Trajectory Constraints Research Articles

Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information

In automatic driving, accurate prediction of vehicle trajectory is the key to achieve automatic driving, and multi-vehicle joint trajectory prediction has become an important part of modern human-computer interaction systems such as automatic driving. In order to better predict vehicle trajectories, we propose a new residual diffusion model to infer the joint distribution of future multi-vehicle trajectories. This approach has several major advantages. First, the model is able to learn multiple probability distributions from trajectory data to obtain potential outcomes for vehicles to multiple future trajectories. Secondly, in order to integrate the motion characteristics of multiple vehicles in the same scene, we use the method of combining the reference denoising and multiple residual denoising to improve the model performance and prediction speed. Finally, on this basis, a general trajectory constraint function is introduced, so that the generated trajectories of multiple vehicles will not collide with each other. We perform a rich experimental comparison of various existing methods on the NGSIM dataset and demonstrate that the proposed algorithm achieves a 26% improvement on mAP.

Two-stage spatiotemporal cooperative reentry guidance strategy using transformer and improved beluga whale optimization

This research addresses the challenge of insufficient control margin caused by the coupling of multiple constraints in the cooperative precise reentry guidance of hypersonic vehicles. Drawing inspiration from the concept of spatiotemporal decoupling control, a rapid guidance strategy is developed to ensure precise handling of all constraints, including attack time, attack angle, and trajectory constraints. Initially, during the early phase of gliding flight, the adjustment of the heading angle is conceptualized as a single variable root-solving problem, in relation to the entrance width of the lateral azimuth error corridor. Subsequently, a lateral azimuth error corridor with adaptively narrowing entrance width, coupled with a Transformer network-based bank angle predictor, is incorporated to achieve precise fine-tuning of the heading angle under the soft constraint of velocity. In the later phase of gliding flight, the design of a cooperative guidance law under complex multiple constraints is transformed into a nonlinear rapid optimization problem of control commands. An enhanced beluga whale optimization suited to this guidance task is proposed. Finally, numerical simulations are carried out to validate the effectiveness of the proposed strategy under both nominal and uncertain conditions.

Onboard Burn Targeting and Guidance of NASA Human Flight Vehicles

The mission requirements of crewed space vehicles flown by NASA drove the development of new techniques for burn targeting and guidance for ascent, rendezvous, lunar landing, and deorbit. The Saturn V Iterative Guidance Mode used an approximately optimal steering law that could adjust the trajectory to compensate for performance issues while meeting trajectory constraints at propulsion-system cutoff. Hypersurface targeting provided constraints for the Saturn V Trans-Lunar Injection burn. E Guidance, in quadratic- and linear-acceleration forms, provided closed-loop descent and ascent guidance for the Apollo Lunar Module. The Space Shuttle’s Powered Explicit Guidance algorithm was more flexible than Apollo-era guidance algorithms, and it had better convergence characteristics, enabling it to support challenging abort profiles. The Space Launch System has built on the heritage of the Space Shuttle’s guidance algorithm. The Orion spacecraft’s Two-Level Targeter enables onboard, autonomous targeting of an entire mission profile subject to three-body dynamics in cislunar space. Orion uses Shuttle-derived guidance as the basis for an orbit-guidance package with more capability than guidance algorithms of previous vehicles.

An Advanced Control Method for Aircraft Carrier Landing of UAV Based on CAPF–NMPC

This paper investigates a carrier landing controller for unmanned aerial vehicles (UAVs), and a nonlinear model predictive control (NMPC) approach is proposed considering a precise motion control required under dynamic landing platform and environment disturbances. The NMPC controller adopts constraint aware particle filtering (CAPF) to predict deck positions for disturbance compensation and to solve the nonlinear optimization problem, based on a model establishment of carrier motion and wind field. CAPF leverages Monte Carlo sampling to optimally estimate control variables for improved optimization, while utilizing constraint barrier functions to keep particles within a feasible domain. The controller considers constraints such as fuel optimization, control saturation, and flight safety to achieve trajectory control. The advanced control method enhances the solution, estimating optimal control sequences of UAV and forecasting deck positions within a moving visual field, with effective trajectory tracing and higher control accuracy than traditional methods, while significantly reducing single-step computation time. The simulation is carried out using UAV “Silver Fox”, considering several scenarios of different wind scales compared with traditional CAPF–NMPC and the nlmpc method. The results show that the proposed NMPC approach can effectively reduce control chattering, with a landing error in rough marine environments of around 0.08 m, and demonstrate improvements in trajectory tracking capability, constraint performance and computational efficiency.

A Deep Reinforcement Learning-Based Intelligent Maneuvering Strategy for the High-Speed UAV Pursuit-Evasion Game

Given the rapid advancements in kinetic pursuit technology, this paper introduces an innovative maneuvering strategy, denoted as LSRC-TD3, which integrates line-of-sight (LOS) angle rate correction with deep reinforcement learning (DRL) for high-speed unmanned aerial vehicle (UAV) pursuit–evasion (PE) game scenarios, with the aim of effectively evading high-speed and high-dynamic pursuers. In the challenging situations of the game, where both speed and maximum available overload are at a disadvantage, the playing field of UAVs is severely compressed, and the difficulty of evasion is significantly increased, placing higher demands on the strategy and timing of maneuvering to change orbit. While considering evasion, trajectory constraint, and energy consumption, we formulated the reward function by combining “terminal” and “process” rewards, as well as “strong” and “weak” incentive guidance to reduce pre-exploration difficulty and accelerate convergence of the game network. Additionally, this paper presents a correction factor for LOS angle rate into the double-delay deterministic gradient strategy (TD3), thereby enhancing the sensitivity of high-speed UAVs to changes in LOS rate, as well as the accuracy of evasion timing, which improves the effectiveness and adaptive capability of the intelligent maneuvering strategy. The Monte Carlo simulation results demonstrate that the proposed method achieves a high level of evasion performance—integrating energy optimization with the requisite miss distance for high-speed UAVs—and accomplishes efficient evasion under highly challenging PE game scenarios.

Accurate relative measurement of multitarget poses by monocular vision for nonmodel-based real-time calibration of industrial robot

On basis of multitarget poses measured by monocular vision, a nonmodel-based real-time calibration method for six-DoF industrial robot is presented. Markers fixed on base and joints of robot are measured by a camera synchronously, at each measurement configuration selected with constraints of marker visibility and work trajectory, to provide decoupled pose data. The accuracy problem due to low precision of camera is addressed by substituting absolute measurement of movable markers with relative measurement from a static marker to the movable ones. On this basis, the deviated pose of each joint can be obtained accurately and the actual kinematic parameters of robot are obtained. The simulation verifies the selection algorithm of measurement configurations and evaluates the effects of decisive error sources on measurement accuracy. The experiment demonstrates the advantage of relative pose measurement using the low-cost camera on calibration accuracy by comparison with typical model-based calibrations using a precise laser tracker.

A time-fuel hybrid optimal guidance for micro/nano satellite’s glideslope approach

Aimed at the trajectory planning problem of micro/nano satellites’ approaching missions under multiple constraints, this paper proposes a V-bar glideslope maneuver optimization method considering both time and fuel consumption. A dual-objective trajectory optimization model is established based on the CW motion equation, considering the constraints of trajectory and impulse. The optimal solution set of the glideslope point position and maneuvering time is obtained by employing the NSGA2 algorithm. In order to reduce computational complexity, a method for simplifying trajectory optimization is proposed. By solving a constrained dual-impulse optimization problem, the feasible range of distance and transfer time between adjacent glideslope points are obtained, which transforms the constraint equations of the glideslope maneuver optimization into the search space of optimization variables. The problem is simplified to an unconstrained optimization problem. Finally, a simulation is conducted with the approaching mission of a typical micro/nano satellite. The results demonstrate that the proposed method can provide an optimal solution set considering both fuel and time consumption, offering a flexible maneuver planning strategy for on-orbit application.

A semantic knowledge-based method for home service robot to grasp an object

To help the home service robot grasp an object with the constraints from the object itself, task and environment at the same time, we propose a knowledge-based method. Firstly, we construct five ontologies (object, task, environment, constraint and probabilistic ontology) to represent the five in a unified format. The remarkable things are that the object ontology is part-based and the constraints (grasp type, grasp location, approach direction, opening width, trajectory constraint and grasp force) are throughout the whole grasp process. To reason the constraints out with the findings, we propose a collaborative reasoning mechanism. It contains ontological reasoning, rule-based reasoning and probabilistic reasoning. Secondly, we propose to use SWRL (Semantic Web Rule Language) rules to label the object’s segments with functional parts. For its textual form, this method is fast. Thirdly, JSHOP2 planner is used to make the inferred constraints as the parameters of grasp subactions. Once the subaction list is output, the corresponding executable codes are called. Finally, we use Tiago robot to grasp different objects in different task and environment in our laboratory.

Flight analysis and optimization design of vectored thrust eVTOL based on cooperative flight/propulsion control

The rise of electric vertical take-off and landing (eVTOL) aircraft has sparked research interest, but attention to modeling and evaluation methods, particularly for vectored thrust eVTOL, lags behind. The intricate flight/propulsion coupling and structure/control complexity contribute to the gap. Moreover, integrated modeling and evaluation processes for vectored thrust eVTOL should fully account for the tilting characteristics of the fan/propeller. This paper proposes a flight/propulsion cooperative control modeling and evaluation method for vectored thrust eVTOL. The methodology uses the Gaussian pseudospectral trajectory optimization method to obtain the overall characteristics and cooperative control strategy of flight/propulsion under strict take-off and landing trajectory constraints. Besides, this paper yielded the parameterized analysis and matching optimization design of the propulsion system. Results indicated that for an aircraft with a take-off weight of 1.8 tons, equipped with four 1.6 m diameter tilting ducted fans and a single discharge rate 5C battery, its cruising range is 135.1 km based on the selected technical parameters. The power consumption accounts for merely 12.6% of the total battery capacity during the take-off phase with high-power demand but short duration. By employing a pack of high-power-density and high-energy-density batteries to drive the ducted fans jointly, the aircraft's cruising range, fan size, and payload can be optimized. The 20C+2C battery scheme can either increase the cruising range by 27.3% without changing the fan diameter or increase the cruising range by 6.1% when the fan diameter is reduced to 1.1 m. Wherein without changing the fan diameter, the battery mass can be reduced from 720 kg to 571 kg while satisfying the takeoff and cruising requirements. The results demonstrated the effectiveness of improving the cruising range and optimizing the fan size and payload.

A novel dual-arm adaptive cooperative control framework for carrying variable loads and active anti-overturning

This paper aims to tackle the issue of carrying variable loads and disturbances in an impedance-based dual-arm robot. When robots are engaged in transportation tasks, deviations in trajectory due to changing loads and the risk of objects falling under external disturbances or unstable gripping can lead to mission failure. To address these issues, a novel Dual-Arm Adaptive Cooperative Control Framework (ACCF) is proposed. The ACCF is designed to ensure adherence to trajectory constraints in the presence of load variations and to actively respond to sliding or overturning caused by disturbances. The framework is user-friendly and robust, featuring a two-loop arrangement. The inner-loop incorporates an adaptive force control law to robustly control internal forces for dual-arm gripping. The outer-loop utilizes centralized impedance control, incorporating a fast gravity estimation scheme to compensate for trajectory deviations and an active anti-overturning scheme to resist sliding or overturning of objects during disturbances. Experimental evaluations and comparisons are conducted. The results successfully demonstrate the ACCF's adaptability under variable loads and external disturbances, providing a solution for practical dual-arm applications, such as transportation, in future scenarios.

Research on the Effect Characteristics of Free-Tail Layout Parameters on Tail-Sitter VTOL UAVs

The tail-sitter VTOL UAV boasts not only high-speed cruising and air hovering capabilities, but also its unique tail-sitting vertical takeoff and landing and hovering attitude enable aerial operations with an exceptionally small cross-sectional area. This feature effectively broadens the scope of application for the UAV in intelligent agriculture, encompassing tasks such as agricultural inspection, production monitoring, and topographic mapping. Given the necessity for frequent modal transitions, this paper is grounded in a thorough examination of the typical structural characteristics of the tail-sitter VTOL UAV. A comprehensive technical solution for tail-sitter VTOL UAVs, based on the free-tail configuration, is proposed in this paper. The free-tail structure is utilized to address the limitations of traditional tailless layout and fixed landing gear in terms of flight stability and takeoff/landing performance of tail-sitter VTOL UAVs. However, the implementation of this solution necessitates the addition of a new maneuvering unit. Consequently, this paper delves into the aerodynamic coupling characteristics and laws between the layout parameters such as tail number, tail length, and tail area and the tail-sitter VTOL UAV fuselage. To optimize the free-tail configuration, a multi-objective optimization is performed by integrating the overall UAV dynamics, landing dynamics, and modal transition trajectory constraints. The results of stability modeling simulations and flight tests demonstrate that the tail-sitter VTOL UAV equipped with this technical solution exhibits enhanced maneuverability and flight efficiency compared to the conventional tailless layout.

Topology optimization for rigid and compliant hybrid mechanisms

This paper proposes a topology optimization method integrating the variable trajectory constraints to design rigid and compliant hybrid mechanisms. The variable trajectory constraints are distributed in the design domain and are uniformly modeled by nonlinear spring model. Each of the variable trajectory constraints has an active or inactive state and different states map various mechanical properties of the nonlinear springs. The nonlinear relation between force and displacement of the spring is formulated. A new group of design variables denoting the states of the springs is introduced into the traditional density-based topology optimization model. The design variables representing the element density of the continuum structure and the states of the variable trajectory constraints are uniformly penalized to avoid intermediate values in order to obtain a physically realizable mechanism. Sensitivity analysis is performed by the adjoint equation method. To demonstrate the versatility and the generality of the proposed method, the typical numerical examples including linear, spline and circular trajectory constraints were implemented. The numerical comparisons between the nonlinear spring model and the actual trajectory constraints verify the accuracy. The proposed method expands the application of topology optimization and can be extensively employed to design the mechanisms integrating both the compliant members and rigid parts.

Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design

The paper describes the development of an optimization model for the layout of an industrial robot relative to known locations of served machines and operations to be performed. Robotized material handling applications, defined by trajectories (paths, speed profiles) and final points, are considered in this research. An energy-monitoring framework set up by joint velocities provides input data that are fed to the optimization model. The physical placement of the robot base stands for the decisional variables, while the objective function is represented by the total distance covered by individual joints along established task routes transposed into energy consumption. The values of the decisional variables are restricted by trajectory constraints (waypoints on paths), joint operating values and link dimensions. Modelling technique and practical results using the Microsoft Solver optimization tool from Excel for Microsoft 365, Version 2312 are reported for SCARA-type robots. The performance of the optimization model is compared with actual measurements of consumed energy on an Adept Cobra S600 SCARA robot.

Authoring and Simulating Meandering Rivers

We present a method for interactively authoring and simulating meandering river networks. Starting from a terrain with an initial low-resolution network encoded as a directed graph, we simulate the evolution of the path of the different river channels using a physically-based migration equation augmented with control terms. The curvature-based terms in the equation allow us to reproduce phenomena identified in geomorphology, such as downstream migration of bends. Control terms account for the influence of the landscape topography and user-defined river trajectory constraints. Our model implements abrupt events that shape meandering networks, such as cutoffs forming oxbow lakes and avulsions. We visually show the effectiveness of our method and compare the generated networks quantitatively to river data by analyzing sinuosity and wavelength metrics. Our vector-based model runs at interactive rates, allowing for efficient authoring of large-scale meandering networks.

The Improved Method for Indoor 3D Pedestrian Positioning Based on Dual Foot-Mounted IMU System.

Micro-Electro-Mechanical System (MEMS) inertial sensors, characterized by their small size, low cost, and low power consumption, are commonly used in foot-mounted wearable pedestrian autonomous positioning systems. However, they also have drawbacks such as heading drift and poor repeatability. To address these issues, this paper proposes an improved pedestrian autonomous 3D positioning algorithm based on dual-foot motion characteristic constraints. Two sets of small-sized Inertial Measurement Units (IMU) are worn on the left and right feet of pedestrians to form an autonomous positioning system, each integrated with low-cost, low-power micro-inertial sensor chips. On the one hand, an improved adaptive zero-velocity detection algorithm is employed to enhance discrimination accuracy under different step-speed conditions. On the other hand, considering the dual-foot gait characteristics and the height difference feature during stair ascent and descent, horizontal position update algorithms based on dual-foot motion trajectory constraints and height update algorithms based on dual-foot height differences are, respectively, designed. These algorithms aim to re-correct the pedestrian position information updated at zero velocity in both horizontal and vertical directions. The experimental results indicate that in a laboratory environment, the 3D positioning error is reduced by 93.9% compared to unconstrained conditions. Simultaneously, the proposed approach enhances the accuracy, continuity, and repeatability of the foot-mounted IMU positioning system without the need for additional power consumption.

Fast fixed-time three-dimensional terminal guidance with non-concave trajectory constraint

Focusing on the non-concave trajectory constraint, a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing, planetary landing and spacecraft rendezvous and docking terminal phase with external disturbance is investigated in this paper. Firstly, a fixed-time observer based on real-time differentiator is developed to compensate for the external disturbance, whose estimation error can converge to zero after a time independent of the initial state. Then, a sliding surface ensuring fixed-time convergence is presented. This sliding surface can guarantee that the vehicle achieves a non-concave trajectory, which is better for avoiding collision and maintaining the visibility of the landing site or docking port. Next, the nonsingular guidance ensuring the fixed-time convergence of the sliding surface is proposed, which is continuous and chatter free. At last, three numerical simulations of Mars landing are performed to validate the effectiveness and correctness of the designed scheme.

Prediction of carbon emissions peak and carbon neutrality based on life cycle CO2 emissions in megacity building sector: Dynamic scenario simulations of Beijing

In order to design an optimal carbon peak and carbon neutralization pathway for the high-density building sector, a dynamic prediction model is established using system-dynamics coupled building life cycle carbon emission model (SD-BLCA) with consideration of future evolutionary trajectory and time constraints. The model is applied in Beijing using the SD-BLCA combined with scenario analysis and Monte Carlo methods to explore optimal trajectory for its building sector under 30-year timeframe. The results indicate that by increasing the proportion of renewable energy generation by 7% and retrofitting 60 million m2 of existing buildings, these two mature measures can offset the growth of carbon emissions and achieve the peak target by 2025. However, achieving carbon neutrality necessitates a shift from isolated technologies to a comprehensive net-zero emissions strategy. The study proposes a time roadmap that integrates a zero-carbon energy supply system and the carbon reduction measures of the whole life cycle. This strategy primarily relies on renewable sources to provide heat, power, and hydrogen, resulting in estimated reductions of 29.8 Mt, 28.1 Mt, and 0.7 Mt, respectively. Zero energy buildings, green buildings, and renovated buildings can reduce carbon emissions through their own energy-saving measures by 8.4, 18.2, and 11.8 kg/m2, respectively.

Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots

Coal Mine Mobile Robots (CMRRs) are generally large in size and inertia, while narrow laneway space and bumpy terrain pose great challenges to CMRR’s planning and control. Aiming at the trajectory planning and tracking problems of CMRR, a new trajectory class MINCO is derived in detail based on the properties of differential flat systems. A trajectory planning method based on MINCO trajectory and safety corridor constraints constructed with underground environmental constraints is further proposed. A trajectory tracking method based on model predictive control (MPC) is further proposed. The prediction model of MPC is constructed by a kinematics model and transformed into a standard quadratic programming problem according to the cost function of a trajectory tracking target. Finally, large quantities of field tests were carried out for the proposed approaches. The results show that the proposed planning algorithm based on MINCO trajectory can achieve good avoidance effects within 10 planning attempts in different obstacle scenarios, and the trajectory is smoother compared to the Fast-Planner algorithm, with shorter trajectory length and less planning time. The tracking error of MPC is always less than 0.05 m in different underground scenarios, having a more adaptable trajectory tracking effect than PID.

Robust approximate constraint following control for SCARA robots system with uncertainty and experimental validation

In the context of mechanical systems characterized by nonlinearity, uncertainty, and unknown external disturbances, this paper presents a novel and practical robust control algorithm based on the Udwadia–Kalaba (UK) equation that incorporates the ideal system trajectory as a servo constraint. Firstly, the constraint force of the system under servo constraints can be written in analytic form, which is the first part of the proposed control algorithm. The remaining parts deal with the initial condition incompatibility and the uncertainty within the system, respectively. Secondly, it is proved that the controlled system with uniform boundedness (UB) and uniform ultimate boundedness (UUB) by using the Lyapunov method. Finally, based on the SCARA robot platform, numerical simulations and experiments are performed using the rapid control prototype CSPACE. The results indicate that the proposed robust control method provides excellent dynamic performance under the constraints of the ideal trajectory. The effectiveness and practicality of the control method are further demonstrated.

Dynamic modeling and analysis of wheeled wall-climbing robot

The dynamic model is very important for the design of a wall-climbing robot and the final realization of its motion performance. The general process of dynamic modeling and expression equations of dynamic models are given for the wheeled wall-climbing robot based on the modeling method of the Udwadia-Phohomsiri equation. Firstly, the dynamic model of an unconstrained four-wheeled wall-climbing robot is constructed. Then, a trajectory constraint is defined, the rationality of the dynamic model for the unconstrained wall-climbing robot is verified by numerical simulation. Again, constraint equations under the conditions of synchronous toothed belt structure, non-lateral motion and nonslip between the driving wheel and the wall surface are established. Finally, the dynamic model of the unconstrained wall-climbing robot is gradually combined with constraint equations, and numerical simulations are implemented. Numerical simulation results verify the correctness of the wall-climbing robot model and constraint models, as well as the effectiveness and advantages of the modeling method.