Manipulation Algorithms Research Articles

An Anti-Interference Control Algorithm for Continuum Robot Arm

The large number of joints in a continuum manipulator complicates its dynamic modeling, making model simplification inevitable for practical motion control. However, due to external disturbances and internal noise, a controller based on the simplified dynamic model often struggles to meet the desired dynamic performance. To address this issue, this paper proposes an anti-interference control algorithm for continuum manipulators, designed to compensate for parameter uncertainties, external disturbances, and measurement noise. At the same time, the parameters of the algorithm are obtained in the form of solvability of linear matrix inequalities (LMIs). The simulation results show that the algorithm proposed in the paper provides better transient performance and is not affected by the entire disturbance. Experimental results further confirm the effectiveness and robustness of the algorithm.

BiCap: A novel bi-modal dataset of daily living dual-arm manipulation actions

Dual-arm manipulation of daily living objects is essential for robots operating in household environments. Learning from demonstration is a promising approach for teaching robots dual-arm manipulation skills. It usually requires a dataset of participants demonstrating the sequence of manipulation actions to achieve a goal. That sequence can be represented using symbolical or trajectory encoding. The symbolic-encoded sequence is known as the task plan. The chief limitations of current datasets are that most tend to disregard dual-arm manipulation skills and omit the formal grammar used to annotate the task plans. This paper introduces BiCap, a novel bi-modal dataset of dual-arm manipulation actions on daily living objects coupled with a bio-inspired action context-free grammar for fine-grained task plan annotation to train, test, and validate learning from demonstration-based algorithms for robotic dual-arm manipulation. 15 participants were recruited. The experimenter placed reflective markers on their upper limbs and pelvis. Then, the participants sat at a table where one or two objects were placed. They performed one of the following tasks: pouring, opening, and passing, using both hands. An RGB camera pointing towards the table recorded the participants’ hand movements. Subsequently, an annotator reviewed the RGB videos and wrote the participants’ task plans using the bio-inspired action context-free grammar. The participants’ upper-limb kinematics were computed, too, which provides the trajectory-encoded action sequences. The resulting dataset, BiCap, contains 4,026 task plans, videos, and motion data of the 15 participants.

EMSA-IK: a real-time evolutionary multi-objective semi-analytical inverse kinematics algorithm for redundant manipulators

Purpose This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators. Design/methodology/approach Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints. Findings Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application. Originality/value The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

The simple macroeconomics of AI

Abstract This paper evaluates claims about large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66% increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. Instead, AI is predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value.

Research on motion planning system for wall-climbing mobile manipulator for large steel structures welding operation

Purpose The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility. Design/methodology/approach A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling. Findings Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions. Originality/value The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

Human manipulation strategy when changing object deformability and task properties

Robotic literature widely addresses deformable object manipulation, but few studies analyzed human manipulation accounting for different levels of deformability and task properties. We asked participants to grasp and insert rigid and deformable objects into holes with varying tolerances and depths, and we analyzed the grasping behavior, the reaching velocity profile, and completion times. Results indicated that the more deformable the object is, the nearer the grasping point is to the extremity to be inserted. For insertions in the long hole, the selection of the grasping point is a trade-off between task accuracy and the number of re-grasps required to complete the insertion. The compliance of the deformable object facilitates the alignment between the object and the hole. The reaching velocity profile when increasing deformability recalls the one observed when task accuracy and precision decrease. Identifying human strategy allows the implementation of human-inspired high-level reasoning algorithms for robotic manipulation.

Distributed bounded finite‐time cooperative control algorithm for multiple nonlinear manipulators

AbstractFor the position synchronization control problem of multiple nonlinear manipulator systems, a bounded distributed cooperative controller is designed in this paper. Based on a fixed‐time position observer, a distributed finite‐time synchronization controller is designed by using the saturated control technique and homogenous system theory. Compared with the existing results, the proposed distributed cooperative control algorithm not only guarantees finite‐time convergence but also satisfies the input saturation. Finally, some numerical simulation results are presented to verify the feasibility of the theory results with six‐degree‐of‐freedom robots.

An Exercise in Tournament Design: When Some Matches Must Be Scheduled

Single-elimination (SE) tournaments are a popular format used in competitive environments and decision making. Algorithms for SE tournament manipulation have been an active topic of research in recent years. In this paper, we initiate the algorithmic study of a novel variant of SE tournament manipulation that aims to model the fact that certain matchups are highly desired in a sporting context, incentivizing an organizer to manipulate the bracket to make such matchups take place. We obtain both hardness and tractability results. We show that while the problem of computing a bracket enforcing a given set of matches in an SE tournament is NP-hard, there are natural restrictions that lead to polynomial-time solvability. In particular, we show polynomial-time solvability if there is a linear ordering on the ability of players with only a constant number of exceptions where a player with lower ability beats a player with higher ability.

Research on trajectory planning algorithm of manipulator based on visual servo

AbstractBackgroundWith the development and progress of society, not only substations, but also various industries are moving towards automation, intelligence, and unmanned direction. There is an increasing need for machinery to replace human activities. However, how to improve the ability of robotic arms to cope with various working environments and emergencies, and develop real‐time, high‐speed, and accurate trajectory planning algorithms has become a hot topic of discussion.PurposeIn order to develop a reasonable trajectory planning algorithm for robotic arms, this article studies the trajectory planning algorithm of robotic arms based on visual servoing, in order to improve the trajectory operation ability of robotic arms.MethodThe feasibility of the algorithm was tested through experiments.ResultsIt was found that the angle, angular velocity, and angular acceleration curves of both the second and fifth sixth joints of the robotic arm were very smooth, and there were no problems such as faults.ConclusionThis confirms the effectiveness of the algorithm's operation, which can assist the robotic arm in safe, accurate, and efficient operations on the basis of visual servoing.

Reactualising the problem of social engineering and digital security

The article explores the current aspects of social engineering in the digital age. Social engineering is considered as a strategic technology of constructing new meanings, principles, rules and facts of social interaction. The socio-philosophical concepts of K. Popper, P. Sorokin, and R. Silverstone are analyzed in the context of constructive proposals of social engineering. The application of historical and philosophical intellectual constructs to the practices of social transformations is described in the article. The article reveals the possibilities and limitations of digital technologies in social engineering. The risks of creating new tools and algorithms for manipulation, disorientation of users of virtual technologies by social engineering methods are shown within the framework of the digital security problem. The diversity of views on the essence of social engineering and the analysis of its spheres of application problematize the interpretation of its social role and meaning. The methods of constructing social events and interfering in people's lives require critical evaluation. The implementation of AI in social engineering developments leads to the new risks, which are systematized in the article. They are related to the manipulation of public consciousness, distortion of identification and personalisation methods, financial fraud, and violation of human security.

Explosive cyclones occurred between 2010 and 2020 in the South Atlantic under the perspective of two detection schemes.

Under two detection schemes, this study analyzes one of the most destructive weather systems - the explosive cyclones - in the South Atlantic, from 2010 to 2020. Then, two methods are presented to study these systems: the Observational Method (OBSM) and the Automated Method (AUTM). The first uses visual analysis of the mean sea level pressure (mslp) fields and functions to identify the local minimums using the Grid Analysis and Display (GrADS) software. The second utilizes a function from OpenGrADS called mfhilo. It shows the local minimum in the grid using laplacian, magnitude, and percentile. Two shell algorithms for data manipulation are used for the AUTM: one to trace the cyclones' trajectories according to a previously defined fixed area and the other to separate them into explosives. The OBSM methodology showed 271 cases averaging 25 yearly and revealed important characteristics regarding the intensities. According to AUTM's methodology, from the 2705 ordinary cyclone cases identified, 299 are explosives. There is a clear seasonality pattern in the systems' distribution along South America, similar to OBSM, but more highlighted. In summer, they concentrate at high latitudes, while in winter and spring, they are assembled near southern Brazilian and Uruguayan coasts.

FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

AbstractThis paper is concerned with the problem of collision-free path planning for manipulators in multi-obstacle scenarios. Aiming at overcoming the deficiencies of existing algorithms in excessive time consumption and poor expansion quality, a path planning algorithm named Fast Bi-directional Rapidly-exploring Random Tree (FBi-RRT) with novel heuristic node expansion is proposed, which includes a selective-expansion strategy and a vertical-exploration strategy. The selective-expansion strategy is designed to guide the selection of the nearest-neighbor node to avoid the repeated expansion failure, thereby shortening the overall planning time. Also, the vertical-exploration strategy is developed to regulate the expansion direction of the collision nodes to escape from the obstacle space with less blindness, thus improving the expansion quality and further reducing time cost. Compared with previous planning algorithms, FBi-RRT can generate a feasible path for manipulators in a drastically shorter time. To validate the effectiveness of the proposed heuristic node expansion, FBi-RRT is conducted on a 6-DOF manipulator and tested in five scenarios. The experimental results demonstrate that FBi-RRT outperforms the existing methods in time consumption and expansion quality.

Multiresolution Representation of Geoelectric Model and Parameter Operation in Wavelet Domain

In this paper, an algorithm for wavelet domain geoelectric model representation and parameter manipulation is introduced, which can be utilized for multiresolution inversions of geophysical data based on tree structure. With proper wavelet choice, the model can be represented by much less parameters than traditional model representation, expecting to reduce the computational cost for inversion remarkably. The use of tree structure to represent the wavelet coefficients makes the parameter operations (e.g., perturbating model parameter and adding more details in a model) in inversion much simpler. Introducing detail in a model or removing detail from a model can be simply realized by adding nodes to or deleting nodes from the existing tree structure. The model parameter value can be perturbated by changing the tree node values. Our algorithm is general and can be applied to develop any geophysical inversions in wavelet domain with trivial additional work once the forward modeling and inversion algorithm is available.

A novel tool path smoothing algorithm of 6R manipulator considering pose-dependent dynamics by designing asymmetrical FIR filters

The joint dynamic constraints of robot manipulators are normally set to conservative constant values to meet the dynamic tolerances at different poses, which leads to incomplete utilization of the joint drive capability. To fully utilize the drive capability of joint drives, this paper proposes an asymmetrical finite impulse response (FIR) filter-based tool path smoothing algorithm, which considers the pose-dependent dynamics of the robot with 6 rotational (6R) joints. To analyze the pose-dependent motor drive capabilities, a simplified dynamics model is established by considering both the tangential and joint dynamic constraints. An asymmetrical FIR filter-based tool path smoothing algorithm is also proposed to realize different constraints of the acceleration and the deceleration limits at different positions of the linear segments. The tool tip position and the tool orientation commands are respectively smoothed in the Cartesian coordinate system and spherical coordinate system, with the constraints of the tool path deviations in the task frame. The synchronization of the position and the orientation is realized by adjusting the parameters of FIR filters. The simulation and experimental results show that the proposed algorithm guarantees the corner error tolerances of the tool tip position and the tool orientation. Moreover, the proposed method improves the robot motion efficiency by more than 10 % through the full utilization of the joint drive capability compared with the traditional tool path smoothing algorithm based on symmetrical FIR filters.

A Time-Optimal Continuous Jerk Trajectory Planning Algorithm for Manipulators

In this paper, we propose a new optimal trajectory planning method for the manipulator to optimize its operating efficiency and ensure the smoothness of the motion process. The position sequences in joint space corresponding to a specified trajectory in task space are obtained by using the inverse kinematic algorithm, and the seventh-degree B-spline curve interpolation method is used to construct the joint trajectory with controllable start–stop kinematic parameters, and continuous velocity, acceleration and jerk. The kinematic constraints of the manipulator are transformed into the control point constraints of the B-spline curve, the sequential quadratic programming method is used to solve the optimal motion time node, and then the time-optimal continuous jerk trajectory satisfying the nonlinear kinematic constraints is planned. Simulation results show that the proposed trajectory planning method provides the ideal trajectory for the joint controller, ensuring the manipulator can smoothly track any specified trajectory in the task space in the shortest time.

A Forward Solution Algorithm of 6RUS Parallel Mechanism Based on Dual Quaternion Method

The 6RUS parallel manipulator is a highly versatile and widely used robotic mechanism with six degrees of freedom. Its intricate kinematic structure and its capability to perform complex motion tasks have garnered significant research interest in recent years. The kinematic analysis of the 6RUS mechanism plays a crucial role in understanding its operational characteristics and optimizing its performance for various applications. In this paper, we present a state-of-the-art kinematic algorithm for the 6RUS parallel manipulator. Our algorithm is aimed at addressing the challenges associated with accurately determining the pose and motion of the end-effector relative to the base, considering the complexity of the mechanism’s architecture. By leveraging advanced mathematical modeling techniques and utilizing efficient computational algorithms, our proposed algorithm offers improved accuracy, efficiency, and robustness in determining the kinematic parameters of the 6RUS mechanism. The key contributions of this work include the development of a comprehensive forward and inverse kinematic model for the 6RUS parallel manipulator, incorporating the effects of joint constraints, singularities, and workspace limitations. We also present a detailed analysis of the algorithm’s performance in comparison to existing approaches, demonstrating its superiority in terms of computational efficiency and accuracy. The proposed kinematic algorithm holds significant potential for enhancing the design, control, and trajectory planning of 6RUS parallel manipulators. It provides a solid foundation for advanced applications such as robotic surgery, industrial automation, and virtual reality systems. The results presented in this paper contribute to the growing body of knowledge in parallel manipulator research and pave the way for future developments in the field.

Parallel molecular computation on digital data stored in DNA

DNA is an incredibly dense storage medium for digital data. However, computing on the stored information is expensive and slow, requiring rounds of sequencing, in silico computation, and DNA synthesis. Prior work on accessing and modifying data using DNA hybridization or enzymatic reactions had limited computation capabilities. Inspired by the computational power of "DNA strand displacement," we augment DNA storage with "in-memory" molecular computation using strand displacement reactions to algorithmically modify data in a parallel manner. We show programs for binary counting and Turing universal cellular automaton Rule 110, the latter of which is, in principle, capable of implementing any computer algorithm. Information is stored in the nicks of DNA, and a secondary sequence-level encoding allows high-throughput sequencing-based readout. We conducted multiple rounds of computation on 4-bit data registers, as well as random access of data (selective access and erasure). We demonstrate that large strand displacement cascades with 244 distinct strand exchanges (sequential and in parallel) can use naturally occurring DNA sequence from M13 bacteriophage without stringent sequence design, which has the potential to improve the scale of computation and decrease cost. Our work merges DNA storage and DNA computing, setting the foundation of entirely molecular algorithms for parallel manipulation of digital information preserved in DNA.

Trajectory planning and control algorithm of industrial robot manipulator

As the industrial robot task becomes more complex, the difficulty of trajectory planning and tracking control of manipulator is gradually increasing. To minimize the vibration during the manipulator motion and improve the planning accuracy, the method of quintic polynomial combined with non-uniform B-spline interpolation is studied for joint space (JS) planning. The trajectory tracking system is easily affected by friction nonlinearity and parameters. So a JS trajectory tracking controller based on based on fuzzy neural network (FNN) is designed. Through simulation experiments, the curve obtained by the planning method studied is smoother and the planning error is minimum. The maximum position error is 0.09 rad, and the speed error is not more than 0.1 rad/s. The controller performance test results under different parameters show that the W^, c^, κ^ parameter in FNN can be adjusted in real time, and the value will not affect the performance of the controller. The fluctuation range of trajectory error of different joints is within ±0.2×10-5rad, which indicates that the performance of AFNNC controller studied is better. And its response time is the shortest and its robustness is better when the load changes suddenly.

A Distributed, Event-Triggered, Adaptive Controller for Cooperative Manipulation With Rolling Contacts

We present a distributed, event-triggered, and adaptive control algorithm for cooperative object manipulation with rolling contacts and unknown dynamic parameters. Whereas conventional cooperative manipulation methods require rigid contact points, our approach exploits rolling effects of passive end-effectors and does not require force/torque sensing. The removal of rigidity allows for more modular grasping, increased application to more object types, and online adjustment of the grasp. The proposed control algorithm exhibits the following properties: 1) it is distributed, in the sense that the robotic agents calculate their own control signal, under an event-triggered communication scheme. Such a scheme reduces the interagent communication requirements with respect to continuous communication schemes; 2) it uses an online adaptation mechanism to accommodate for unknown dynamic parameters of the object and the agents and 3) it adapts existing internal force controllers to guarantee no slip throughout the manipulation task despite the event-triggered nature of the communication scheme. Hardware implementation validates the effectiveness of the proposed approach.

A High-Certainty Visual Servo Control Method for a Space Manipulator with Flexible Joints.

This paper introduces a novel high-certainty visual servo algorithm for a space manipulator with flexible joints, which consists of a kinematic motion planner and a Lyapunov dynamics model reference adaptive controller. To enhance kinematic certainty, a three-stage motion planner is proposed in Cartesian space to control the intermediate states and minimize the relative position error between the manipulator and the target. Moreover, a planner in joint space based on the fast gradient descent algorithm is proposed to optimize the joint's deviation from the centrality. To improve dynamic certainty, an adaptive control algorithm based on Lyapunov stability analysis is used to enhance the system's anti-disturbance capability. As to the basic PBVS (position-based visual servo methods) algorithm, the proposed method aims to increase the certainty of the intermediate states to avoid collision. A physical experiment is designed to validate the effectiveness of the algorithm. The experiment shows that the visual servo motion state in Cartesian space is basically consistent with the planned three-stage motion state, the average joint deviation index from the centrality is less than 40%, and the motion trajectory consistency exceeds 90% under different inertial load disturbances. Overall, this method reduces the risk of collision by enhancing the certainty of the basic PBVS algorithm.