Continuation method approach to trajectory planning in robotic systems

Processing configuration off-line optimization for functionally redundant robotic drilling tasks

Robotic systems have significantly transformed the landscape of minimally invasive surgery, offering enhanced precision, reduced recovery times, and improved patient outcomes. These systems have evolved from simple mechanical tools to sophisticated platforms integrating advanced control algorithms and real-time feedback mechanisms. This paper provides a comprehensive overview of the role of robotic systems in minimally invasive surgery, exploring the various benefits, inherent risks, and the challenges associated with their adoption and development. We examine the technical aspects of robotic control systems, such as kinematics, force control, and haptic feedback, which enable greater surgical accuracy. Despite these advantages, challenges such as system complexity, high costs, and ethical concerns remain significant barriers to widespread adoption. The future of robotic surgery lies in overcoming these obstacles, with advancements in system miniaturization and further refinement of control systems poised to enhance the efficacy and accessibility of robotic platforms. This paper explores the role of robotic systems in minimally invasive surgery, focusing on three key areas: bronchoscopy and ureteroscopy. By reviewing the benefits, risks, case studies, and future directions of these technologies, the paper seeks to provide an in-depth understanding of how robotic systems are shaping the future of surgery. Key words: Robotic Surgery, Minimally Invasive Surgery, Robotic Control Systems, Surgical Precision, PID Control, Kinematics, Inverse Kinematics, Forward Kinematics, Dynamic Control, Force Control, Impedance Control, Surgical Robotics, Haptic Feedback, Robotic Arm, Robotic Surgery Benefits, Robotic Surgery Risks, Robot-Assisted Surgery, Surgeon Console, Real-Time Feedback, Surgical Training, Robotic System Development, System Testing, Clinical Trials, Surgical Robot Performance, Robotics in Urology, Telepresence Surgery, Robotic Surgery Cost, Robotic Surgery Future Directions.

Editorial: Endoscopic coronary artery bypass grafting—The first steps on a long journey

PurposeThe purpose of this paper is to present a novel automatic planning and coordinated control method of redundant dual‐arm space robot for inner space‐station operation based on multiple sensors information by stages.Design/methodology/approachIn order to improve the coordinated control capability of dual‐arm robot system, a four‐layer hierarchical control structure is designed based on the theory of centralization and decentralization. At the high‐level planning of dual‐arm system, a task decomposition strategy based on task knowledge and a task allocation strategy in terms of the robotic capability are proposed, respectively. Moreover, a control method by stages based on the information of multiple sensors is introduced to object recognition, task planning, path planning and trajectory planning. Finally, a 3D simulation and experiment of screwing nut and bolt are implemented on a dual‐arm robot system, and the feasibility and applicability of this control strategy are verified.FindingsThe automatic planning can be accomplished by means of sensors information by stages, and by this method, the autonomy and intelligence of dual‐arm space robot system can be further improved.Practical implicationsA new automatic planning strategy integrated with multiple sensors information by stages is proposed, and can be implemented on a dual‐arm robot system for inner space‐station operations. This method specializes in heterogeneous dual‐arm robot system.Originality/valueA task decomposition strategy based on task knowledge and a task allocation strategy in terms of the robotic capability are proposed, respectively. Moreover, a control method by stages based on the information of multiple sensors is introduced to object recognition, task planning, path planning and trajectory planning of dual‐arm robot system.

This paper deals with trajectory planning and motion learning for a redundant manipulator. Recently, behavior engineering for robotic systems has been discussed as a new technological discipline. A robotic system requires the whole structure of intelligence, and acquires skill and knowledge through interaction with a dynamic environment. Consequently, the whole structure determines the potentiality of intelligence. This paper proposes a robotic system with structured intelligence. Based on perceptual information, a robot with structured intelligence makes decision and action from four levels in parallel. In addition, the robot generates its motion through interaction with the environment, and at the same time, gradually acquires its skill based on the generated motion. To acquire skill and motion, the robot requires internal and external evaluations at least. This paper applies a virus-evolutionary genetic algorithm for trajectory planning and applies neural network for motion learning. Furthermore, we discuss its effectiveness through computer simulation results.

We describe a new method for adaptive model-based control of robotic dynamic systems using a new hybrid neuro-fuzzy-fractal approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. Optimal control of many robotic systems also requires methods which make use of predictions of future behavior. We describe an intelligent system for controlling robot manipulators to illustrate our neuro-fuzzy-fractal hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant selection parameters for the problem. We use neural networks for identification and control of robotic dynamic systems.

More than 130,000 people have strokes each year in the United States [1]. Of these victims, 76% are left with disabilities that cost the nation over $54 billion in lost work and medical fees. One prominent disability is upper extremity hemiplegia, which occurs among approximately 50% of stroke sufferers [2]. Robotic technology has the potential to provide an automated platform for controlled rehabilitation and assisted, task-oriented therapy. Several systems have been designed to assist in patient articulation of an impaired hand using rigid robotic components. While these products have been successful in articulating the pure bending motions of the four fingers, the limited capabilities of rigid technologies fail to reproduce the complicated motion path [5] of the thumb during opposition grasp (see Figure 1). This is the most important articulation for normal hand function and specifically for picking up everyday objects. To date, robotic systems for thumb rehabilitation have not been widely investigated [3] apart from some recent work [4] that used a multi-joint rigid robot.

Control systems for autonomous robots are concurrent, distributed, embedded, real-time and data intensive software systems. A real-world robot control system is composed of tens of software components. For each component providing robotic functionality, tens of different implementations may be available. The difficult challenge in robotic system engineering consists in selecting a coherent set of components, which provide the functionality required by the application requirements, taking into account their mutual dependencies. This challenge is exacerbated by the fact that robotics system integrators and application developers are usually not specifically trained in software engineering. In various application domains, software product line (SPL) development has proven to be the most effective approach to face this kind of challenges. In a previous paper [D. Brugali and N. Hochgeschwender, Managing the functional variability of robotic perception systems, in First IEEE Int. Conf. Robotic Computing, 2017, pp. 277–283.] we have presented a model-based approach to the development of SPL for robotic perception systems, which integrates two modeling technologies developed by the authors: The HyperFlex toolkit [L. Gherardi and D. Brugali, Modeling and reusing robotic software architectures: The HyperFlex toolchain, in IEEE Int. Conf. Robotics and Automation, 2014, pp. 6414–6420.] and the Robot Perception Specification Language (RPSL) [N. Hochgeschwender, S. Schneider, H. Voos and G. K. Kraetzschmar, Declarative specification of robot perception architectures, in 4th Int. Conf. Simulation, Modeling, and Programming for Autonomous Robots, 2014, pp. 291–302.]. This paper extends our previous work by illustrating the entire development process of an SPL for robot perception systems with a real case study.

The kinematic and dynamic analysis of the high-degree of freedom (dof) hybrid systems that using serial and parallel robots together on fixed or mobile platform such as legged robots and service robots, is a rather difficult and complex, time consuming problem and a topic of great interest for researchers. There are many educational and commercial toolboxes which analyze serial and parallel robots separately. With the toolbox developed here, high-dof serial, parallel and hybrid robotics systems used on both fixed and mobile platforms can be designed and studied easily. Forward and inverse kinematics, Jacobian matrix, singularities, trajectory planning, torques and forces in joint and operational space can be examined with different robot designs. Using the toolbox that proposed in this paper, robot designer, researchers, students and educators can easily develop their own robotics systems and visualize them in the virtual reality environment provided by MATLAB. Also, it helps understanding fundamentals of high-dof multi-body hybrid robotics systems through user friendly interactive simulation. The most of existing toolboxes in the literature allow only user to analyze the predefined robots in their library with limited interactive capabilities. In some of them, the user can define a new robot with limited dof. But, the user must already have a prior knowledge about designing process of robots. In the proposed toolbox, designing and defining any new robotics systems become very easy. What makes this toolbox unique compared with the existing toolboxes is that any type of robot structures with high dof can be developed, analyzed and studied in detail.

The authors previously (1996) proposed a new type of space robot system, consisting of a spacecraft and a robot attached through a tether to it. This system is called a robot system. In this paper, we clarify the characteristics of the tethered robot system, and especially focus on position control of the center of mass of the robot. Tether tension is used to control the position, taking into account the gravity gradient and the centrifugal force. The results from the trajectory planning procedure suggest that the shape of the path depends both on the direction to the destination point and the time to accomplish the mission, it does not depend on the distance. From the simulation results with feedforward control it is concluded that accurate path planning is possible only if the destination point is close to the initial point, or if the time to accomplish the mission is long enough.

In many applications of advanced robotic systems, reaction forces and moments transmitted by a manipulator to its base are highly undesirable. Such reactions reduce the accuracy of high-speed manipulators, destroy zero-g environments in space, require the use of thruster fuel to stabilize free-flying space robots, or excite suspension modes in mobile robotic systems. In this paper, we analyze the problem of force and torque transmission in robotic systems, and propose design and planning methods that can reduce it, or eliminate it. It is shown that designing a force-balanced manipulator with an invariant mass matrix, and employing appropriate trajectory planning, can result in reactionless motions. Two redundant planar manipulator designs, that can be used as building blocks of more complicated systems, demonstrate the usefulness of the proposed methods. An important advantage of these methods is that if reactionless behavior is not needed, system redundancy can be recovered.

The low gravimetric and volumetric energy densities of energy storage technologies are principal limitations for realizing the full potential of modern robotics technologies[1]. In many cases, the size and weight of energy storage technologies required to power robotic systems are too large or massive for a robot to carry untethered. Therefore, most robotic systems are tethered, or constrained to limited operational times and long-recharging times over which they remain unused. Energy storage technologies are particularly limiting for micrometer to centimeter scale robots, vehicles, and sensors, as microbatteries dominate the size and mass of the corresponding devices [2,3]. One approach to overcome the energy limitations of batteries is to use battery chemistries with increased energy density. Metal-air batteries are particularly appealing due to their high energy densities. Iron-air, zinc-air, and aluminum-air batteries have 1229, 1535, and 8076 Wh/kg theoretical energy densities [4], which is 5 – 32 X larger than commercial lithium ion batteries. Practical metal-air batteries, however, only achieve a fraction of the theoretical energy density (350 – 500 Wh/kg for Zn-air, for example) and suffer from poor and inefficient rechargeability. Although promising, realizing rechargeable metal-air batteries with near theoretical energy densities is likely several years or decades away. New approaches for powering robots that take advantage of specific environments or use-cases are required to realize untethered robotic systems in the near future. In a conventional battery, active materials are stored in a rigid package so that the maximum volumetric energy density is the energy available in the lowest capacity electrode divided by the total battery volume or mass. A robot, therefore, is required to carry both the active material mass as well as the packaging and electrolyte required for the battery to work. In metal-air batteries, the pertinent electrode is the solid metal anode which oxidizes to produce electrons and, typically, a metal hydroxide or metal oxide. An electrolyte connects the metal anode to a thin porous cathode that reduces oxygen in the air. Metal-air batteries have high energy densities because oxygen for the cathode reaction can be extracted from the air and therefore does not contribute to the battery mass or volume. Electrons from the anode are transferred to the cathode through the robot electronics, powering the robot. In this work, we present a metal-air scavenger (MAS), which is a device that extracts electrical energy from external metal surfaces to power centimeter scale or smaller robots, vehicles, and electronics. The MAS undergoes the same chemical reactions as a metal-air battery, but is only composed of a polymer electrolyte and cathode current collector so that both the anode and cathode active materials are external to the device. The powered devices, therefore, only need to carry the mass of the electrolyte and cathode current collector. We show that, when stationary, a MAS extracts near the theoretical capacity of aluminum and zinc surfaces, as well as extracts up to 1,450 mAh/g from iron surfaces. The maximum power densities of a MAS on aluminum, zinc, and iron surfaces are 130, 80, and 25 mW/cm2. A principal advantage of the MAS is that it can continue to power a device as it moves across a metal surface, so that the total mass and volume of metal oxidized are many multiples of the mass and volume of the device. We demonstrate the utility of a MAS by powering a 5 x 4 x 2 cm electric vehicle on aluminum and zinc surfaces with a 2 x 2 x 0.3 cm MAS. The MAS powered the vehicle over many square feet of metal surface and at up to 8 mm/s velocity. The MAS concept presented here takes advantage of the prolific variety of external metal energy sources available in modern urban environments. We believe the MAS will find utility in many applications, lead to further unconventional methods for powering robotic systems, and open new materials development opportunities. Yang, Guang-Zhong, et al. "The grand challenges of Science Robotics." Science Robotics3.14 (2018): eaar7650.St. Pierre, Ryan, and Sarah Bergbreiter. "Toward Autonomy in Sub-Gram Terrestrial Robots." Annual Review of Control, Robotics, and Autonomous Systems(2019).Oudenhoven, Jos FM, Loïc Baggetto, and Peter HL Notten. "All‐solid‐state lithium‐ion microbatteries: a review of various three‐dimensional concepts." Advanced Energy Materials1.1 (2011): 10-33.Li, Yanguang, and Jun Lu. "Metal–air batteries: will they be the future electrochemical energy storage device of choice?." ACS Energy Letters2.6 (2017): 1370-1377. Figure 1

This thesis focuses on the problem of efficiently allocating resources for enhancing the performance of an autonomous robotic agent. Such an agent is expected to operate in complex dynamic environments by continuously monitoring its internal states and the external events. These requirements give raise to countless problems that have populated research in the autonomous robotics community in the last two decades. Among these issues, one of the most relevant is to coordinate different low and high-level behaviors, giving them, from time to time, different priority values both for resource allocation and for action selection processes. The main problem in achieving this requirement is that the number and complexity of the stimuli received by each behavior may be quite high and also the effects on the emerging activity may be very hard to foresee. It is clear that it is not possible for the robotic control system to process all the incoming information, especially for real-time applications. Thus, it becomes necessary to build mechanisms able to guide this sensory input selection process and to choose the best action to perform, assuring an efficient use of the robot limited sensorial and cognitive resources. For this purpose, attentional mechanisms, balancing sensors elaboration and actions execution, can be very useful since they play two main roles: they focus the attention on salient regions of the space and they distribute resources and activities in time. As a result of the application of these mechanisms within the robotic control system for sensory-motor coordination, the robot behavior is improved: the robot becomes able to react faster to task-related or safety-critical stimuli and to opportunely split resources among concurrent behaviors. Attentional mechanisms applied to autonomous robotic systems have already been proposed, but mainly for vision-based robotics; conversely, the contribution introduced by the present thesis is the use of an artificial attentional mechanism suitable both for optimizing the use of resources and for execution monitoring and control.

The main field of our research is artificial intelligence already implemented in the experimental autonomous road vehicle. Artificial intelligence now is a really huge area of studies. This investigation is focused more on a development of intelligent systems. Such as expert systems, semantic text processing systems, semantic image understanding systems, robotics systems. Wi!Mi is a solution for developing mivar knowledge models. This tool belongs to the class of so-called ES shells - programs that allow significantly simplify and accelerate the ES development process. It uses a novel knowledge representation mechanism based on bipartite graphs and the significantly improved but simple inference engine, which is able to process large systems involving millions of variables in just a few seconds. A special representation model which is called mivar nets with linear computational complexity using if-then rules is used in Wi!Mi. The idea is that the subject domain is divided into objects and connections between them. Scientific investigation allows to implement new decision-making system called “RoboRazum” as a form of an embedded software platform with flexible capabilities to adapt to control by any robotic suites and systems. RoboRazum is a platform for designing autonomous robotic control systems. Provided solutions don’t require powerful computational resources and could be used in small systems-on-chip like microcontrollers. That is why this solution is perfectly fit with conception of Internet of Things (IoT). Reasoned and fast logical decisions based on mivar technologies play important role for autonomous road vehicles.

Under rapid development of science and industry the industrial robots and robotic systems are more widely used in the field of industry. The robots and robotic systems help to manage and master new technological processes efficiently and effectively. In this article there was carried out the analysis of the two degree of freedom (DOF) robot arm, which is widely used in practice in order to reveal its functions and to identify its key dynamic parameters using fuzzy logic system. The mathematical model of robot arm dynamics is composed, which includes the mathematical models of the robot arm links twirling motors and the direct positions task model generating the end point of the robot. After the analysis of the robot model the expanded research was performed – after calculating the missing parameters of experimental 2DOF robot arm motors there was created the robot arm model with „V-REP“ and “MATLAB Simulink” software packages that allowed to set the control voltages and to observe the changing in time processes of link velocity trajectories. There is calculated designed planar robot end point trajectory deviation from the dynamic trajectory.