Motion Planning Method of Continuum Manipulator Based on Multi Objective Optimization

In recent years, a number of trajectory optimization algorithms have been proposed and established for motion planning of robot manipulators in complex, but static, predefined environments. To enable reactive motion planning under uncertain conditions caused, for example, by moving obstacles, this paper proposes a formulation of the trajectory optimization problem that is tailored for model predictive control. The proposed algorithmic solution leverages off-the-shelf computational tools for nonlinear model predictive control, optimization, and collision checking. In addition, a motion planning paradigm is introduced to allow for online collision-free motion when following a joystick command. The approach is validated in the context of an industrial pick-and-place application using MATLAB® and a Kinova® robot manipulator, both in simulation and with actual hardware.

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

Aerial manipulators can manipulate objects while flying, allowing them to perform tasks in dangerous or inaccessible areas. Advanced aerial manipulation systems are often based on rigid-link mechanisms, but the balance between dexterity and payload capacity limits their broader application. Combining unmanned aerial vehicles with continuum manipulators emerges as a solution to this trade-off, but these systems face challenges with large actuation systems and unstable control. To address these challenges, we propose Aerial Elephant Trunk, an aerial continuum manipulator inspired by the elephant trunk, featuring a small-scale quadrotor and a dexterous, compliant tendon-driven continuum arm for versatile operation in both indoor and outdoor settings. We develop state estimation for the quadrotor using an Extended Kalman Filter, shape estimation for the continuum arm based on piecewise constant curvature, and whole-body motion planning using minimum jerk principles. Through comprehensive fundamental verifications, we demonstrate that our system can adapt to various constrained environments, such as navigating through narrow holes, tubes, or crevices, and can handle a range of objects, including slender, deformable, irregular, or heavy items. Our system can potentially be deployed in challenging conditions, such as pipeline maintenance or electricity line inspection at high altitudes.

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: the redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of 90%, with an increase of less than 15% of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately 50% and 80%, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system.

This paper investigates the motion planning of redundant free-floating manipulators with seven prismatic joints. On the earth, prismatic-jointed manipulators could only position their end-effectors in a desired way. However, in space, the end-effectors of free-floating manipulators can achieve both the desired orientation and desired position due to the dynamical coupling between manipulator and satellite movement, which is formally expressed by linear and angular momentum conservation laws. In this study, a tractable algorithm particle swarm optimization combined with differential evolution (PSODE) is provided to deal with the motion planning of redundant free-floating prismatic-jointed manipulators, which could avoid the pseudo inverse of the Jacobian matrix. The polynomial functions, as argument in sine functions are used to specify the joint paths. The coefficients of the polynomials are optimized to achieve the desired end-effector orientation and position, and simultaneously minimize the unit-mass-kinetic energy using the redundancy. Relevant simulations prove that this method provides satisfactory smooth paths for redundant free-floating prismatic-jointed manipulators. This study could help to recognize the advantages of redundant prismatic-jointed space manipulators.

Objectives. The work analyzes features of one of the most promising approaches to solve the problems for motion planning of autonomous robotic manipulators of various types and purposes using the rapidly exploring random tree (RRT) method. The development of modern robotics is shown to be inextricably linked with the improvement of the designs of the created samples, for which the placement of a manipulator on platform becomes a typical layout option. Prospects for using the RRT method as a constructive basis for creating a universal motion planner are evaluated for mobile and robotic manipulators, including autonomous robotic systems with a manipulator on a moving platform.Methods. The object of the research is the RRT method and its well-known modifications RRT* and RRT-Connect. The effectiveness of applying such methods for solving problems associated with planning the motions of robotic manipulators of various types was evaluated using computer and natural simulation methods.Results. Based on a review of the literature and the results of the research, the wide possibilities of the RRT method can be used for solving motion planning problems not only for mobile and robotic manipulators, but also for robotic systems on whose transport platform an onboard manipulator has been installed (including those having a redundant or reconfigurable structure). The effectiveness of the applied application of the RRT method is confirmed by examples of modeling a mobile platform with an onboard manipulator and the results of full-scale experiments with a prototype of the ARAKS reconfigurable mechatronic-modular robotic manipulators (RTU MIREA, Russia). It can be experimentally demonstrated and theoretically substantiated that the final dimension of the exploring tree, and hence the time of its construction up to reaching a given target state, is largely determined by the value of the growth factor.Conclusions. The generalization of the results obtained opens up real prospects for using the RRT method as a constructive basis not only for creating universal means for motion planning mobile robotic systems with an onboard manipulator, but also for solving the problems of automating the docking of autonomous mobile platforms.

Due to the excessive degree of freedom of the space flexible manipulator, we can hardly obtain its accurate dynamic model for its motion planning. In this work, we formulate the precise motion control of the free-floating space piecewise constant curvature (FSPCC) continuum manipulator (i.e. space flexible manipulator) as a sparse reward problem in reinforcement learning, and use the Soft Actor-Critic (SAC) algorithm along with the Random Network Distillation (RND) method to train the optimal policies. Firstly, we use the RND method to jointly train a predictive network and a fixed network. The discrepancy between the output values of the two networks is served as an internal reward for the environment. Secondly, the SAC algorithm aims to maximize the expected return and the entropy of the policy. Policies with high entropy will successfully complete the task while acting as randomly as possible. Finally, the internal rewards tend to incentivize the agent to explore more widely for faster convergence of the algorithm. We applied this method to the FSPCC continuum manipulator simulation model and the results demonstrate that the SAC algorithm together with RND method can control the FSPCC continuum manipulator to catch the target quickly, even in the presence of sparse reward.

We present an optimization-based motion planning algorithm to compute a smooth, collision-free trajectory for a manipulator used to transfer a liquid from a source to a target container. We take into account fluid dynamics constraints as part of the trajectory computation. In order to avoid the high complexity of exact fluid simulation, we introduce a simplified dynamics model based on physically inspired approximations and system identification. Our optimization approach can incorporate various other constraints such as collision avoidance with obstacles, kinematic and dynamics constraints of the manipulator, and fluid dynamics characteristics. We demonstrate the performance of our planner on different benchmarks corresponding to various obstacles and container shapes. We also evaluate its accuracy by validating the motion plan using an accurate but computationally costly Navier-Stokes fluid simulation.

We present a two-layered architecture for task-agent assignment and motion planning of Bi-manual Mobile manipulators for executing complex tasks. We use search trees in temporal windows to determine feasible task assignments of agents using task and spatial constraint-based heuristics. We also introduce a caching scheme for moving between different trees so as to avoid re-planning of those portions of the robot motion that were successful. This greatly reduces the number of calls to the motion planner as compared to direct motion planning after task-agent assignment. We have shown our approach works on a set of complex tasks with significantly lower computation times.

Due to the space constraints and obstacles, the traditional industrial manipulator is too difficult to achieve some tasks, such as the gluing for the wing bulkhead of the aircraft and the maintenance for cooling pipes of the nuclear power plant, etc. Continuum manipulator, inspired by the trunk and the tentacle, proves to be very effective for above-mentioned tasks. A novel octopus-like biomimetic robots, is proposed in this paper, which is consisting of continuum joints and discrete joints, and provide a host of benefits, such as the large space of movement, the high flexibility and the heavy load. A novel analytical approach for solving kinematics of the octopus-like arm manipulator with mixed joints is presented in this paper. Based on the bionic mechanism of the continuum manipulator constructed from mixed joints, the robot configuration is established. In this paper, we present a detailed formulation and explanation of a novel kinematic model for the continuum robots with mixed joints. The modeling method based on the Denavit–Hartenberg parameters(also called DH parameters) is used to depict the motion of robot. The robot is comprised of the continuum joint and the rotated joint, so the kinematic model of continuum joint is crucial for constructing that of the whole robot. The continuum joint is equivalent to a section of elastic body, whose D-H parametors can be obtain from the constant-curvature method. Then the forward kinematics of the whole robot can be builded in a D-H frame. Research results will create a new modeling method for the octopus-like continuum manipulators with mixed joints, which can give a new approach for the design on the biomimetic manipulators operating in the unstructured envirement.

Although tendon-driven continuum manipulators have been extensively researched, how to realize tip contact force sensing in a more general and efficient way without increasing the diameter is still a challenge. Rather than use a complex modeling approach, this paper proposes a general tip contact force-sensing method based on a recurrent neural network that takes the tendons’ position and tension as the input of a recurrent neural network and the tip contact force of the continuum manipulator as the output and fits this static model by means of machine learning so that it may be used as a real-time contact force estimator. We also designed and built a corresponding three-degree-of-freedom contact force data acquisition platform based on the structure of a continuum manipulator designed in our previous studies. After obtaining training data, we built and compared the performances of a multi-layer perceptron-based contact force estimator as a baseline and three typical recurrent neural network-based contact force estimators through TensorFlow framework to verify the feasibility of this method. We also proposed a manually decoupled sub-estimators algorithm and evaluated the advantages and disadvantages of those two methods.

PurposeThe purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.Design/methodology/approachAnother RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.FindingsIt is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.Originality/valueThe paper presents a new nested RRT algorithm for fault tolerant motion planning.

This article is the first of three companion papers which introduce some novel concepts useful in the kinetmatic design, analysis, motion planning, control, and performance evaluation of serial manipulators. Here an efficient methodology is developed to classify the special configurations of an N DOF manipulator. This classification yields hyper‐surfaces in the joint space on each of which the manipulator loses a certain number of its degrees of freedom. The efficiency of the methodology is based on a theorem proved in Appendix B and the utilization of the minimal reference frames. These frames are obtained and tabulated for all possible types of manipulators. Furthermore the adaptability of a manipulator to a common control algorithm is quantified via an index. The numerical values of these indices are listed for all types of manipulators.

Geometry perception and motion planning in robotic assembly based on semantic segmentation and point clouds reconstruction

Rigid manipulators are applicable for a very structured environment and standard applications. For real world applications, continuum manipulators are used which has required high degrees of freedom, and compliance. The current work focus on the trajectory optimization of continuum robot for a specified application to minimize energy usage. To achieve this task Lagrangian mechanics is used to develop the mathematical model of the continuum robot with the payload. In this case the trajectory optimization has been carried out by treating the problem as a nested optimization problem. The outer optimization task is to optimize the trajectory using minimization of input force as primary goal where initial and final configurations of the arm are already available. Here, Genetic Algorithm is used as the optimizer for the selected tasks. The purpose of inner optimization loop is to find the feasible inverse solution for the manipulator that is required to calculate input forces which is further required to optimize the trajectory of the arm. A constrained non-linear optimization algorithm is used for the task. The optimization results show 30-80 % decrease in the input force required for the specified trajectories of the arm. The current paper shows that various tasks can be optimized using the formulated strategy to save the energy required by the arm to execute specified task.