Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints

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

This paper presents a new method for generating motion profiles of rotating bodies, for example, the joint angle motion of mechanical systems such as space manipulators and antennas. In the planning of a motion from the given initial conditions to the final conditions, there are many constraints that should be considered, for example, actuator torque limits, rate limits, jerk limits, etc. Furthermore, flexibility of a system cannot be ignored in general. Vibration suppression is of great importance in some applications. For stationary boundary conditions (rest-to-rest cases), if the only constraint is the torque limit, a simple bang-bang type profile is the optimal one. However, if we consider more general boundary conditions, complex constraints, and vibration suppression conditions, the problem becomes considerably complicated. In this study, motion planning for a fixed motion time is considered. The control input or the acceleration of the motion is expressed as a linear superposition of triangle waves whose weight coefficients are determined in order to optimize the motion profile. In this formulation, all the boundary conditions and several constraints, including vibration suppression conditions, are expressed as linear equality and inequality constraints. Therefore, by appropriately setting the performance index, the problem becomes a linear programming problem and can be solved efficiently.

In the case that the total angular momentum conservation about the system center of mass, a torque-free spacecraft system with space manipulators becomes the nonholonomic control system. Due to the nonholonomic characteristic, the motion planning and control of a nonholonomic system is much more difficult than that of a conventional. In terms of the nonholonomic characteristic, the three-dimensional attitude control problem of the free floating space manipulators is investigated. The mathematical model of attitude control of rigid spacecraft with space manipulators is obtained. The control problem of the spacecraft attitude becomes a motion planning problem for a drift free control system. The control input rule is expressed by Fourier orthonormal basis and coefficient vector. Based on the optimal motion planning, the particle swarm optimization algorithm is proposed for free floating space manipulators. The results of numerical simulation indicate that this approach is effective for the motion planning problem of space manipulators.

Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot's proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution.

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.

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.

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.

It has been shown in recent robot planning control research that planning with simplified domain models is efficient and can be robust by detecting execution failures and replanning online. In this work, we alter the traditional HTN planning by interleaving with execution and extend it with a geometric planner for mobile manipulation tasks. We also build a memory-base which is comprised of several sub-memories. It can memory and manage mass patter-specific records, and supply data with the most efficient patter to the planner and various actuators. At the last, we present preliminary results on a mobile robot with manipulator and shows some advantage of IPAE when robot encounter uncertainty and need replanning, compared with the traditional HTN method.

Task and Motion Planning for Apple Harvesting Robot

Solutions to robotic manipulation problems can be substantially improved through integrated task and motion planning. Existing approaches typically focus on satisfaction, finding a feasible solution, instead of optimization. We formulate large-scale robotic manipulation problems as multi-level optimization, incorporating task, action, and motion planning. We develop an integrated planning approach for solving this optimization problem and generating a combined motion plan for a robot to optimize a task-level objective. This approach utilizes a combinatorial search algorithm for task planning and incrementally exploits information from lower-level optimization to improve the high-level task plan. Empirical results show that this integrated approach not only significantly outperforms a traditional top-down approach in solution quality, but also avoids infeasible lower-level motion plans.

We present a hybrid planning architecture that combines both Goal-Oriented Action Planning (GOAP) and Hierarchical Task Network (HTN) planning to avoid the enemies' predictable behaviors and dynamically adapt their actions according to the game-play context. The proposed approach is assessed and illustrated through a 3D custom brawler game where the player character fights invaders by controlling fluid material. Although the planning is computationally expensive, the first results show that our planner is efficient/fast and the game session has enjoyable playability.

In solving specific problems, physical laws and mathematical theorems directly express the connections between variables with equations/inequations. At times, it could be extremely hard or not viable to solve these equations/inequations directly. The PE (principle of equivalence) is a commonly applied pragmatic method across multiple fields. PE transforms the initial equations/inequations into simplified equivalent equations/inequations that are more manageable to solve, allowing researchers to achieve their objectives. The problem-solving process in many fields benefits from the use of PE. Recently, the ZE (Zhang equivalency) framework has surfaced as a promising approach for addressing time-dependent optimization problems. This ZEF (ZE framework) consolidates constraints at different tiers, demonstrating its capacity for the solving of time-dependent optimization problems. To broaden the application of ZEF in time-dependent optimization problems, specifically in the domain of motion planning for redundant manipulators, the authors systematically investigate the ZEF-I2I (ZEF of the inequation-to-inequation) type. The study concentrates on transforming constraints (i.e., joint constraints and obstacles avoidance depicted in different tiers) into consolidated constraints backed by rigorous mathematical derivations. The effectiveness and applicability of the ZEF-I2I are verified through two optimization motion planning schemes, which consolidate constraints in the velocity-tier and acceleration-tier. Schemes are required to accomplish the goal of repetitive motion planning within constraints. The firstly presented optimization motion planning schemes are then reformulated as two time-dependent quadratic programming problems. Simulative experiments conducted on the basis of a six-joint redundant manipulator confirm the outstanding effectiveness of the firstly presented ZEF-I2I in achieving the goal of motion planning within constraints.

Fault-tolerant motion planning and control of redundant manipulator

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 proposes a modeling method of criteria of skilled operators for motion planning of a redundant manipulator in industrial applications. The methodemploys Fuzzy-ID3 to extract important factors with mainties from the criteria and GMDH (group method of data handling) to model it to evaluate motion plans. Then, it applies a genetic algorithm to optimize mfundancy of a manipulator. The proposed method reduces the operator's labor and time for task teaching process thus a path, without considering redundant parameters, only needs to be &termin& Experimental results show the effectiveness of the proposed method.