Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

Mitigation of signalized intersection collision risks with trajectory based dynamic dilemma zone protection

Dilemma zone is one of the major factors causing red-light violations, right-angled and rear-end crashes at signalized intersections. In this paper, a dilemma zone protection system is introduced, which employs a dynamic vehicular trajectory optimization approach to guide vehicles approaching a signalized intersection. Unlike conventional methods that aim to eliminate dilemma zones, this system adjusts the speed profiles of individual vehicles to shift the distribution of dilemma zones and prevent vehicles from becoming trapped. Extensive simulated experiments were conducted to test and validate the proposed system for both individual vehicles and platoons. Results demonstrate that the system offers superior protection for individual vehicles, with full coverage across various settings of initial speeds and distances to the stop line. In the traffic environment with realistic platooning settings, the proposed system significantly reduces the number of vehicles in the dilemma zone, resulting in improved operational and safety benefits such as reduced risks of hazardous maneuvers and savings in vehicular delay.

Multi-target trajectory planning and control technique for autonomous navigation of multiple robots

The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption.

Source-detector trajectory optimization for CBCT metal artifact reduction based on PICCS reconstruction

Precise instrument placement plays a critical role in all interventional procedures, especially percutaneous procedures such as needle biopsies, to achieve successful tumor targeting and increased diagnostic accuracy. C-arm cone beam computed tomography (CBCT) has the potential to precisely visualize the anatomy in direct vicinity of the needle and evaluate the adequacy of needle placement during the intervention, allowing for instantaneous adjustment in case of misplacement. However, even with the most advanced C-arm CBCT devices, it can be difficult to identify the exact needle position on CBCT images due to the strong metal artifacts around the needle. In this study, we proposed a framework for customized trajectory design in CBCT imaging based on Prior Image Constrained Compressed Sensing (PICCS) reconstruction with the goal of reducing metal artifacts in needle-based procedures. We proposed to optimize out-of-plane rotations in three-dimensional (3D) space and minimize projection views while reducing metal artifacts at specific volume of interests (VOIs). An anthropomorphic thorax phantom with a needle inserted inside and two tumor models as the imaging targets were used to validate the proposed approach. The performance of the proposed approach was also evaluated for CBCT imaging under kinematic constraints by simulating some collision areas on the geometry of the C-arm. We compared the result of optimized 3D trajectories using the PICCS algorithm and 20 projections with the result of a circular trajectory with sparse view using PICCS and Feldkamp, Davis, and Kress (FDK), both using 20 projections, and the circular FDK method with 313 projections. For imaging targets 1 and 2, the highest values of structural similarity index measure (SSIM) and universal quality index (UQI) between the reconstructed image from the optimized trajectories and the initial CBCT image at the VOI was calculated 0.7521, 0.7308 and 0.7308, 0.7248 respectively. These results significantly outperformed the FDK method (with 20 and 313 projections) and the PICCS method (20 projections) both using the circular trajectory. Our results showed that the proposed optimized trajectories not only significantly reduce metal artifacts but also suggest a dose reduction for needle-based CBCT interventions, considering the small number of projections used. Furthermore, our results showed that the optimized trajectories are compatible with spatially constrained situations and enable CBCT imaging under kinematic constraints when the standard circular trajectory is not feasible.

Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs' impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment.

Distributed multi-agent collision avoidance using robust differential game

This paper proposes a novel robust differential game scheme to solve the collision avoidance problem for networked multi-agent systems (MASs), subject to linear dynamics, external disturbances and limited observation capabilities. Compared with the existing differential game approaches only considering obstacle avoidance objectives, we explicitly incorporate the trajectory optimization target by penalizing the deviation from reference trajectories, based on the artificial potential field (APF) concept. It is proved that the strategies of each agent defined by individual optimization problems will converge to a local robust Nash equilibrium (R-NE), which further, with a fixed strong connection topology, will converge to the global R-NE. Additionally, to cope with the limited observation for MASs, local robust feedback control strategies are constructed based on the best approximate cost function and distributed robust Hamilton-Jacobi-Isaacs (DR-HJI) equations, which does not require global information of agents as in the traditional Riccati equation form. The feedback gains of the control strategies are found via the ant colony optimization (ACO) algorithm with a non-dominant sorting structure with convergence guarantees. Finally, simulation results are provided to verify the efficacy and robustness of the novel scheme. The agents arrived at the targeted position collision-free with a reduced arrival time, and reached the targeted positions under disturbance.

Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data

Optimal control simulations of musculoskeletal models can be used to reconstruct motions measured with optical motion capture to estimate joint and muscle kinematics and kinetics. These simulations are mutually and dynamically consistent, in contrast to traditional inverse methods. Commonly, optimal control simulations are generated by tracking generalized coordinates in combination with ground reaction forces. The generalized coordinates are estimated from marker positions using, for example, inverse kinematics. Hence, inaccuracies in the estimated coordinates are tracked in the simulation. We developed an approach to reconstruct arbitrary motions, such as change of direction motions, using optimal control simulations of 3D full-body musculoskeletal models by directly tracking marker and ground reaction force data. For evaluation, we recorded three trials each of straight running, curved running, and a v-cut for 10 participants. We reconstructed the recordings with marker tracking simulations, coordinate tracking simulations, and inverse kinematics and dynamics. First, we analyzed the convergence of the simulations and found that the wall time increased three to four times when using marker tracking compared to coordinate tracking. Then, we compared the marker trajectories, ground reaction forces, pelvis translations, joint angles, and joint moments between the three reconstruction methods. Root mean squared deviations between measured and estimated marker positions were smallest for inverse kinematics (e.g., 7.6 ± 5.1 mm for v-cut). However, measurement noise and soft tissue artifacts are likely also tracked in inverse kinematics, meaning that this approach does not reflect a gold standard. Marker tracking simulations resulted in slightly higher root mean squared marker deviations (e.g., 9.5 ± 6.2 mm for v-cut) than inverse kinematics. In contrast, coordinate tracking resulted in deviations that were nearly twice as high (e.g., 16.8 ± 10.5 mm for v-cut). Joint angles from coordinate tracking followed the estimated joint angles from inverse kinematics more closely than marker tracking (e.g., root mean squared deviation of 1.4 ± 1.8 deg vs. 3.5 ± 4.0 deg for v-cut). However, we did not have a gold standard measurement of the joint angles, so it is unknown if this larger deviation means the solution is less accurate. In conclusion, we showed that optimal control simulations of change of direction running motions can be created by tracking marker and ground reaction force data. Marker tracking considerably improved marker accuracy compared to coordinate tracking. Therefore, we recommend reconstructing movements by directly tracking marker data in the optimal control simulation when precise marker tracking is required.

Real-time signal-vehicle coupled control: An application of connected vehicle data to improve intersection safety

The proliferation of Connected Vehicles and their ability to collect a large amount of data presents an opportunity for the real-time safety optimization of traffic networks. At intersections, Adaptive Traffic Signal Control (ATSC) systems and dynamic speed advisories are among the proactive real-time safety interventions that can assist in preventing rear-end collisions. This study proposes a Signal-Vehicle Coupled Control (SVCC) system incorporating ATSC and speed advisories to optimize safety in real-time. By applying a rule-based approach in conjunction with a Soft-Actor Critic RL framework, the system assigns speed advisories to platoons of vehicles on each approach and extends the current signal time accordingly. Dynamic traffic parameters are collected in real-time and are used to estimate the current conflict rate at the intersection, which is then used both as an input to the model and to evaluate performance. The system was tested on two different intersections modeled using real-world data through the simulation platform VISSIM. Traffic conflicts were reduced by 41-55%, and vehicle delay was reduced by 21-24%. The results also show that the system functions at lower levels of market penetration, with diminishing returns beyond 50% MPR. The proposed system presents an SVCC framework that is both effective and low in computational intensity to optimize safety at signalized intersections.

Autonomous space systems control incorporating automated maneuvers strategies in the presence of parameters uncertainties

The research attempts to deal with the autonomous space systems incorporating new automated maneuvers strategies in the presence of parameters uncertainties. The main subject behind the investigation is to realize the high-resolution small amplitude orbital maneuvers via the first control strategy. And subsequently to realize the large amplitude orbital maneuvers via the second control strategy, as well. There is a trajectory optimization to provide the three-axis referenced commends for the aforementioned overactuated autonomous space system to be able to transfer from the initial orbit to its final ones, in finite burn, as long as the uncertainties of key parameters of the system such as the thrust vector, the center of the gravity, the moments of the inertia and so on are taken into real consideration. The strategies performances are finally considered through a series of experiments and a number of benchmarks to be tangibly verified.

Direct Methods for Predicting Movement Biomechanics Based Upon Optimal Control Theory with Implementation in OpenSim

The aim of this study was to compare the computational performances of two direct methods for solving large-scale, nonlinear, optimal control problems in human movement. Direct shooting and direct collocation were implemented on an 8-segment, 48-muscle model of the body (24 muscles on each side) to compute the optimal control solution for maximum-height jumping. Both algorithms were executed on a freely-available musculoskeletal modeling platform called OpenSim. Direct collocation converged to essentially the same optimal solution up to 249 times faster than direct shooting when the same initial guess was assumed (3.4 h of CPU time for direct collocation vs. 35.3 days for direct shooting). The model predictions were in good agreement with the time histories of joint angles, ground reaction forces and muscle activation patterns measured for subjects jumping to their maximum achievable heights. Both methods converged to essentially the same solution when started from the same initial guess, but computation time was sensitive to the initial guess assumed. Direct collocation demonstrates exceptional computational performance and is well suited to performing predictive simulations of movement using large-scale musculoskeletal models.

Improving recorded volume in mesial temporal lobe by optimizing stereotactic intracranial electrode implantation planning

PURPOSE

Intracranial electrodes are sometimes implanted in patients with refractory epilepsy to identify epileptic foci and propagation. Maximal recording of EEG activity from regions suspected of seizure generation is paramount. However, the location of individual contacts cannot be considered with current manual planning approaches. We propose and validate a procedure for optimizing intracranial electrode implantation planning that maximizes the recording volume, while constraining trajectories to safe paths.

METHODS

Retrospective data from 20 patients with epilepsy that had electrodes implanted in the mesial temporal lobes were studied. Clinical imaging data (CT/A and T1w MRI) were automatically segmented to obtain targets and structures to avoid. These data were used as input to the optimization procedure. Each electrode was modeled to assess risk, while individual contacts were modeled to estimate their recording capability. Ordered lists of trajectories per target were obtained. Global optimization generated the best set of electrodes. The procedure was integrated into a neuronavigation system.

RESULTS

Trajectories planned automatically covered statistically significant larger target volumes than manual plans [Formula: see text]. Median volume coverage was [Formula: see text] for automatic plans versus [Formula: see text] for manual plans. Furthermore, automatic plans remained at statistically significant safer distance to vessels [Formula: see text] and sulci [Formula: see text]. Surgeon's scores of the optimized electrode sets indicated that 95% of the automatic trajectories would be likely considered for use in a clinical setting.

CONCLUSIONS

This study suggests that automatic electrode planning for epilepsy provides safe trajectories and increases the amount of information obtained from the intracranial investigation.