Robot Motion Planning Research Articles

Neural dynamics solver for time-dependent infinity-norm optimization based on ACP framework with robot application

In general, infinity-norm optimization (INO) is commonly deemed as a subset of nonlinear optimization. In the last decade, there have been relatively few reports on solving INO problems, specifically from the time-dependent aspect originating from the real-time motion planning of robots. Therefore, to compensate for the vacancy, this paper proposes a class of neural dynamics (ND) solvers utilizing an ACP framework that combines artificial systems (A), computational experiments (C), and parallel execution (P), respectively. Specifically, two improved ND solvers are constructed by exploiting simplified sign-bi-power and saturation activation functions for solving time-dependent INO (TDINO) problems subject to equality and inequality constraints. Moreover, the corresponding theoretical analysis and proof are conducted, which ensures that residual errors of the proposed improved ND solvers based on the ACP framework converge in a short time. Compared with a SOTA (state-of-the-art) zeroing neural network model presented recently, the average error of the proposed ones is reduced by 52%, the speed of error convergence is increased by 152%, and the transient state is extended by 4% on average. Finally, simulation results of an illustrative example and an application in the robot are provided, which illustrates the effectiveness, superiority, and feasibility of the proposed ND solvers.

A general method for the manipulability analysis of serial robot manipulators

Manipulability quantification and evaluation of robot manipulators plays an important role in robotic mechanism design, motion planning, and control. In this article, a general methodology of using manipulability polytopes to characterize the manipulator’s capability to generate velocities, forces, and accelerations at the end-effector is proposed. This methodology can be used to determine the kinematic, mechanical, and dynamic manipulability of both redundant and nonredundant serial manipulators, where influences of gravity, joint velocities and accelerations, and end-effector payload on the dynamic manipulability are incorporated into the derivation of the manipulability polytopes. Since the manipulability polytope is the feasible region defined by all the joint constraints, the proposed method is suitable for solving the problems including both symmetric and asymmetric joint constraints. Furthermore, a systematic architecture and detailed procedures for performance computation with respect to different types of manipulability of serial robot manipulators are presented. Two typical application examples, respectively, for two and three degree-of-freedom planar manipulators are given to illustrate the correctness and capability of the proposed methodology.

PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.

A Spatio-Temporal Prediction and Planning Framework for Proactive Human–Robot Collaboration

AbstractA significant challenge in human–robot collaboration (HRC) is coordinating robot and human motions. Discoordination can lead to production delays and human discomfort. Prior works seek coordination by planning robot paths that consider humans or their anticipated occupancy as static obstacles, making them nearsighted and prone to entrapment by human motion. This work presents the spatio-temporal avoidance of predictions-prediction and planning framework (STAP-PPF) to improve robot–human coordination in HRC. STAP-PPF predicts multi-step human motion sequences based on the locations of objects the human manipulates. STAP-PPF then proactively determines time-optimal robot paths considering predicted human motion and robot speed restrictions anticipated according to the ISO15066 speed and separation monitoring (SSM) mode. When executing robot paths, STAP-PPF continuously updates human motion predictions. In real-time, STAP-PPF warps the robot’s path to account for continuously updated human motion predictions and updated SSM effects to mitigate delays and human discomfort. Results show the STAP-PPF generates robot trajectories of shorter duration. STAP-PPF robot trajectories also adapted better to real-time human motion deviation. STAP-PPF robot trajectories also maintain greater robot/human separation throughout tasks requiring close human–robot interaction. Tests with an assembly sequence demonstrate STAP-PPF’s ability to predict multi-step human tasks and plan robot motions for the sequence. STAP-PPF also most accurately estimates robot trajectory durations, within 30% of actual, which can be used to adapt the robot sequencing to minimize disruption.

CARRT-Motion Capture Data for Robotic Human Upper Body Model.

In recent years, researchers have focused on analyzing humans' daily living activities to study various performance metrics that humans subconsciously optimize while performing a particular task. In order to recreate these motions in robotic structures based on the human model, researchers developed a framework for robot motion planning which is able to use various optimization methods to replicate similar motions demonstrated by humans. As part of this process, it will be necessary to record the motions data of the human body and the objects involved in order to provide all the essential information for motion planning. This paper aims to provide a dataset of human motion performing activities of daily living that consists of detailed and accurate human whole-body motion data collected using a Vicon motion capture system. The data have been utilized to generate a subject-specific full-body model within OpenSim. Additionally, it facilitated the computation of joint angles within the OpenSim framework, which can subsequently be applied to the subject-specific robotic model developed MATLAB framework. The dataset comprises nine daily living activities and eight Range of Motion activities performed by ten healthy participants and with two repetitions of each variation of one action, resulting in 340 demonstrations of all the actions. A whole-body human motion database is made available to the public at the Center for Assistive, Rehabilitation, and Robotics Technologies (CARRT)-Motion Capture Data for Robotic Human Upper Body Model, which consists of raw motion data in .c3d format, motion data in .trc format for the OpenSim model, as well as post-processed motion data for the MATLAB-based model.

Reinforcement learning in robotic motion planning by combined experience-based planning and self-imitation learning

High-quality and representative data is essential for both Imitation Learning (IL)- and Reinforcement Learning (RL)-based motion planning tasks. For real robots, it is challenging to collect enough qualified data either as demonstrations for IL or experiences for RL due to safety consideration in environments with obstacles. We target this challenge by proposing the self-imitation learning by planning plus (SILP+) algorithm, which efficiently embeds experience-based planning into the learning architecture to mitigate the data-collection problem. The planner generates demonstrations based on successfully visited states from the current RL policy, and the policy improves by learning from these demonstrations. In this way, we relieve the demand for human expert operators to collect demonstrations required by IL and improve the RL performance as well. Various experimental results shows that SILP+ achieves better training efficiency, higher and more stable success rate in complex motion planning tasks compared to several other methods. Extensive tests on physical robots illustrate the effectiveness of SILP+ in a physical setting, retaining a success rate of 90% where the next-best contender drops from 87% to 75% in the Sim2Real transition.

Motion planning and control strategy of a cable-driven body weight support gait training robot

Abstract. In this paper, a cable-driven body weight support gait training robot (C-BWSGTR) that provides patients with partial body weight support as well as a kind of stable gait training driving force was designed; this device enabled those patients to walk again. Firstly, the overall configuration of the C-BWSGTR was determined, and the structural composition and working principle of the robot were established. Secondly, the vector algebra method was applied to carry out the kinematic analysis and establish the mathematical model of the C-BWSGTR. The displacement of each cable during the patient gait training was also calculated. Thirdly, the motion planning of the C-BWSGTR was carried out in stages, using the time–phase distribution relationship based on an S-shaped speed curve. Meanwhile, the displacement, speed, and acceleration of each cable during the patient gait training were calculated and corresponding change curves were generated. Finally, a position servo composite control strategy for the C-BWSGTR was designed by analyzing the robot's dynamic characteristics of the forward channel transfer function. The simulation analysis and prototype experiment in this paper verified that the designed composite position servo control strategy can meet the requirements of the system with respect to stability and a fast response of the system to the loading command.

METHODS OF TASK AND MOTION PLANNING FOR ROBOTS: APPLICATIONS AND LIMITATIONS

Robots are increasingly required to perform more complex tasks, which in turn demand advanced planning algorithms. Task and Motion Planning (TAMP) methods, studied for decades, have made significant progress but still face various challenges. This document provides an overview of TAMP's development, encompassing problem-solving, simulation environments, methods, and remaining limitations. It particularly compares different simulation environments and methods used in various tasks, offering a practical guide and overview for beginners. Task planning is typically seen as planning in discrete spaces, while motion planning deals with continuous spaces. Significant progress has been made in integrating discrete and continuous planning methods to address TAMP problems. A recent survey has focused on TAMP integration, summarizing various methods for solving multimodal motion planning and TAMP problems. It introduces general concepts but primarily focuses on methods that operate in fully observable environments, which are far from real-world applications. Additionally, it demonstrates TAMP problem-solving in a theoretical manner that may not be user-friendly for beginners looking to apply these methods in practice. Therefore, this article aims to provide a practical and broader overview to readers, facilitating an easy entry into the field of TAMP for solving various tasks. Keywords: task and motion planning, simulation environment, learning methods, TAMP.

Task-oriented safety field for robot control in human-robot collaborative assembly based on residual learning

With the development of smart manufacturing, human-robot collaboration (HRC) is seen as the future of manufacturing. In a manufacturing environment with HRC, safety has been raising significant attention since the conventional separation of workspaces between robots and humans is removed. In this paper, a dynamic safety field is constructed that takes into account the collaborative assembly task requirements and the motions of both humans and robots. This safety field guides robot actions, ensuring that the robot carries out the required tasks while maintaining a safe distance from human workers. Based on the task requirements and safety considerations between humans and robots, a robot motion planning and control problem is formulated. To solve this problem, a hybrid RRL control scheme is proposed where a residual reinforcement learning (RRL) method is developed to combine the safety field-based control method with a deep reinforcement learning (DRL) method. Numerical studies are conducted to evaluate the performance of the proposed, which is compared with a pure deep Q-network (DQN) based method and a rapidly exploring random tree (RRT) method. The simulation results show that the proposed method can effectively optimize robot trajectories and outperforms the DQN-based and the RRT method in terms of computational efficiency.

Collision-free and smooth motion planning of dual-arm Cartesian robot based on B-spline representation

This paper discusses a new approach to motion planning of dual-arm gantry kinematic that solves the self-collision problem while ensuring G2-continuity and low curvatures. Starting from collision-free start and target points, the proposed method defines the geometric path of the end-effectors with classical five-segment planar trajectories, described by fifth-degree B-splines. The rototranslation of the loaded parts is evaluated in frames to define sampled overlapping curves, used to compute splines that act as deviation curves being added on the previously defined geometric paths in order to prevent collisions. Here, a quadratic programming minimization is employed to reduce the curvature of these spline and preserve the kinematics properties of the initial path. The kinematic constraints of the axes are ensured by iso-parametric trajectory planning, which results in an optimized motion profile to reduce the task execution time. Finally, the proposed approach is applied to a sorting system for laser machine (LST) having a dual-arm 7-d.o.f. gantry kinematic. The results of a case study of a typical sorting operation are presented and discussed to illustrate and clarify the method. The approach presented in this work can be applied to other robotic systems with similar kinematic structures, providing a useful tool for motion planning in pick-and-place applications.

A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

CMOR motion planning and accuracy control for heavy-duty robots

Abstract Factors like rising work costs and the imminent transformation and upgrading of manufacturing industries are driving the rapid development of the industrial robotics market. In this study, by analyzing the structure of the transport arm and China Fusion Engineering Test Reactor and performing mathematical modeling, a feasible solution for the robot can be obtained using the dynamic ant colony optimization algorithm and grayscale values. However, for multiple degree of freedom robots, due to a large number of joints, the pure use of joint angle restrictions cannot avoid their own mutual interference. The design of the transport arm robot’s own collision algorithm is shown, which focuses on each linkage as a rod wrapped by a cylinder. The experiment shows that the relationship between the integrated center of mass and the whole machine center of mass can get the action area of the whole machine center of mass of the robot, according to which the relationship between the radius of the catch circle and time of the projection area of the whole machine center of mass of the robot in the horizontal plane can be obtained. The maximum outer circle radius r com = 267.977 mm {r}_{\text{com}}=267.977\hspace{.25em}\text{mm} , according to the stability criterion r ssa > r con {r}_{\text{ssa}}\gt {r}_{\text{con}} , can be obtained, so the stability analysis of the gait switching process can be judged to be correct and effective.

Design and motion planning of a 7-DOF assembly robot with heavy load in spacecraft module

Currently, there is great demand for automated assembly of equipment inside spacecraft module, which would require an assembly robot with small size, large workspace, high load capacity, and high flexibility. To meet this demand, a redundant seven-degree-of-freedom (7-DOF) assembly robot with three prismatic joints and four rotating joints is proposed in this study. First, a kinematic model of the assembly robot is established, and the positional forward kinematics are derived. Then, the positional inverse kinematic equations are derived by using the fixed joint method and the coordinate inverse transformation method for the characteristics of the robot with redundant degrees of freedom. The inverse solution optimization model of the assembly robot is designed based on genetic algorithm, and B-spline interpolation algorithm is used for joint trajectory planning. Next, NSGA-2 algorithm is used to construct the trajectory optimization model, and the time-pulsation multi-objective Pareto optimal frontier is obtained. Finally, the joint simulation model of mechatronics and the prototype experiment are established to verify the effectiveness of the optimization results. Analysis shows that the proposed assembly robot has better load-bearing capacity and dynamic characteristics than similar existing assembly robots. Furthermore, the segmented trajectory optimization strategy for the assembly robot using inverse solution optimization, interpolation algorithm, and NSGA-2 based multi-objective optimization gets each motion joint to realize the trajectory tracking function quickly and stably, and the trajectory tracking error is small enough to meet the requirements of end positioning accuracy.

An Analytical Method for Sensitivity Analysis of Rigid Multibody System Dynamics Using Projective Geometric Algebra

Abstract The analytical sensitivity analysis, i.e., the analytical first-order partial derivatives of dynamical equations, is one key to improving descent-based optimization methods for motion planning and control of robots. This paper proposes an efficient algorithm that recursively evaluates the analytic gradient of the dynamical equations of a multibody system. The theory of projective geometric algebra (PGA) is used to generate the algorithm. It provides a systemic and geometrically intuitive interpretation for the multibody system dynamics, and the resulting algorithm is highly efficient, with concise formula. The algorithm is first applied to the open-chain system and extended for the cases when kinematic loops are contained. The runtime varying with respect to the degree-of-freedom (DOF) of the system is analyzed. The results are compared with that obtained from the algorithm based on spatial vector algebra (SVA) using open-source matlab codes. A 2DOF serial robot, a 3DOF robot with a kinematic loop and the PUMA560 robot are used for the validation of the minimum-effort motion planning, and it is verified that the proposed algorithm improves the efficiency.

Design and analysis of a continuum manipulator for use in narrow spaces

PurposeThis paper aims to present a kinematics analysis method and statics based control of the continuum robot with mortise and tenon joints to achieve better control performance of the robot.Design/methodology/approachThe kinematics model is derived by the geometric analysis method under the piecewise constant curvature assumption, and the workspace and dexterity of the proposed robot are analyzed to optimize its structure parameters. Moreover, the statics model is established by the principle of virtual work, which is used to analyze the mapping relationship between the bending deformation and the applied forces/torques. To improve the control accuracy of the robot, a model-based controller is put forward.FindingsResults of the experiments verify the feasibility of the proposed continuum structure and the correctness of the established model and the control method. The force deviation between the theoretical value and the actual value is relatively small, and the mean value of the deviation between the driving forces is only 0.46 N, which verify the established statics model and the controller.Originality/valueThe proposed model and motion controller can realize its accurate bending control with a few deviations, which can be used as the reference for the motion planning and dynamic model of the continuum robot.

Astrodynamics-Informed Kinodynamic Sampling-Based Motion Planning for Relative Spacecraft Motion

This paper presents a sampling-based spacecraft relative motion planning algorithm to reconfigure a spacecraft from a given initial state to a desired final state, subject to kinodynamic (simultaneous kinematic and dynamics) constraints. We leverage theoretical advancement in the field of sampling-based motion planning in robotics and extend their application to achieve motion planning in astrodynamics. We present an astrodynamics-informed kinodynamic motion planning (AIKMP) algorithm that takes advantage of a spacecraft’s natural motion to compute a safe, fuel-efficient motion plan for spacecraft reconfiguration in a cluttered environment. This algorithm is developed in the relative orbital element space of the deputy spacecraft around a chief spacecraft that provides very useful geometric insight to relative spacecraft motion, as well as helps in generating an overall smooth final transfer trajectory without demanding additional postprocessing of the transfer solution. Through several example scenarios, we demonstrate that the AIKMP algorithm is regularly able to compute safe and fuel-efficient solutions to relative transfer problems. We also show that this algorithm iteratively improves on its computed solutions and thus holds the capability of finding near-optimal transfer solutions given a sufficient number of algorithm iterations—without requiring an initial guess of the solution.

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.

An Optimized and Stable CF2 Mobile Robot Motion Planning Approach and Adaptation for Moving Targets

In this paper, the mobile robot motion planning approach (PSO-CF2-mt: PSO-CF2 for moving targets) is improved to track a moving target following a smooth path. PSO-CF2-mt was tested previously in various static and dynamic environments and proved its capacity to track moving targets whatever the form of the target’s trajectory. The problem with PSO-CF2-mt that we want to face in this paper is the stability of the robot’s motion. It is tackled by limiting the angular velocity of the robot. The angular speed limit is PSO selected and the variance of the angular speed is added to the fitness function as a third objective. This new version of PSO-CF2-mt is called OS-CF2-mt (Optimized Stable CF2-mt). Simulation results prove the capacity of OS-CF2-mt in ensuring stable travels for mobile robots following short, secure and smooth paths when tracking moving targets whether the environment is static or dynamic and whatever the form of the target’s trajectory.

Robot forming: Automated English wheel as an avenue for flexibility and repeatability

The English wheel is a highly flexible traditional metalworking tool. Currently, English wheeling is a manual manufacturing process that allows skilled craftsmen/smiths to form compound curves. The geometric accuracy and repeatability of the forming pieces are heavily influenced by human factors. Consequently, its applications in modern production industries are limited due to its mechanism. This paper presents the application of a single-robot automation system in the English wheeling process (i.e., robot forming). The automation system reads simulation-based toolpaths or camera-tracked trajectories, autonomously computes the end-effector trajectory and ensures efficient robotic end-effector motion planning by avoiding workspace constraints in operation setup. The automation system hardware (UR5e robotic arm) setup and software program structures developed are demonstrated. Experiments are conducted to compare the automation system performance against human performance when following the same toolpath data. It is found that the automation system is compatible with various types of trajectory data and the system effectively enhances the accuracy and repeatability of toolpath executions.

Motion Planning Using Hyperproperties for Time Window Temporal Logic

Hyperproperties are increasingly popular in verifying security policies and synthesis of control for dynamic systems. Hyperproperties generalize trace properties to enable reasoning about multiple computation traces that traditional trace properties cannot. Recent works show the effectiveness and prospect of Hyperproperties, specifically Hyperproperties for Linear Temporal Logic (HyperLTL), in optimality-, robustness-, and privacy-aware robotic motion planning. However, despite their rich expressiveness, HyperLTL cannot express tasks with time constraints. This paper presents HyperTWTL, which extends the compact semantics of Time Window Temporal Logic (TWTL) with explicit and concurrent quantification over multiple execution traces. We demonstrate that HyperTWTL can be used to formalize complex robotic planning objectives. Given HyperTWTL specifications, we also propose a symbolic approach for synthesizing optimality-, robustness-, and privacy-aware strategies by reducing the planning problem to a first-order logic satisfiability problem. The planning problem was then solved using two industrial-strength SMT solvers. The feasibility of HyperTWTL and the efficiency and scalability of the proposed strategy synthesis approach are demonstrated by formalizing important motion planning objectives of a surveillance mission case study and synthesizing the respective strategies using Z3 and CVC4 SMT solvers.