Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Three-dimensional ultrasound image-guided robotic system for accurate microwave coagulation of malignant liver tumours

Background The further application of conventional ultrasound (US) image-guided microwave (MW) ablation of liver cancer is often limited by two-dimensional (2D) imaging, inaccurate needle placement and the resulting skill requirement. The three-dimensional (3D) image-guided robotic-assisted system provides an appealing alternative option, enabling the physician to perform consistent, accurate therapy with improved treatment effectiveness. Methods Our robotic system is constructed by integrating an imaging module, a needle-driven robot, a MW thermal field simulation module, and surgical navigation software in a practical and user-friendly manner. The robot executes precise needle placement based on the 3D model reconstructed from freehand-tracked 2D B-scans. A qualitative slice guidance method for fine registration is introduced to reduce the placement error caused by target motion. By incorporating the 3D MW specific absorption rate (SAR) model into the heat transfer equation, the MW thermal field simulation module determines the MW power level and the coagulation time for improved ablation therapy. Two types of wrists are developed for the robot: a ‘remote centre of motion’ (RCM) wrist and a non-RCM wrist, which is preferred in real applications. Results The needle placement accuracies were < 3 mm for both wrists in the mechanical phantom experiment. The target accuracy for the robot with the RCM wrist was improved to 1.6 ± 1.0 mm when real-time 2D US feedback was used in the artificial-tissue phantom experiment. By using the slice guidance method, the robot with the non-RCM wrist achieved accuracy of 1.8 ± 0.9 mm in the ex vivo experiment; even target motion was introduced. In the thermal field experiment, a 5.6% relative mean error was observed between the experimental coagulated neurosis volume and the simulation result. Conclusion The proposed robotic system holds promise to enhance the clinical performance of percutaneous MW ablation of malignant liver tumours. Copyright © 2010 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78054/1/313_ftp.pd

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper