Method For Motion Planning Research Articles

An iterative learning-based integrated motion planning and control method for autonomous patrolling of unmanned surface vehicles

Abstract The minimization of maritime patrol durations and the optimization of patrol trajectories are still a prevailing challenge for autonomous navigation of unmanned surface vehicles (USVs). In this paper, we propose an integrated trajectory planning and control method to achieve time-optimal autonomous patrol of USVs by a data-efficient iterative learning-based predictive control algorithm. We build the optimization problem of the algorithm by introducing a local cost and a local safe constraint set for closed-loop efficient data-driven learning. To guarantee recursive feasibility, stability and convergence properties of the optimization algorithm at each iteration, the local cost and the local constraint set are designed and update iteratively using the historical vehicle states at previous iterations as a dataset. Different from traditional optimization methods, the proposed method does not require a reference path. The cost function of the optimization method decreases monotonically and converge to obtain a time-optimal control law for trajectory planning and tracking of USVs only after several iterations. The proposed approach is validated on the robotic operation system in typical maritime patrol scenarios. The results show that the proposed method implement a 5% reduction of patrol time, fewer lateral track errors, and smoother trajectories compared to the traditional MPC algorithm under water flow conditions. In addition, Enhancements in patrol operation efficiency of USVs are achieved by the algorithm, even under the constraints of varied patrol paths, attesting to its versatility.

Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer.

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital-model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.

Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration.

This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system's observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles.

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.

Task and motion planning using mixed integer linear programming for solving fetch-and-carry tasks by a mobile manipulator

This manuscript describes a TAsk and Motion Planning (TAMP) method for mobile manipulators. We focus on fetch-and-carry tasks, and we aim to simultaneously generate both a sequence of actions and a motion sequence for each action. The number of factors to be considered in solving such a problem, and the interactions among them are complex due to the multifaceted characteristics of mobile manipulation. As a result, straightforwardly performing simultaneous TAMP may require a significant amount of processing time. Therefore, we reduce the complexity of the problem by formulating fetch-and-carry planning for a Mixed Integer Linear Programming (MILP) problem. The proposed method can obtain the sequence of actions and the movement of the mobile manipulator in less than one second in many cases. The effectiveness of the proposed method is verified in environments in which delivery objects and obstacles are placed in various patterns, using a robot with an omnidirectional mobile platform and a serial link manipulator.

Force manipulability-oriented manipulation planning for collaborative robot

PurposeCollaborative robots (cobots) are widely used in various manipulation tasks within complex industrial environments. However, the manipulation capabilities of cobot manipulation planning are reduced by task, environment and joint physical constraints, especially in terms of force performance. Existing motion planning methods need to be more effective in addressing these issues. To overcome these challenges, the authors propose a novel method named force manipulability-oriented manipulation planning (FMMP) for cobots.Design/methodology/approachThis method integrates force manipulability into a bidirectional sampling algorithm, thus planning a series of paths with high force manipulability while satisfying constraints. In this paper, the authors use the geometric properties of the force manipulability ellipsoid (FME) to determine appropriate manipulation configurations. First, the authors match the principal axes of FME with the task constraints at the robot’s end effector to determine manipulation poses, ensuring enhanced force generation in the desired direction. Next, the authors use the volume of FME as the cost function for the sampling algorithm, increasing force manipulability and avoiding kinematic singularities.FindingsThrough experimental comparisons with existing algorithms, the authors validate the effectiveness and superiority of the proposed method. The results demonstrate that the FMMP significantly improves the force performance of cobots under task, environmental and joint physical constraints.Originality/valueTo improve the force performance of manipulation planning, the FMMP introduces the FME into sampling-based path planning and comprehensively considers task, environment and joint physical constraints. The proposed method performs satisfactorily in experiments, including assembly and in situ measurement.

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.

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation.

Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

Motion planning and contact force distribution for heavy‐duty hexapod robots walking on unknown rugged terrains

AbstractHeavy‐duty hexapod robots have impressive stability, high load‐bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming‐based Contact Force Distribution (DQP‐based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods.

Hoverable structure transformation for multirotor UAVs with laterally actuated frame links

This paper presents motion planning strategies that assure stable hovering for a transformable unmanned aerial vehicle (UAV) throughout its transformation. The considered multirotor UAV consists of multiple links equipped with rotors. The UAV can transform its structure by changing the lateral joint angle connecting these links. We first introduce the concept of hoverability, which assures static hovering with a linear state feedback controller. Because the hoverability can be evaluated quantitatively by utilizing a convex hull yielded by the positions of rotors, we propose motion planning methods that leverage the hoverability to achieve transformation while guaranteeing stable hovering with desired margin. As the hoverability can be investigated geometrically without analyzing the dynamics of UAVs, the proposed method can be readily employed for a wide class of transformable UAVs, including the one in which clockwise and counterclockwise rotors are not arranged alternately. The proposed motion planning is designed to secure the specified threshold for hoverability and avoid interferences between links or rotors. We then present two types of costs for motion planning: one minimizes the transformation duration, and the other maximizes the hoverability during the transformation. The proposed motion planning is optimized via Dijkstra's algorithm and demonstrated in simulation.

A Random Path Sampling-Based Method for Motion Planning in Many Dimensions

In this paper, we present GRPMP (Gaussian Random Paths Motion Planner), a method for solving robot motion planning problems. Compared with traditional sampling-based methods which build large-scale random trees or roadmaps by sampling random nodes in configuration-spaces, GRPMP generates a set of smooth random paths discretized into sparse nodes, using a random path sampler based on Gaussian process, to reduce resource space occupation and improve planning success rate. Employing the high-quality path selecting strategy with the lazy collision checking technique, GRPMP can fast select the collision-free high-quality path that has the lowest cost in the set of smooth random paths, so as to improve the efficiency and quality of the motion planning. We present challenging experiments with GRPMP on pick-and-place tasks, using a humanoid robot, to show that GRPMP has the ability to find high-quality paths with efficiency and stability.

Model-based reinforcement learning for robot-based laser material processing

Articulated robotic arms in laser material processing require precise motion planning. Traditional motion planning methods face challenges in trajectory accuracy. This study demonstrates model-based reinforcement learning as an effective approach for motion planning of these robotic arms. The process involves training a neural network trajectory model based on Pilz Industrial Motion Planner, followed by training an agent to optimize motion by adjusting joint velocities.The study compares Proximal Policy Optimization and Soft Actor-Critic algorithms to the baseline Pilz motion plan. Results show that model-based reinforcement learning improves accuracy in x-direction, reducing mean absolute error to 1.75 × 10−3 m from 6.37 × 10−3 m. However, it slightly increases z-direction mean absolute error, from 6 × 10−6 m to 2.5 × 10−4 m. This leads to an increase in on-surface beam radius, from 2.9 × 10−5 m to 3.3 × 10−5 m, and decrease in peak intensity of 22.77 % compared to baseline.These results highlight reinforcement learning’s potential to enhance trajectory accuracy in motion planning, advancing robot-based laser material processing.

A Personalized Motion Planning Method with Driver Characteristics in Longitudinal and Lateral Directions

Humanlike driving is significant in improving the safety and comfort of automated vehicles. This paper proposes a personalized motion planning method with driver characteristics in longitudinal and lateral directions for highway automated driving. The motion planning is decoupled into path optimization and speed optimization under the framework of the Baidu Apollo EM motion planner. For modeling driver behavior in the longitudinal direction, a car-following model is developed and integrated into the speed optimizer based on a weight ratio hypothesis model of the objective functional, whose parameters are obtained by Bayesian optimization and leave-one-out cross validation using the driving data. For modeling driver behavior in the lateral direction, a Bayesian network (BN), which maps the physical states of the ego vehicle and surrounding vehicles and the lateral intentions of the surrounding vehicles to the driver’s lateral intentions, is built in an efficient and lightweight way using driving data. Further, a personalized reference trajectory decider is developed based on the BN, considering traffic regulations, the driver’s preference, and the costs of the trajectories. According to the actual traffic scenarios in the driving data, a simulation is constructed, and the results validate the human likeness of the proposed motion planning method.

Features and perspectives of application of the rapidly exploring random tree method for motion planning of autonomous robotic 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.

Energy-optimal motion planning of underwater gliders accounting for seabed topography and ocean currents

The sawtooth gliding profile of underwater gliders can be determined by motion parameters, including yaw angles, diving depths, and pitch angles. Planning a series of motion parameters can generate a 3D gliding path that helps underwater gliders adapt to ocean currents and seabed topography. This paper presents an offline motion planning method for underwater gliders. By optimizing each gliding profile’s motion parameters, i.e., yaw angle, diving depth, and pitch angle, the seabed topography and ocean currents can be taken into account to generate an energy-optimal 3D path. To solve the motion planning problem, we propose a well-designed encoding method and a piecewise fitness function, which are used with the JADE algorithm to determine the optimal motion parameters. Experimental results on real seabed topography demonstrate that our method significantly reduces energy consumption and has better adaptability to complex ocean environments than traditional underwater glider path planning methods.

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.

Near-Optimal Multi-Robot Motion Planning with Finite Sampling

An underlying structure in several sampling-based methods for continuous multirobot motion planning (MRMP) is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tensor roadmap</i> , which emerges from combining multiple probabilistic roadmap (PRM) graphs constructed for the individual robots via a tensor product. We study the conditions under which the tensor roadmap encodes a near-optimal solution for MRMP—satisfying these conditions implies near optimality for a variety of popular planners, including dRRT*, and the discrete methods M* and conflict-based search, when applied to the continuous domain. We develop the first finite-sample analysis of this kind, which specifies the number of samples, their deterministic distribution, and magnitude of the connection radii that should be used by each individual PRM graph, to guarantee near-optimality using the tensor roadmap. This significantly improves upon a previous asymptotic analysis, wherein the number of samples tends to infinity. Our new finite sample-size analysis supports guaranteed high-quality solutions in practice within finite time. To achieve our new result, we first develop a sampling scheme, which we call the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">staggered grid</i> , for finite-sample motion planning for individual robots, which requires significantly fewer samples than previous work. We then extend it to the much more involved MRMP setting, which requires to account for interactions among multiple robots. Finally, we report on a few experiments that serve as a verification of our theoretical findings and raise interesting questions for further investigation.

Model-Based Navigation Control for Communication-Aware Motion Using Harmonic Potential Fields

This paper suggests an optimal and provably-correct method for inducing communication-aware, context-sensitive mobility in an autonomous agent. The method can fuse crisp representations of arbitrarily shaped hard-obstacles with soft representations of the wireless signal strength field that forms within the confines of the hard-obstacles. The procedure can produce a navigation control signal able to induce in a wide class of realistic agents optimal, communication-aware, dynamically-friendly trajectories to a specified target point in the environment. The approach is developed with the aid of the potential-field method for motion planning. Its immediate goal is to tackle the model-based case where the navigator assumes full knowledge of the obstacles and Wireless Communication Link (WCL). The treatment in this part provides the theoretical foundations and the tools necessary to extend the method to the sensor-based case where the agent does not a-priori know the obstacles or the WCL which have to be sensed online. Theoretical proofs of the communication-aware navigator’s performance are provided and its behavior is verified using intensive realistic simulation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we target applications like search and rescue, where mobile agents may be utilized for information gathering and mapping purposes in regions that are hazardous to humans. Our proposed approach can simultaneously combine obstacle information and signal strength information to generate guidance signals. Trajectories manifested using these guidance signals can avoid regions with very low signal strength. Typically, guidance signals from a planner need to be smoothed before generating actuation signals. Our proposed planner provides guidance signal that can be directly used to generate actuation signals. Also the proposed approach results in trajectories that are very smooth (infinitely differentiable), therefore, energy consumption due to motion is reduced. Simulations show the effectiveness of our approach. In future work, we intend to use real-time signal strength and obstacle information data in out approach.

A safety-enhanced eco-driving strategy for connected and autonomous vehicles: A hierarchical and distributed framework

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs.

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.