Robot Motion Planning Research Articles

A Fractional-Order Gradient Neural Solution to Time-Variant Quadratic Programming With Application to Robot Motion Planning

A Fractional-Order Gradient Neural Solution to Time-Variant Quadratic Programming With Application to Robot Motion Planning

Robots in Partially Known and Unknown Environments: A Simulated Annealing Approach for Re-Planning

The path planning problem using mobile robots, also known as robot motion planning, is a key problem in robotics. The goal is to find a collision-free path from a starting point to a target point in an environment with obstacles. These obstacles can be known, partially unknown, or completely unknown to the robot. The robot’s decisions are different according to its degree of knowledge of the environment. If the environment is fully known, using an offline approach is sufficient. Otherwise, online techniques are required to find a path. They must be fast, effective, and adaptive to tackle the complexity of changing environments. In this work, we propose a framework based on a simulated annealing approach to solve the path planning problem with different degrees of knowledge of the environment for the robot. We evaluate our approach with a set of large-scale instances with different features. The results show that our framework can quickly find quality solutions and is also able to manage environmental changes.

Design and Motion Planning of a Cable Robot Utilizing Cable Slackness

Abstract Extending the workspace of cable robots can enhance their motion ability and help us make fuller use of their potential. This work designs a novel cable robot and utilizes cable slackness to extend its workspace. First, we use driving cables actuated by main motors and constrained cables driven by auxiliary motors equipped with lockers to control and restrain the motion of the end-effector, respectively. Then we classify the constraints on the end-effector into different modes according to the slackness of different constrained cables whose lengths can be changed by auxiliary motors, and continuous tension distribution (realized by tensioning devices) is applied to switch among them. To expand the mechanism’s workspace, the properties of the constraint mode series are analyzed and the static reachable workspace associated with it is defined, which guarantees the existence of feasible point-to-point trajectories. In particular, tree-based basic and general constraint mode series connection algorithms are designed to connect two targets in the static reachable workspace efficiently considering the necessary condition for the existence of the connection. After that, dual workspaces and subspace-based motion planning algorithms are designed to connect multiple targets in the static reachable workspace of the mechanism, and the quality of the trajectories is improved by using different manipulations of the constraint mode series. Since only driving cables are actuated by real-time capable servo motors and passively constrained cables are adopted to compensate for the weight of the end-effector, the size and the number of actuators and the energy consumption of the robot are reduced. Finally, the performance of the mechanism designed and the motion planning methods proposed are verified numerically under practical modeling errors regarding the cable elongation phenomenon.

Application of artificial intelligence in the new generation of underwater humanoid welding robots: a review

Underwater welding robots play a crucial role in addressing challenges such as low efficiency, suboptimal performance, and high risks associated with underwater welding operations. These robots face a dual challenge encompassing both hardware deployment and software algorithms. Recent years have seen significant interest in humanoid robots and artificial intelligence (AI) technologies, which hold promise as breakthrough solutions for advancing underwater welding capabilities. Firstly, this review delves into the hardware platforms envisioned for future underwater humanoid welding robots (UHWR), encompassing both underwater apparatus and terrestrial support equipment. Secondly, it provides an extensive overview of AI applications in underwater welding scenarios, particularly focusing on their implementation in UHWR. This includes detailed discussions on multi-sensor calibration, vision-based three-dimensional (3D) reconstruction, extraction of weld features, decision-making for weld repairs, robot trajectory planning, and motion planning for dual-arm robots. Through comparative analysis within the text, it becomes evident that AI significantly enhances capabilities such as underwater multi-sensor calibration, vision-based 3D reconstruction, and weld feature extraction. Moreover, AI shows substantial potential in tasks like underwater image enhancement, decision-making processes, robot trajectory planning, and dual-arm robot motion planning. Looking ahead, the development trajectory for AI in UHWR emphasizes multifunctional models, edge computing in compact models, and advanced decision-making technologies in expansive models.

Motion planning for a quadruped robot in heat transfer tube inspection

Steam generators (SGs) are essential in nuclear power facilities and require regular inspection to maintain their safety and operational effectiveness. This paper presents a quadruped robot designed to inspect SG heat-transfer tubes. The point-to-point crawling-motion planning problem of the robot is addressed by integrating an improved A* algorithm with an offline motion-posture library established for the tube-sheet environment. The planner can provide the global path, the step size for each crawling cycle, and the strategic placement of footholds. The proposed method can facilitate the robot in navigating obstacles on a tube sheet, seamlessly adapt to various postures and step lengths, and eliminate the necessity for turning or superfluous posture adjustments, thereby ensuring crawling efficiency. The efficacy of the proposed planner is validated rigorously through simulations and experimental trials using an actual SG tube sheet.

Incorporating shape dependent power law in motion planning for drawing robots

Incorporating human natural features into the algorithmic functions of robots can enhance performance efficiency. One of the most popular features of human movements is the power law that defines the connection between movement speed and path curvature. To contribute to this area, by discussing how the power law can provide a reasonable balance between velocity and efficiency, we propose a novel method to design motion profiles based on the power law. The novelty of this solution lies in the adjustment approach for the power law. In this work, inspired by features of human hand movements, the overall curvature of non-circular shapes is considered as the shape-dependent criterion for motion planning. Also, a framework to apply the proposed approach to any open non-circular curvy contours is presented. To investigate the efficiency of the approach, we considered a simple drawing robot. The simulation and experimental results verify the efficacy of the proposed motion planning method.

Motion planning of free-floating space robots for tracking tumbling targets by two-axis matching via reinforcement learning

The increasing amount of space debris poses a persistent threat to the safety of spacecraft. These non-cooperative targets are generally in a tumbling state, which places stringent requirements on the precision of the end-effector pose for capture tasks. Moreover, there are still difficulties in exploring the orientation state space, making it impractical to track targets with complex tumbling states based on reinforcement learning. To track tumbling targets quickly with high accuracy, we propose Two-Axis Matching Path Tracking (TMPT) algorithm. Our algorithm reduces the complexity of orientation exploration and improves the convergence speed through two-axis matching and the self-guidance module. Furthermore, the training environment has been redesigned by adopting staged tumbling target environment, which expands the range of trackable targets. Comparative simulation results demonstrate the efficiency and robustness of the algorithm.

Causal deconfounding deep reinforcement learning for mobile robot motion planning

Deep reinforcement learning (DRL) has emerged as an efficient approach for motion planning in mobile robot systems. It leverages the offline training process to enhance real-time computation efficiency. In DRL-based methods, the DRL models are trained to compute an action based on the current state of the robot and the surrounding obstacles. However, the trained models may capture spurious correlations through potential confounders, resulting in non-robust state representations, which limits the models’ robustness and generalizability. In this paper, we propose a Causal Deconfounding DRL method for Motion Planning, CD-DRL-MP, to address spurious correlations and learn robust and generalizable policies. Specifically, we formalize the temporal causal relationships between states and actions using a structural causal model. We then extract the minimal sufficient state representation set by blocking the backdoor paths in the causal model. Finally, using the representation set, CD-DRL-MP learns the causal effect between states and actions while mitigating the detrimental influence of potential confounders and computes motion commands for mobile robots. Comprehensive experiments show that the proposed method significantly outperforms non-causal DRL methods and existing causal methods, while guaranteeing good robustness and generalizability.

Optimizing Redundant Robot Kinematics and Motion Planning via Advanced D-H Analysis and Enhanced Artificial Potential Fields

Optimizing Redundant Robot Kinematics and Motion Planning via Advanced D-H Analysis and Enhanced Artificial Potential Fields

A novel planner framework compatible with various end-effector constraints

Purpose Aiming at the problem of insufficient adaptability of robot motion planners under the diversity of end-effector constraints, this paper proposes Transformation Cross-sampling Framework (TC-Framework) that enables the planner to adapt to different end-effector constraints. Design/methodology/approach This work presents a standard constraint methodology for representing end-effector constraints as a collection of constraint primitives. The constraint primitives are merged sequentially into the planner, and a unified constraint input interface and constraint module are added to the standard sampling-based planner framework. This approach enables the realization of a generic planner framework that avoids the need to build separate planners for different end-effector constraints. Findings Simulation tests have demonstrated that the planner based on TC-framework can adapt to various end-effector constraints. Physical experiments have also confirmed that the framework can be used in real robotic systems to perform autonomous operational tasks. The framework’s strong compatibility with constraints allows for generalization to other tasks without modifying the scheduler, significantly reducing the difficulty of robot deployment in task-diverse scenarios. Originality/value This paper proposes a unified constraint method based on constraint primitives to enhance the sampling-based planner. The planner can now adapt to different end effector constraints by opening up the input interface for constraints. A series of simulation tests were conducted to evaluate the TC-Framework-based planner, which demonstrated its ability to adapt to various end-effector constraints. Tests on a physical experimental system show that the framework allows the robot to perform various operational tasks without requiring modifications to the planner. This enhances the value of robots for applications in fields with diverse tasks.

Robust motion planning for mobile robots under attacks against obstacle localization

AbstractThanks to its real-time computation efficiency, deep reinforcement learning (DRL) has been widely applied in motion planning for mobile robots. In DRL-based methods, a DRL model computes an action for a robot based on the states of its surrounding obstacles, including other robots that may communicate with it. These methods always assume that the environment is attack-free and the obtained obstacles’ states are reliable. However, in the real world, a robot may suffer from obstacle localization attacks (OLAs), such as sensor attacks, communication attacks, and remote-control attacks, which cause the robot to retrieve inaccurate positions of the surrounding obstacles. In this paper, we propose a robust motion planning method ObsGAN-DRL, integrating a generative adversarial network (GAN) into DRL models to mitigate OLAs in the environment. First, ObsGAN-DRL learns a generator based on the GAN model to compute the approximation of obstacles’ accurate positions in benign and attack scenarios. Therefore, no detectors are required for ObsGAN-DRL. Second, by using the approximation positions of the surrounding obstacles, ObsGAN-DRL can leverage the state-of-the-art DRL methods to compute collision-free motion commands (e.g., velocity) efficiently. Comprehensive experiments show that ObsGAN-DRL can mitigate OLAs effectively and guarantee safety. We also demonstrate the generalization of ObsGAN-DRL.

Cops and robbers game and applications of its variant

Pursuit-evasion game is a game with a pursuer player and an evader player, it has numerous applications including artificial intelligence, robot motion planning and so on. The cops and robbers game is a type of pursuit-evasion game played on graphs, and its variant is a very fruitful research area in graph theory. The purpose of this paper is to find the specific relations between pursuit-evasion game applications and different types of variants of the cops and robbers game. The research method is to find variants with different game rules and winning strategies and classify them. Then compare these rules and strategies with the applications of pursuit-evasion games to find their relationships. The comparison result shows that there are many differences between the variants and the applications, which lead to their inability to be directly related. The application of the pursuit-evasion game is mainly based on route planning and object distance, and generally contains multiple variant types, which is different from the current research direction of variant.

Interactive Path Editing and Simulation System for Motion Planning and Control of a Collaborative Robot

Robots in hazardous environments demand precise and advanced motion control, making extensive simulations crucial for verifying the safety of motion planning. This paper presents a simulation system that enables interactive path editing, allowing for motion planning in a simulated collaborative robot environment and its real-world application. The system includes a simulation host, a control board, and a robot. Unity 3D on a Windows platform provides the simulation environment, while a virtual Linux environment runs ROS2 for execution. Unity sends edited motion paths to ROS2 using the Unity ROS TCP Connector package. The ROS2 MoveIt framework generates trajectories, which are synchronized back to Unity for simulation and real-world validation. To control the six-axis Indy7 collaborative robot, we used the MIO5272 embedded board as an EtherCAT master. Verified trajectories are sent to the target board, synchronizing the robot with the simulation in position and speed. Data are relayed from the host to the MIO5272 using ROS2 and the Data Distribution Service (DDS) to control the robot via EtherCAT communication. The system enables direct simulation and control of various trajectories for robots in hazardous environments. It represents a major advancement by providing safe and optimized trajectories through efficient motion planning and repeated simulations, offering a clear improvement over traditional time-consuming and error-prone teach pendant methods.

Locomotion gait control of snake robots based on a novel unified CPG network model composed of Hopf oscillators

Snake robots with limbless structure and rich locomotion gaits have been designed and built for wide application in various fields including military reconnaissance, pipeline operation, disaster search and rescue, etc. However, the problem how to flexibly and smoothly control switch and change of different locomotion gaits is still facing enormous challenges. A novel unified design rule of the CPG network model composed of improved Hopf oscillators is proposed, based on which a variety of different network structures can be created by designing connection distances and coupling weights among all oscillator units. Through the relationships between the control parameters of the Hopf oscillator, decoupling of the bifurcation parameters is achieved to solve inconsistent output waveform amplitude when the bifurcation parameters are not completely equal. Furthermore, five typical movement modes of biological snake are designed and smooth switch between different locomotion gaits is realized. A control system is constructed based on the Robot Operating System (ROS) and a prototype of snake robot is built, and the effectiveness of the proposed CPG model in controlling locomotion gaits was verified through simulations and experiments. The CPG modeling approach has important theoretical significance and practical instructive value for motion planning and gait control of snake robots in complex environments.

Robot Motion Planning Based on an Adaptive Slime Mold Algorithm and Motion Constraints

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time.

A CLOUD COMPUTING PARADIGM IN ROBOT MOTION PLANNING: INNOVATIONS AND PROSPECTS

The paper presents a new approach to the simulation of robot motion planning using cloud computing technologies. Robot motion planning plays a crucial role in robotics and enables robots to efficiently control and perform tasks in various environments. Using cloud computing, the proposed simulation framework offers significant advantages in terms of scalability, resource utilization, and availability. The paper begins with an overview of the potential applications of cloud computing in robot motion planning and simulation tasks. It then presents the architecture and components of a cloud-based simulation framework, highlighting its advantages over traditional methods. Overall, this paper provides valuable insights into the integration of cloud technologies into robotics, paving the way for more efficient and scalable simulation solutions.

Fast and precise approximation of Minkowski sum of two rotational ellipsoids with a superellipsoid

In this paper, we propose a fast and accurate method for approximating the Minkowski sum of two rotational ellipsoids with a superellipsoid. The Minkowski sum is used in a variety of applications such as robot motion planning and particle flow simulation requiring collision detection. Many of them are computed based on Minkowski sum, whose accuracy and processing time depend on how the mesh is created. We approximate Minkowski sum with a superellipsoid function. The superellipsoid has various shapes with the value of the exponents, and the computation of the parameters including the exponents is an algebraic computation with the time complexity O(1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{O}(1)$$\\end{document}. As a result, approximation error is small and computation is fast in all combinations of rotational ellipsoids. In addition, collision is quickly detected by solving the inequality of superellipsoid.

Quadruped Robot Control: An Approach Using Body Planar Motion Control, Legs Impedance Control and Bézier Curves.

In robotics, the ability of quadruped robots to perform tasks in industrial, mining, and disaster environments has already been demonstrated. To ensure the safe execution of tasks by the robot, meticulous planning of its foot placements and precise leg control are crucial. Traditional motion planning and control methods for quadruped robots often rely on complex models of both the robot itself and its surrounding environment. Establishing these models can be challenging due to their nonlinear nature, often entailing significant computational resources. However, a more simplified approach exists that focuses on the kinematic model of the robot's floating base for motion planning. This streamlined method is easier to implement but also adaptable to simpler hardware configurations. Moreover, integrating impedance control into the leg movements proves advantageous, particularly when traversing uneven terrain. This article presents a novel approach in which a quadruped robot employs impedance control for each leg. It utilizes sixth-degree Bézier curves to generate reference trajectories derived from leg velocities within a planar kinematic model for body control. This scheme effectively guides the robot along predefined paths. The proposed control strategy is implemented using the Robot Operating System (ROS) and is validated through simulations and physical experiments on the Go1 robot. The results of these tests demonstrate the effectiveness of the control strategy, enabling the robot to track reference trajectories while showing stable walking and trotting gaits.

A Novel Foot Placement Planning of Biped Robots Based on A* Algorithm and Kinematic Manipulability

In motion planning of biped robots, the place where the swing foot lands on the ground is crucial. Stability, energy consumption, smoothness of movement as well as naturalness of motion can be depending on the selected foot placement. In this paper, a novel method based on the A* algorithm and kinematic manipulability for foot placement planning is presented. In this method, by calculating the magnitude and direction of the maximum manipulability for the candidate foot placements, one is selected that leads to the most dexterous and agile walking while maintaining stability. The advantage of this approach is avoiding singularities and being ready to create a higher potential for foot velocities. Therefore, the possibility of performing maneuvers to maintain walking stability would be enhanced. The mentioned algorithm has been successfully implemented on a simulated three-dimensional biped robot.

Novel Probabilistic Collision Detection for Manipulator Motion Planning Using HNSW

Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method.