Dynamic Motion Planning Research Articles

Freeway merging trajectory prediction for automated vehicles using naturalistic driving data

Due to differences in speed and the complex interaction between merging and through-lane vehicles at freeway merging sections, crashes involving both human drivers and automated vehicles (AVs) persist. To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning, researchers have focused on developing trajectory prediction algorithms. Few studies, however, have developed merging trajectory prediction models using naturalistic driving data in China, making it urgent to put it on the agenda for AVs’ safety and efficiency at freeway merging sections. Based on merging periods extracted from the Shanghai Naturalistic Driving Study (SH-NDS), this study compares merging behavior on freeways with through-lane speed limits of 80 km/h, 100 km/h, and 120 km/h using analysis of variance (ANOVA). Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network (BPNN) and long short-term memory neural network (LSTM NN) approaches. Results show 1) significant differences among the three cases in all merging behavior variables except for longitudinal gap; and that 2) the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTM NN. Two major contributions to the safe operation of AVs are provided: 1) the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions, and assist ongoing motion planning strategies for AVs; and 2) the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.

Optimization-based dynamic motion planning and control for quadruped robots

Optimization-based dynamic motion planning and control for quadruped robots

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

The objective was to investigate the impacts of the robot’s dynamic affective expressions in task-related scenarios on human–robot collaboration (HRC) and performance in human–robot collaborative assembly tasks in flexible manufacturing. A human–robot hybrid cell was developed to facilitate a human co-worker and a robot to collaborate to assemble a few parts into a final product. The collaborative robot was a humanoid manufacturing robot with the ability to display its affective states due to changes in task scenarios on its face. The assembly task was divided into several subtasks, and based on an optimization strategy, the subtasks were optimally allocated to the human and the robot. A computational model of the robot’s affective states was derived inspired by that of humans following the biomimetic approach, and an affect-based motion planning strategy for the robot was proposed to enable the robot to adjust its motions and behaviors with task situations and communicate (inform) the situations to the human co-worker through affective expressions. The HRC and the assembly performance for the affect-based motion planning were experimentally evaluated based on a comprehensive evaluation scheme and were compared with two alternative conditions: (i) motion planning that did not display affective states, and (ii) motion planning that displayed text messages instead of displaying affective states to communicate the situations to the human co-worker. The results clearly showed that the dynamic affect-based motion planning produced significantly better HRC and assembly performance than that produced by motion planning associated with the display of no affective states or text messages. The results encouraged employing manufacturing robots with dynamic affective expressions to collaborate with humans in flexible assembly in manufacturing to improve HRC and assembly performance.

Role Dynamic Assignment of Human–Robot Collaboration Based on Target Prediction and Fuzzy Inference

Aiming to solve the problem of role dynamic assignment in human-robot collaborative motion with unknown and changeable targets, a role dynamic assignment method based on target prediction and fuzzy inference is proposed. First, a human-robot collaborative motion framework based on role dynamic assignment is constructed. Then, based on the analysis of the human-robot collaborative motion with fixed role (HMFR), a human-robot collaborative motion target prediction module, a role dynamic assignment module, and a robot motion planning module are designed to realize the role dynamic assignment and motion planning of the robot. Finally, human-robot collaborative motion experiments based on role dynamic assignment are performed. Experimental results show that the proposed role dynamic assignment algorithm can effectively implement role dynamic assignments. Compared with the human-robot collaborative motion with fixed roles, the human-robot collaborative motion based on role dynamic assignment has better overall performance in terms of actual path and desired path similarity (Fréchet distance) and participant workload.

Neural motion planning in dynamic environments*

Motion planning is a mature field within robotics with many successful solutions. Despite this, current state-of-the-art planners are still computationally heavy. To address this, recent work have employed ideas from machine learning, which have drastically reduced the computational cost once a planner has been trained. It is mainly static environments that have been studied in this way. We continue along the same research direction but expand the problem to include dynamic environments, hence increasing the difficulty of the problem. Analogously to previous work, we use imitation learning, where a planning policy is learnt from an expert planner in a supervised manner. Our main contribution is a planner mimicking an expert that considers the future movement of all the obstacles in the environment, which is key in order to learn a successful policy in dynamic environments. We illustrate this by evaluating our approach in a dynamic environment and by comparing our planner with a conventional planner that re-plans at every iteration, which is a common approach in dynamic motion planning. We observe that our approach yields a higher success rate, while also taking less time and accumulating less distance to reach the goal.

Hierarchical framework for mobile robots to effectively and autonomously explore unknown environments

Achieving efficient and safe autonomous exploration in unknown environments is an urgent challenge to be overcome in the field of robotics. Existing exploration methods based on random and greedy strategies cannot ensure that the robot moves to the unknown area as much as possible, and the exploration efficiency is not high. In addition, because the robot is located in an unknown environment, the robot cannot obtain enough information to process the surrounding environment and cannot guarantee absolute safety. To improve the efficiency and safety of exploring unknown environments, we propose an autonomous exploration motion planning framework that is divided into the exploration and obstacle avoidance levels. The two levels are independent and interconnected. The exploration level finds the optimal frontier target point in the global scope based on the forward filtering angle and cost function, attracting the robot to move to the unknown area as much as possible, and improving the exploration efficiency; the obstacle avoidance level establishes a scenario-speed conversion mechanism, and the target point and obstacle information are weighed to realise dynamic motion planning and completes obstacle avoidance control, and ensures the safety of exploration. Experiments in different simulation scenarios and real environments verify the superiority of the method. Results show that our method is superior to the existing methods.

Real-Time Motion Planning with Dynamic Obstacles

Robust robot motion planning in dynamic environments requires that actions be selected under real-time constraints. Existing heuristic search methods that can plan high-speed motions do not guarantee real-time performance in dynamic environments. Existing heuristic search methods for real-time planning in dynamic environments fail in the high-dimensional state space required to plan high-speed actions. In this paper, we present extensions to a leading planner for high-dimensional spaces, R*, that allow it to guarantee real-time performance, and extensions to a leading real-time planner, LSS-LRTA*, that allow it to succeed in dynamic motion planning. In an extensive empirical comparison, we show that the new methods are superior to the originals, providing new state-of-the-art search performance on this challenging problem.

Novel Lyapunov-Based Autonomous Controllers for Quadrotors

In this paper, we look into the dynamic motion planning and control of an unmanned aerial vehicle, namely, the quadrotor, governed by its dynamical equations. It is shown for the first time that the Direct or the Second Method of Lyapunov is an effective tool to derive a set of continuous nonlinear control laws that not only provide smooth trajectories from a designated initial position to a designated target, but also continuously minimise the roll and pitch of the quadrotor en route to its targets. The latter successfully addresses the challenging problem of a quadrotor autonomously transporting valuable and fragile payloads safely to the designated target. Computer simulations are used to illustrate the effectiveness of the proposed control laws.

A fast parameterized gait planning method for a lower-limb exoskeleton robot

In order to meet requirements of diverse activities of exoskeleton robot in practical application, a dynamic motion planning system is proposed using a fast parameterized gait planning method in this article. This method can plan the required gait data by adaptively adjusting very few parameters according to different application requirements. The inverted pendulum model is used to ensure the sagittal stability of the robot in the planning process. And this article specifies the end location of robot and iterates the associated joint angles by inverse kinematics. The gait trajectories generated by the proposed method are applied to the lightweight lower-limb exoskeleton robot. The results demonstrate that the trajectories of gait can be online generated smoothly and correctly, meanwhile every variable step can be satisfied as expected.

Dynamic Motion Planning for Autonomous Wheeled Robot using Minimum Fuzzy Rule based Controller with Avoidance of Moving Obstacles

In this article, the Minimum Fuzzy Rule-Based (MFRB) sensor-actuator controller has designed for dynamic motion planning of a differential drive wheeled robot among the moving, non-moving obstacles and goal in two-dimensional environments. The ring of ultrasonic sensors and infrared sensors have been attached on the front side, left side, and right side of the wheeled robot, which detects the moving obstacles, as well as non-moving obstacles in any environment. This proposed MFRB sensor-actuator controller helps the wheeled robot to move safely in a different scenario. The onboard sensor interpretation data are fed as input to the MFRB controller, and the MFRB controller provides the Pulse Width Modulation (PWM) based wheel velocity commands to both the left and right motors of a wheeled robot. In the numerical simulation and experiment, we have taken one condition that the speed of wheels of the differential drive wheeled robot is at least more than or equal to the rate of the moving obstacles and the moving goal. The numerical simulations are performed through a MATLAB graphical user interface (GUI), and we have used the differential drive wheeled robot to conduct experiments. The presented numerical simulation and experimental results illustrate that the MFRB controller operated wheeled robot has successfully avoided the stationary and non- stationary obstacles in various scenarios.

Study on motion performance of robot-aided passive rehabilitation exercises using novel dynamic motion planning strategy

The motion rehabilitation training robot is developed to help patients with motion dysfunction recover their motor function by providing a large amount of repetitive robot-aided exercise. To achieve stable and smooth robot-aided exercises for stroke patients, a motion control method with a novel dynamic motion planning strategy is proposed. The physical state of the training limb is assessed real time during the rehabilitation exercises. The dynamic motion planning strategy is developed by employing a suitable interpolation method dynamically corresponding to the physical state of the training limb to plan a trajectory tracking system that completely utilizes different interpolation characteristics to manage the movement in accordance with the time-varying physical state of the training limb. Concurrently, a position-based impedance control is adopted to achieve compliant movement. Functional (quantitative and qualitative) and clinical experiments are conducted on a four-degree-of-freedom whole-arm manipulator upper limb rehabilitation robot to verify the effectiveness of the control method designed with the dynamic motion planning strategy. The results indicate that the proposed control strategy can exhibit better performances in terms of the stability and smoothness.

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

The transformation of today’s manufacturing lines into truly adaptive systems facilitating individualized mass production requires new approaches for the efficient integration, configuration and control of robotics and automation components. Recently, various types of Plug-and-Produce architectures were proposed that support the discovery, integration and configuration of field devices, automation equipment or industrial robots during commissioning or even operation of manufacturing systems. However, in many of these approaches, the configuration possibilities are limited, which is a particular problem if robots operate in dynamic environments with constrained workspaces and exchangeable automation components as typically required for flexible manufacturing processes. In this article, we introduce an extended Plug-and-Produce concept based on dynamic motion planning, co-simulation and a collaborative human-robot interaction scheme that facilitates the quick adaptation of robotics behaviors in the context of a modular production system. To confirm our hypothesis on the efficiency and usability of this concept, we carried out a feasibility study where participants performed a flexible workcell setup. The results indicate that the assistance and features for planning effectively support the users in tasks of different complexity and that a quick adaption is indeed possible. Based on our observations, we identify further research challenges in the context of Plug, Plan and Produce applied to smart manufacturing.

Research on a mobile manipulator for biochemical sampling tasks

PurposeThe paper aims to present a tracked robot comprised of several biochemical sampling instruments and a universal control architecture. In addition, a dynamic motion planning strategy and autonomous modules in sampling tasks are designed and illustrated at length.Design/methodology/approachSeveral sampling instruments with position tolerance and sealing property are specifically developed, and a robotic operation system (ROS)-based universal control architecture is established. Then, based on the system, two typical problems in sampling tasks, i.e. arm motion planning in unknown environment and autonomous modules, are discussed, implemented and tested. Inspired by the idea of Gaussian process classification (GPC) and Gaussian process (GP) information entropy, three-dimensional (3D) geometric modeling and arm obstacle avoidance strategy are implemented and proven successfully. Moreover, autonomous modules during sampling process are discussed and realized.FindingsSmooth implementations of the two experiments justify the validity and extensibility of the robot control scheme. Furthermore, the former experiment proves the efficiency of arm obstacle avoidance strategy, while the later one demonstrates the time reduction and accuracy improvement in sampling tasks as the autonomous actions.Practical implicationsThe proposed control architecture can be applied to more mobile and industrial robots for its feasible and extensible scheme, and the utility function in arm path planning strategy can also be utilized for other information-driven exploration tasks.Originality/valueSeveral specific biochemical sampling instruments are presented in detail, while ROS and Moveit! are integrated into the system scheme, making the robot extensible, achievable and real-time. Based on the control scheme, an information-driven path planning algorithm and automation in sampling tasks are conceived and implemented.

A Two-Stage Trajectory Optimization Strategy for Articulated Bodies With Unscheduled Contact Sequences

In this letter, we propose a two-stage strategy for optimal control problems of robotic mechanical systems that proves to be more robust, and yet more efficient, than straightforward solution strategies. Specifically, we focus on a simplified humanoid model, represented as a two-dimensional articulated serial chain of rigid bodies, in the tasks of getting up (sitting down) from (to) the supine and prone postures. Interactions with the environment are integral parts of these motions, and a priori unscheduled contact sequences are discovered by the solver itself, opportunistically making or breaking contacts with the ground through feet, knees, hips, elbows, and hands. The present investigation analyzes the effects on the computational performance of: 1) the explicit introduction of contact forces among the optimization variables, 2) the substitution of undesired contact forces with geometric constraints that prevent interpenetrations, and 3) the splitting of the planning problem into two consecutive phases of increasing complexity. To the best of our knowledge, these tests represent the only quantitative analysis of the performances achievable with different solution strategies for optimization-based, whole-body dynamic motion planning in the presence of contacts.

Dynamic non-holonomic motion planning by means of dynamically consistent Jacobian inverse

Dynamic non-holonomic motion planning by means of dynamically consistent Jacobian inverse

A Spline-based Flexible Method of Virtual Force Design for Dynamic Motion Planning of Robots

ABSTRACTVirtual force approach is preferable for motion planning of mobile robots in dynamic environments. It composes virtual attractive forces to drive robots to destinations and virtual repulsive forces to steer robots away from neighbouring robots. However, most traditional methods use functions with limited controllable parameters, compromising design flexibility essential for smooth yet responsive robot motions.This paper proposes a spline-based method to enhance design flexibility of virtual forces that streamline robot motions. We take advantage of local controllability of interpolating cubic splines to generate desirable smooth virtual forces for robots. This merit facilitates responsive robot motions in dynamic situations. A case study of autonomous military robots is presented to validate the approach, in terms of enhanced motion safety and shortened operational time.

Dynamic Motion Planning and Adaptive Tracking Control for a Class of Two-Wheeled Autonomous Vehicle With an Underactuated Pendular Suspension

This paper investigates a dynamic motion planning approach and an adaptive tracking control scheme for a class of two-wheeled autonomous vehicle with an underactuated pendular suspension subject to nonholonomic constraint. Compared with the wheeled inverted pendulum system, this kind of two-wheeled pendular suspension (WPS) vehicle is more suitable for autonomous exploration in the complex unstructured environment. By Lagrange principle, a four-independent-coordinate dynamic model, which can describe the multivariate, nonlinear, and underactuated characteristics of the system, is first proposed. Besides, a reduced order dynamic is developed in the following so as to tackle the nonholonomic problem, and then the three-independent-coordinate reduced order dynamic is divided into an actuated part constituted by the rotational subsystem, and an underactuated part combined by the longitudinal and the swing angle subsystems. The sliding mode control (SMC) technique is utilized to construct the controller; especially, a composite sliding mode surface is proposed which can realize the velocity tracking and oscillation suppression for pendular suspension simultaneously. Furthermore, the adaptive mechanism is employed to update the true values of the inaccessible physical parameters which can enhance the adaptability of the WPS vehicle in unstructured environment. In addition, a dynamic motion planning method is presented, by aid of which the vehicle can track an arbitrary trajectory in Cartesian coordinate. The simulation results show the effectiveness and merits of the proposed control approaches.

Dynamic optimal payload path planning of mobile manipulators among moving obstacles

This paper is dealt with dynamic analysis of the wheeled mobile manipulators in the presence of moving obstacles considering optimal payload criterion. General dynamic formulation of the system was derived, and the moving obstacle avoidance strategy was proposed in terms of dynamic potential functions. The problem of dynamic motion planning and payload maximization was formulated using open-loop optimal control theory. Then, the indirect solution based on Pontryagin’s minimum principle was employed to solve the problem. Using the proposed method, complete nonlinear states and control constraints were treated without any simplifications such as linearizing the dynamics equations, discretizing the robot’s workspace, or parameterizing the solution. The proposed method will be useful for the system design and in the situation where the trajectories of obstacles are predefined. Finally, capability and applicability of the proposed method were investigated by the number of simulations on a two-link mobile manipulator.

Velocity-Change-Space-based dynamic motion planning for mobile robots navigation

This paper deals with the problem of dynamic motion planning in an unknown environment, where the workspace is cluttered with moving obstacles and robots. First, we give the principle of hybrid velocity obstacles, the definition of the preferred velocity and the collision-avoidance behavior. Second, we give new rules for the size regulation of obstacles and the kinematic and dynamic constraints of wheeled robot. Then, we establish a new Velocity Change Space (VCS) using the changes of the speed and direction of the robot׳s velocity as coordinate axis, and map the goal, velocity obstacles and dynamics constraints in this space. Finally, we explore the dynamic motion planning problem in the VCS. Mobile robot making motion planning in its velocity change window is achieved in multiple sensing-acting time steps, and directly gets the new velocity using point search and multi-objective optimization. We apply VCS-based motion planning methods to mobile robots, and simulation is used to illustrate the collision-free, interactive, un-conservative, foresighted and multi-objective optimized navigation of mobile robots.

Real-time heuristic search for motion planning with dynamic obstacles

Robust robot motion planning in dynamic environments requires that actions be selected under real-time constraints. Existing heuristic search methods that can plan high-speed motions do not guarantee real-time performance in dynamic envi- ronments. Existing heuristic search methods for real-time planning in dynamic environments fail in the high-dimensional state space required to plan high-speed actions. In this paper, we present extensions to a leading planner for high-dimensional spaces, R ∗ , that allow it to guarantee real-time performance, and extensions to a leading real-time planner, LSS-LRTA ∗ ,t h a ta l l o wi tt o succeed in dynamic motion planning. In an extensive empirical comparison, we show that the new methods are superior to the originals, providing new state-of-the-art heuristic search performance on this challenging problem.