Trajectory Execution Research Articles

Characterizing Performance Tradeoffs from Air Traffic Management Services for Advanced Air Mobility

The emergence of Advanced Air Mobility (AAM) has called for novel Air Traffic Management (ATM) solutions to enable the projected high number of operations, particularly in already dense and complex metroplex airspace. Understanding the multidimensional performance impacts of developing air traffic rules, procedures, and services is key to guiding this new traffic management system design. This paper presents a simulation framework to provide a quantitative assessment of the operational performance of ATM services in support of AAM. The framework embeds several modules for high-fidelity modeling of urban air traffic flows based on actual data, ATM service provision, stochastic simulation, and performance quantification. Two key services are analyzed: airspace geofencing and strategic deconfliction. We evaluate the use of these services in a scenario of air mobility operations associated with daily urban commuting in the Sao Paulo metropolitan region. Monte Carlo experiments are performed to simulate the execution of aircraft trajectories managed under different ATM service configurations, allowing for the characterization of operational performance tradeoffs, particularly in terms of safety and efficiency. The work contributes to quantifying the value of novel ATM services for integrating AAM operations in dense metroplex airspace, supporting their design and implementation.

Design of a two‐degrees‐of‐freedom robotic arm for monitoring applications for teaching robotics and artificial intelligence technology

AbstractThis manuscript proposes a methodology for teaching undergraduate engineering students fundamental concepts of artificial intelligence and robotics applicable in Industry 4.0 using a project‐based learning strategy. A low‐cost prototype of a two‐degrees‐of‐freedom robotic arm was designed and implemented for monitoring applications, integrating the Internet of Things and artificial intelligence technology. Inverse and direct kinematics are applied in the robotic arm design, enabling the execution of desired trajectories by the robot's end effector. The robot, capable of following user‐programmed trajectories, uses two servo motors with a 180° mobility range, integrated into a 3D printed structure made from polylactic materials, whereas the programmable logic was accomplished using an ESP32 microcontroller. Furthermore, the robot is controlled through a MATLAB GUI (Graphical User Interface), designed to obtain detailed process information such as activity status, trajectory type, and quality. The results demonstrate the feasibility of integrating various cutting‐edge technologies to teach fundamental concepts of Industry 4.0. The proposed methodology could allow educators to design a robotics course where students are motivated by practical experience implementing impactful technologies beyond the academic realm.

Expanding the wrench feasible workspace of quadrotor-based mobile cable-driven parallel manipulators using multi-objective optimization and machine learning

Quadrotor-based Reconfigurable Cable-Driven Parallel Manipulators (QRCDPMs) have previously been introduced as having significant potential in extending the feasible workspace of Cable-Driven Parallel Manipulators (CDPMs). However, such configurations have limitations in executing positive Z-direction trajectories resulting in non-optimal workspace and performance. This study proposes a novel Quadrotor-based Enhanced Reconfiguration Cable-Driven Parallel Manipulator (QERCDPM) that significantly enhances the Wrench Feasible Workspace (WFW). This allows for extended safe platform operation along all XYZ directions by adjusting the bottom cable exit point. Simulation results indicate that the proposed setup achieves a 96.39 % ratio of safe workspace to available workspace in various cable exit points configurations. Tension Distribution analysis for different trajectories demonstrates the proposed setup's superior cable tension management, increased flexibility and reachability. A Random Forest based Particle Swarm Optimization - Modified Frequency Bat Algorithm (RFPSOMFB) optimization algorithm is proposed, which outperforms existing methods in terms of run time and solution quality. The findings hold significance for applications requiring safe and efficient trajectory execution in dynamic and constrained environments, contributing to the evolution of multi-robot based intelligent and distributed systems.

Optimizing Lane Change Precision in Autonomous Vehicles: Smooth Trajectory Execution With MPC-Based State Estimation

Optimizing Lane Change Precision in Autonomous Vehicles: Smooth Trajectory Execution With MPC-Based State Estimation

Multiagent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots.

In this article, a novel hybrid multirobot motion planner that can be applied under no explicit communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multirobot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multiagent soft actor-critic (MASAC) algorithm under the centralized training with decentralized execution (CTDE) diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multigroup experimental results verify the effectiveness of the proposed motion planner.

Achieving Crash Fault Tolerance In Autonomous Vehicle Autopilot Software Stacks Through Safety-Critical Module Rejuvenation

Autonomous driving vehicles (ADV), have been in recent years, victims of their own success. Through their use of increasingly sophisticated sensor modalities and deep learning capabilities, ADVs have not only learned how to probe their chaotic environment with higher granularity coupled with smooth trajectory execution but also inherited all the vulnerabilities that were hiding behind these new features. Ensuring the safety of ADVs is crucial since a simple fault along their underlying autopilot software stack can lead to catastrophic accidents with the loss of human lives. Therefore we propose a crash-fault tolerant scheme that can be triggered whenever a crash fault of the safety critical submodules of the autopilot software stack is detected, which executes an emergency trajectory and effectively steers the car into a safe spot where the autopilot can be rejuvenated. We implement and evaluate the efficacy of this recovery scheme in the Apollo ADV software stack in conjunction with the SVL simulator. Keywords: autonomous driving vehicles, rejuvenation, crash fault tolerance, simplex architecture, apollo software stack.

Spherical acquisition trajectories for x-ray computed tomography with a robotic sample holder

This work presents methods for the seamless execution of arbitrary spherical trajectories with a seven-degree-of-freedom robotic arm as a sample holder. The sample holder is integrated into an existing x-ray computed tomography setup. We optimized the path planning and robot control algorithms for the seamless execution of spherical trajectories. A precision-manufactured sample holder part is attached to the robotic arm for the calibration procedure. Different designs of this part are tested and compared to each other for optimal coverage of trajectories and reconstruction image quality. We present experimental results with the robotic sample holder where a sample measurement on a spherical trajectory achieves improved reconstruction quality compared to a conventional circular trajectory. Our results demonstrate the superiority of the discussed system as it outperforms single-axis systems by reaching nearly 82% of all possible rotations. The proposed system is a step towards higher image reconstruction quality in flexible x-ray CT systems. It will enable reduced scan times and radiation dose exposure with task-specific trajectories in the future, as it can capture information from various sample angles.

A cell-type-specific error-correction signal in the posterior parietal cortex

Neurons in the posterior parietal cortex contribute to the execution of goal-directed navigation1 and other decision-making tasks2–4. Although molecular studies have catalogued more than 50 cortical cell types5, it remains unclear what distinct functions they have in this area. Here we identified a molecularly defined subset of somatostatin (Sst) inhibitory neurons that, in the mouse posterior parietal cortex, carry a cell-type-specific error-correction signal for navigation. We obtained repeatable experimental access to these cells using an adeno-associated virus in which gene expression is driven by an enhancer that functions specifically in a subset of Sst cells6. We found that during goal-directed navigation in a virtual environment, this subset of Sst neurons activates in a synchronous pattern that is distinct from the activity of surrounding neurons, including other Sst neurons. Using in vivo two-photon photostimulation and ex vivo paired patch-clamp recordings, we show that nearby cells of this Sst subtype excite each other through gap junctions, revealing a self-excitation circuit motif that contributes to the synchronous activity of this cell type. These cells selectively activate as mice execute course corrections for deviations in their virtual heading during navigation towards a reward location, for both self-induced and experimentally induced deviations. We propose that this subtype of Sst neurons provides a self-reinforcing and cell-type-specific error-correction signal in the posterior parietal cortex that may help with the execution and learning of accurate goal-directed navigation trajectories.

Speed-First: An Aggressive Gradient-Based Local Planner for Quadrotor Faster Flight

Autonomous flight for quadrotors is maturing with the development of real-time local trajectory planning. However, the current local planning method is too conservative to waste the agility of the quadrotors. So in this paper, we have focused on aggressive local trajectory planning and proposed a gradient-based planning method to rapidly plan faster executable trajectories while ensuring it is collision-free. A distance gradient information generation strategy is proposed, which finds a collision-free Hybrid-A* path to replace the control points in obstacles for safety and creates the distance gradient used in the back-end optimization. Besides, we present a novel and aggressive time span cost term to tackle unfeasibility and improve the overall trajectory speed. Extensive simulations and real-world experiments are tested to validate our method. The results show that our proposed method generates a more aggressive trajectory with a shorter planning time and a faster flight speed than the classical gradient-based method.

Execution of new trajectories toward a stable goal without a functional hippocampus.

The hippocampus is a critical component of a mammalian spatial navigation system, with the firing sequences of hippocampal place cells during sleep or immobility constituting a "replay" of an animal's past trajectories. A novel spatial navigation task recently revealed that such "replay" sequences of place fields can also prospectively map onto imminent new paths to a goal that occupies a stable location during each session. It was hypothesized that such "prospective replay" sequences may play a causal role in goal-directed navigation. In the present study, we query this putative causal role in finding only minimal effects of muscimol-induced inactivation of the dorsal and intermediate hippocampus on the same spatial navigation task. The concentration of muscimol used demonstrably inhibited hippocampal cell firing in vivo and caused a severe deficit in a hippocampal-dependent "episodic-like" spatial memory task in a watermaze. These findings call into question whether "prospective replay" of an imminent and direct path is actually necessary for its execution in certain navigational tasks.

An Evolutionary Learning Framework of Lane-Changing Control for Autonomous Vehicles at Freeway Off-Ramps

This paper proposes a lateral control strategy for autonomous vehicles (AVs) and develops an evolutionary learning framework for off-ramps. Random forest (RF) and back-propagation neural network (BPNN) integrated with model predictive control (MPC) algorithm are respectively used to capture the decision-making and trajectory characteristics during the lane-changing maneuver based on the Next Generation Simulation (NGSIM) dataset. Then, a running cost function is calculated to optimize the trajectory dataset. Finally, the numerical simulation is conducted to investigate the characteristics of the proposed framework. Simulation results indicate that the performance of our method is much better than some other methods in lane-changing gap choice and trajectory execution. Moreover, the traffic system controlled by the evolutionary algorithms reaches the highest capacity and safest level when all vehicles are equipped with cooperative adaptive cruise control (CACC) systems. On the contrary, the scenario with 50% CACC vehicles shows the lowest travel efficiency and the worst safety because of the CACC vehicles’ degradation. Furthermore, three iterations and 500 vehicle trajectories at each optimization cycle are recommended for the application in the off-ramp traffic control.

Combination of Dynamical Movement Primitives With Trajectory Segmentation and Node Mapping for Robot Machining Motion Learning

Movement primitive (MP) is proposed to model the robot skills with the idea of reusing basic behavior units. By learning the demonstrations, the MP can be modeled and utilized to generate similar motions to adapt to slightly changed scenarios. However, many kinds of MP models do not consider the geometric shape constraints and the trajectory execution accuracy, which will bring the shape distortion and contour error when task generalization. Traditional MP models are mainly studied for imitating human-like operating skills and have not been applied in industrial scenarios such as robot machining. In this article, a framework based on the combination of dynamical movement primitives (CDMP) with trajectory segmentation and node mapping methods is proposed to realize the imitation of robot machining motion. The demonstration machining trajectory is segmented first according to the kinematic features, and the shape features are extracted and stored by the node mapping policy. Then, the CDMP model is built for describing a complicated machining motion with many component DMPs. The parameters preferences associated with the model performance of CDMP are analyzed in the simulation. At last, a robot grinding experiment is conducted to show that the proposed framework can successfully imitate the machining motion with low geometric distortion, contour error, and computation cost, which also obtain a good surface quality and can significantly improve the intelligence level of robot machining.

MOGA 4WD: multi-objective genetic algorithm for four-wheel drive electrical vehicle torque distribution in challenging conditions

This article aims to present a strategy for multi-objective optimization based on torque distribution for electrical 4WD (four-wheel drive) vehicles. By considering applications on uneven terrain, common to the navigation of tractors, off-road vehicles, or even mobile robots, an algorithm is developed having as input the vehicle attitude and output the controlled torque on each actuated wheel. The main criterion adopted is to guarantee the execution of a stable trajectory. And, to avoid wheel slippage, which occurs when low torques are applied, as well as vehicle rollover, which can occur in the presence of high torques, it is necessary to use two objective functions. To find the Pareto optimal solutions, the simplified dynamic model of a vehicle is adopted, considering a quasi-static motion. For each vehicle, its electrical, mechanical, and geometric characteristics can be used as formulation constraints. From an optimization performed offline, and adopting a polynomial approximation-based approach for real-time application, simulations and experiments show an interesting behavior: solutions that go beyond allowing the ascent of simple ramps or the overcoming of smooth obstacles are found - it is possible, for example, to climb ramps with high slopes, taking the vehicle to the limit between stability and instability.

IWAAM: An automated system for monitoring and control of wire-arc additive manufacturing

In this work, a wire-arc additive manufacturing (WAAM) automated system named iWAAM is presented. iWAAM is an automated system for monitoring and control of WAAM depositions. The communication framework is based on ROS (Robot Operating System) and kuka_experimental package which allow the execution of predefined trajectories (ros_control) or online trajectory correction during the deposition. The power source control and configuration, and the deposition sequence are also embedded in the proposed framework. A Graphical User Interface (GUI), based on Qt, is used to monitor process variables, such as arc current, voltage, wire feed speed and travel speed. Furthermore, iWAAM controls the fabrication process of parts designed by external planners or by simple depositions using primitive trajectories (raster, zigzag, circular). In addition, a web interface is presented, allowing the users to monitor the process remotely. Experimental results of actual WAAM depositions illustrate the functionalities of the proposed automated system.

Toward Safer and Energy Efficient Global Trajectory Planning of Self-Guided Vehicles for Material Handling System in Dynamic Environment

For a sustainable operation of multiple Self-Guided Vehicles (SGVs) in a dynamic manufacturing environment, it is essential to guarantee collision-free and efficient navigation to the autonomous mobile platforms and safety to the surrounding subjects. To prevent from navigation failures, an SGV must avoid conflicts that constrain itself to abruptly brake or stop to avoid collisions. These inefficient conflicts result from unexpected changes in the configuration space or due to nearby unforeseen obstacle. In this paper, a navigation approach is proposed to adapt the global trajectory in order to reduce conflict occurrence while limiting energy consumption of the mobile platform. To generate such trajectory, first the collision risks are characterized using an objective risk perception parameter, the Time-To-Collision TTC, that rely on the kinematics of the egoSGV and the neighboring obstacles. Next, weighted Kernel Density Estimation (wKDE) defines the spatial distribution of conflict severity in configuration space. The defined zones are incorporated as a conflict layer in the global map. Then, a global trajectory planner algorithm is used to weigh between the length cost and conflict cost. Finally, to test the proposed solution, a simulation is performed in a factory-like environment, then an experiment is conducted with a real SGV. In comparison with the state-of-the-art geometrical path planning method, the results show that the proposed approach reduces navigation failures by up to 52%, while reducing the trajectory execution time by around up to 10 %. Also, the smoothness of the executed motion allowed to reduce energy consumption by over 12%.

Management of Business Projects of the Enterprise as a Factor of Increasing International Competitiveness in the Conditions of Global Sustainability

The article is devoted to the substantiation of the conceptual aspects of business project management, which is caused by the aggravation of international competition in the context of the establishment of sustainable development. The purpose of the study is to substantiate theoretical and methodological principles of the business projects management of enterprises, which will contribute to increasing international competitiveness in the conditions of global sustainability. The research used resource and system approaches, as well as the principles of purposefulness, interaction and efficiency of business project management. The functional dependence of the business project management is substantiated. Directions for improving the business projects management in the area of critical deviations of the trajectory of the project state are proposed. It has been proven that the randomness and uncontrollability of events form the variability of the expected value of the result of the business project management and expands the range of searching for the goals achievement with the deviation minimization of the project execution trajectory from the planned one. It has been justified that taking into account and increasing managerial, methodological, financial, production, scientific and innovative and marketing potentials contributes to increasing the competitiveness of enterprises in the international market in the conditions of global sustainability.

Multi-Error Location Method Based on Path Clustering and Failure Weighting

Since the traditional multiple-error locating method in software testing is difficult to achieve, and its information analysis is inaccurate, a software multiple-error locating method based on path clustering and failure weighting is proposed in this paper. In an environment with complete test cases, the program execution context information is dynamically captured by running test cases, and a path matrix of execution trajectory information is constructed. The cluster analysis is used to divide clusters and expand the weight of failed execution and added to suspicious in the process of degree evaluation to troubleshoot multiple errors. Experiments are implemented on four benchmark programs. The results show that compared with the five methods based on the equivalent evaluation function, the error location cost of the proposed method was reduced by 19.15% on average and effectively improved the efficiency of error location.

Phantom and Animal Study of a Robot-Assisted, CT-Guided Targeting System using Image-Only Navigation for Stereotactic Needle Insertion without Positional Sensors

PurposeTo evaluate the feasibility and accuracy of a robotic system to integrate and map computed tomography (CT) and robotic coordinates, followed by automatic trajectory execution by a robotic arm. The system was hypothesized to achieve a targeting error of <5 mm without significant influence from variations in angulation or depth. Materials and MethodsAn experimental study was conducted using a robotic system (Automated Needle Targeting device for CT [ANT-C]) for needle insertions into a phantom model on both moving patient table and moving gantry CT scanners. Eight spherical markers were registered as targets for 90 insertions at different trajectories. After a single ANT-C registration, the closed-loop software targeted multiple markers via the insertion of robotically aligned 18-gauge needles. Accuracy (distance from the needle tip to the target) was assessed by postinsertion CT scans. Similar procedures were repeated to guide 10 needle insertions into a porcine lung. A regression analysis was performed to test the effect of needle angulation and insertion depth on the accuracy of insertion. ResultsIn the phantom model, all needle insertions (median trajectory depth, 64.8 mm; range, 46.1–153 mm) were successfully performed in single attempts. The overall accuracy was 1.36 mm ± 0.53, which did not differ between the 2 types of CT scanners (1.39 mm ± 0.54 [moving patient table CT] vs 1.33 mm ± 0.52 [moving gantry CT]; P = .54) and was not significantly affected by the needle angulation and insertion depth. The accuracy for the porcine model was 9.09 mm ± 4.21. ConclusionsRobot-assisted needle insertion using the ANT-C robotic device was feasible and accurate for targeting multiple markers in a phantom model.

Strategic Execution Trajectories

ABSTRACT We obtain the optimal execution strategy for two sequential trades in the presence of a transient price impact. We first present a novel and general solution method for the case of a single trade (a metaorder) that is executed as a sequence of sub-trades (child orders). We then analyze the case of two sequential metaorders, including the case where the size and direction of the second metaorder are uncertain at the time the first metaorder is initiated. We obtain the optimal execution strategy under two different cost functions. First, we minimize the total cost when each metaorder is benchmarked to the price at its initiation, the total separate costs approach widely used by practitioners. Although simple, we show that optimizing total separate costs can lead to a significant understatement of the real costs of trading whilst also adversely impacting order scheduling. We overcome these issues by introducing a new cost function that splits the second metaorder into two parts, one that is predictable when the first metaorder is initiated and a residual that is not. The predictable and residual parts of the second metaorder are benchmarked using the initiation prices of the first and second metaorders, respectively. We prove existence of an optimal execution trajectory for linear instantaneous price impact and positive definite decay, and derive the explicit form of the minimizer in the special case of exponentially decaying impact, however uniqueness in general remains unproven. Various numerical examples are included for illustration.

Learning Minimum-Time Flight in Cluttered Environments

We tackle the problem of minimum-time flight for a quadrotor through a sequence of waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can plan minimum-time trajectories; yet, the trajectories are executed with control methods that do not account for obstacles. Thus, a successful execution of such trajectories is prone to errors due to model mismatch and in-flight disturbances. To this end, we leverage deep reinforcement learning and classical topological path planning to train robust neural-network controllers for minimum-time quadrotor flight in cluttered environments. The resulting neural network controller demonstrates substantially better performance of up to 19% over state-of-the-art methods. More importantly, the learned policy solves the planning and control problem simultaneously online to account for disturbances, thus achieving much higher robustness. As such, the presented method achieves 100% success rate of flying minimum-time policies without collision, while traditional planning and control approaches achieve only 40%. The proposed method is validated in both simulation and the real world, with quadrotor speeds of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$42 \,\mathrm{k}\mathrm{m}\mathrm{h}^{-1}$</tex-math></inline-formula> and accelerations of 3.6 g.