects without any processing in advance. The experiment results validate our model’s exceptional robustness and generalization.

In an effort to alleviate the issue of knee joint fatigue and injury during lower limb ambulation, a novel passive assisted exoskeleton robot with human–machine interaction is investigated to assist the movement of the human knee joint. The design of the exoskeleton configuration takes into consideration the physiological structure and gait function of the knee joint, ensuring that it satisfies the requirements for motion, force, and gait function of the knee joint. To explore the interaction between the wearer and the exoskeleton, a human–machine kinematic model after wearing exoskeleton is established, which is instrumental in analyzing the integration motion of the wearer and exoskeleton. In addition, the dynamic and static models of the knee joint after wearing the exoskeleton are established, utilizing the Newton–Euler method and force polygon method, respectively, to evaluate the effectiveness of the exoskeleton. Moreover, the size parameters and spring stiffness of the exoskeleton are optimized, using both human body kinematic model and mechanic model. Furthermore, the effectiveness of the exoskeleton in proving assistance is evaluated through human body simulation, using OpenSim. The results indicate that the exoskeleton significantly reduces the knee joint torque by 48.42%.

Machines and mechanisms realize processes, from the shaping process of a milling machine and the motion process of an automotive system to the trajectory realization of a robot. The dynamics of a machine generated by a properly chosen set of constraints in combination with an appropriate drive system is designed to meet the prescribed requirements of the process, which is done by projecting the machine equations of motion on the process dynamics. We get a set of nonlinear relations, which represent the machine motion in terms of the required process motion. A well-known example is the projection of arbitrary many robot degrees of freedom (DOFs) on a given path resulting in a set of nonlinear equations with one DOF only, the path coordinate s. Application of this idea can be used to construct a mobility space $(\ddot{s}, \dot{s}, s)$ for any combination of coordinates and constraints. The paper presents a corresponding approach for n-link robots by applying multibody system theory. Method might be interesting for layout of machines and mechanisms. Practical aspects are discussed, and an example is given.

Figures 2 and 5 were incorrectly reproduced in the above publication (Ratajczak et al. 2009). The figures are reproduced below in their correct form. Fig. 2.

Task-priority algorithm (both S1 and S2).

Single-task algorithm (only S1).

A direct subspace of a dynamic three-dimensional joint space is found to be useful for robot path planning in workspaces comprised of both static and dynamic objects. Dynamic descriptions permit positioning tables, automated guided vehicles, conveyors and cycling machine tools to be modeled by elements which translate or cycle along rectilinear paths. Graphical path planning procedures use cursor indicators to move the robot configuration point between the desired starting and final configurations while avoiding both the static and dynamic joint space obstacles.

The search space of the path planning problem can greatly affect the running time and memory consumption, for example, the concave obstacle in grid-based map usually leads to the invalid search space. In this paper, the filling container algorithm is proposed to alleviate the concave area problem in 2D map space, which is inspired from the scenario of pouring water into a cup. With this method, concave areas can be largely excluded by scanning the map repeatedly. And the effectiveness has been proved in our experiments.