Motion planning for fluid manipulation using simplified dynamics

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.

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.

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.

We describe some recent work on randomized motion planning algorithms and consider the problem of motion planning for systems with both kinematic and dynamic constraints. Such a problem is often referred to as kinodynamic motion planning. A rapidly-exploring random tree (RRT) is used for the motion planning of a blimp system, and some techniques for improving the performance of the planner are proposed. Based on a dynamic model of a blimp, dynamic constraints are introduced into the algorithm design and simulations are conducted for trajectory generation.

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.

One of the fundamental problems in the field of robotic motion planning is to safely and efficiently drive the end effector of a robotic manipulator to a specified goal position. Here, safety refers to the requirement that the robotic manipulator must have no collision with surrounding obstacles, and efficiency requires that some predefined cost function is minimized. In addition, kinematic and dynamic constraints have to be satisfied. These requirements lead to non-convex optimization problems, which may be approximated by mixed-integer linear programs (MILPs). The solution of the latter, however, is often intolerably complex due to a huge number of binary decision variables. In the present paper, we consider motion planning scenarios with polyhedral obstacles and velocity constraints for the joint positions of the robotic manipulator. We provide a geometric result whose application leads to MILPs with drastically reduced numbers of binary decision variables. Computational efficiency is demonstrated for two- and three-link manipulators interacting with obstacles, where the number of simplex steps during the MILP solution is reduced by a factor of roughly 200 over previous methods. We also demonstrate the application of the proposed method to an industrial robot.

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.

Recently, it is expected to develop an autonomous robot working in a living environment. Motion planning is very important for an autonomous mobile robot, but kinematic constraints and dynamic constraints are generally solved separately in conventional motion planning. In this research, a simpler way of deciding the control input for an autonomous mobile robot, which is described by a highly nonlinear model in a multi-dimensional space, is proposed by solving kinematic constraints and dynamic constraints simultaneously, under the kinodynamic motion planning based on a harmonic potential field (HPF). In this paper, a quadrotor that is very attractive as an aerial robot due to its high maneuverability, is assumed to be a controlled object, and it is aimed at guiding the quadrotor to an arbitrary target point while avoiding obstacles. Then, three types of controllers for kinodynamic motion planning based on an HPF, which were developed for point mass control, are extended to be applied to the quadrotor. The extended controllers are compared each other in trajectory, amplitude of attitude, and arrival time through some flight simulations. Moreover, multiple gains included in the proposed controllers are optimized automatically by using genetic algorithm (GA), in order to realize highly accurate control. Flight simulation in an untrained environment, which is different from the environment that the gains were selected by GA, is also conducted, and it is confirmed that the quadrotor can move onto the target point with the same gains while avoiding obstacles.

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.

This study proposes an innovative algorithm integrating kinematic modeling and intelligent optimization to address the motion planning of multi-segment cable-driven continuum manipulator. Under the piecewise constant curvature (PCC) hypothesis, a forward kinematic framework is formulated to define a structured mapping between joint-space parameters and task-space coordinates. Leveraging the established model, a refined APF strategy is implemented to synthesize collisionavoidance paths for the manipulator, while the Jacobian pseudo-inverse method is applied to obtain corresponding general joint angle solutions. Moreover, to enhance optimization efficiency, the initial population is generated by leveraging the least-squares solution as a baseline, with search boundaries dynamically defined through the null-space properties of the Jacobian pseudoinverse. Then the trajectory optimization is executed under dual constraints of end-effector pose and arm shape via the cheetah optimizer (CO). Numerical simulations validate the algorithm's capability to maintain trajectory tracking accuracy while accounting for end-effector pose, providing an effective solution for motion planning of multi-segment continuum manipulators.

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

In this article a fast and simple method for the motion planning of telerobotic manipulators is presented. It is based on solving numerically a linear system of equations that includes a simple null space vector for obstacle avoidance, and an efficient procedure for the appropriate damping of the pseudoinverse matrix. This approach enables the pursuance simultaneously, of both collision avoidance and singularities prevention on-line in a sensor based environment. These properties also make it suitable for entirely autonomous operations. >

Vision-Language-Action (VLA) models integrate visual perception, natural language understanding, and embodied control into a unified framework, enabling end-to-end task execution from multimodal instructions. While such models have demonstrated impressive generalization across tasks and environments, their direct outputsoften in the form of discrete action tokens or waypoint sequencesfrequently overlook key physical constraints, such as trajectory feasibility, collision avoidance, and dynamic consistency. This limitation hinders deployment in safety-critical and dynamic real-world settings. Integrating motion planning into VLA systems offers a principled solution, embedding geometric and dynamic constraints into the control pipeline to transform high-level semantic goals into safe, smooth, and executable trajectories. This work examines representative integration strategies alongside the trade-offs between discrete tokenized outputs and continuous control policies. Applications are analyzed highlighting performance gains in generalization, safety, and execution efficiency. A discussion of current challengessuch as the balance between planning speed and precision, and generalization across embodimentsis followed by prospective research directions, including continuous prediction with hierarchical control, low-resource edge deployment, and multi-robot collaborative planning. The study underscores motion planning as a critical enabler for reliable, adaptable, and scalable embodied intelligence.

Constrained motion planning of free-float dual-arm space manipulator via deep reinforcement learning