Fast Trajectory Research Articles

Fast Trajectory Generation on a Path using Feedback Linearization*

This paper describes a method for quickly generating feasible trajectories for fully actuated autonomous surface vehicles. It is assumed that a path consisting of a list of positions with associated heading angles is given. The goal is to generate a feasible trajectory that follows a given path and takes into account the nonlinear dynamical model. The trajectory contains the state as well as force-level input variables and can be used as feed-forward control by a trajectory controller. The generation of the trajectory is based on a closed-loop simulation of the nonlinear model in combination with a nonlinear controller and guidance law. Feedback linearization is applied to linearize the motion model of the surface vehicle at the velocity level. A state feedback controller is used to control the pose along the path. The presented method allows for simple and fast trajectory generation and can therefore be applied in sampling-based motion planners where many trajectories have to be generated.

An online reliable local trajectory planner for quadrotors

PurposeThis paper aims to propose an online local trajectory planner for safe and fast trajectory generation that combines the jerk-limited trajectory (JLT) generation algorithm and the particle swarm optimization (PSO) algorithm. A trajectory switching algorithm is proposed to improve the trajectory tracking performance. The proposed system generates smooth and safe flight trajectories online for quadrotors.Design/methodology/approachFirst, the PSO algorithm method can obtain the optimal set of target points near the path points obtained by the global path searching. The JLT generation algorithm generates multiple trajectories from the current position to the target points that conform to the kinetic constraints. Then, the generated multiple trajectories are evaluated to pick the obstacle-free trajectory with the least cost. A trajectory switching strategy is proposed to switch the unmanned aerial vehicle (UAV) to a new trajectory before the UAV reaches the last hovering state of the current trajectory, so that the UAV can fly smoothly and quickly.FindingsThe feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles.Practical implicationsThe proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient.Originality/valueThe proposed local planner for trajectory generation and evaluation combines PSO and JLT generation algorithms. The proposed method can provide a collision-free and continuous trajectory, significantly reducing the required computing resources. The PSO algorithm locally searches for feasible target points near the global waypoint obtained by the global path search. The JLT generation algorithm generates trajectories from the current state toward each point contained by the target point set. The proposed trajectory switching strategy can avoid unnecessary hovering states in flight and ensure a continuous and safe flight trajectory. It is especially suitable for micro quadrotors with a small payload and limited onboard computing power.

Structural Transformer Improves Speed-Accuracy Trade-Off in Interactive Trajectory Prediction of Multiple Surrounding Vehicles

Fast and accurate long-term trajectory prediction of surrounding vehicles (SVs) is critical to autonomous driving systems. In high-density traffic flows, strongly correlated vehicle behaviors require considering the interactions among multiple SVs when predicting their future trajectories. However, existing interactive prediction methods, most based on Long Short-Term Memory (LSTM), are suffering from slow prediction because they analyze SVs one by one and analyze trajectory sequence node by node. This paper presents a fast interactive trajectory prediction method called Structural Transformer which learns both spatial and temporal dependencies among multiple SVs in parallel. Specifically, our model first removes the internal states and loops of LSTM and replaces with a weighted self-reference mapping to realize parallel computation. Then, it embeds the relative spatial information of multiple SVs into trajectory states and reorganizes the self-reference mapping with neighbor-only interaction masks to achieve interactive prediction. Results on the NGSIM dataset show satisfyingly speed and accuracy performance on long-term trajectory prediction of multiple SVs. The longitudinal and lateral errors are reduced to 2.67m and 0.25m over 5s time horizon. The computational time of each step is only 12ms on a 2080ti GPU, which is over 4 times faster than the Structural LSTM.

Online obstacle avoidance path planning and application for arc welding robot

• The system of online collision-free path planning for the arc welding robot is established. • An improved lazy-PRM algorithm with repulsion field is proposed to solve the path planning. • The collision-free path of the 7-DOF arc welding robot can be planned online. • The path quadratic optimization strategy is proposed to fine-tuning the pose and position of the welding gun. • Fast trajectory planning for each workpiece at any position during the welding process. The offline path planning problem for arc welding robots becomes increasingly difficult to achieve with the increasing product complexity and the open work environment, such as the welding tasks in ship formation. Most research considers the path planning problem without time limitations or other constraints that conform to the actual production process. During the welding process, the placement of workpieces seriously affects the efficiency of finding feasible paths for the welding robots for weld tasks that cannot be repeated accurately. Thus, in the flow welding workshop, the improved lazy Probabilistic Roadmap (PRM) algorithm based online collision-free path planning method is proposed to finish the welding tasks of general stiffened plates at an arbitrary position on the workbench within the robot's reach. The work environment is built with the grid method to reduce the computational dimension of collision detection. The local path planner is implemented by the lazy-PRM algorithm with the repulsive force field to rapidly search the collision-free path for the welding robot with specific safe distances. The robot posture adjustment strategy is also applied to the collision detection process to adjust some poses of the robot at candidate paths to be feasible. The improved path planning method is tested on both the simulated environment and actual application scenario and proved to be suitable for the path planning problem of the arc welding robot. The results of this study have been integrated into a computer approach, which can accelerate the motion planning process and reduce human efforts.

A finite and fast time-optimal trajectory planning based on velocity sinking method for robots along specified path

Time-optimal trajectory planning is used in many areas of industrial robots and has a wide range of applications. This paper presents a finite and fast computation method for calculating time-optimal robot trajectories along specified geometric paths, namely the velocity sinking method. The velocity sinking method differs from those proposed in the existing literature which solve uncertain optimization problem. Firstly, the maximum velocity in multiple state constraints is calculated using a path parameterization method. Then, the pseudo maximum velocity trajectory is calculated using a numerical integration method. Finally, the time-optimal trajectory planning under path constraints is determined using an iterative velocity sinking algorithm. The experiment demonstrates the effectiveness and efficiency of this method in industrial robotic settings.

Tele‐aiming control design for reconnaissance robot using a strong tracking multi‐model extended super‐twisting observer

AbstractIn this study, a tele‐aiming control system for reconnaissance robots is proposed, capable of enabling operators to quickly control the robot to aim at and track the manoeuvring target with random motion characteristics under measurement noise. To track such manoeuvring targets, a cascaded multi‐model super‐twisting observer is designed. First, the system uncertainty and the inertial impact force disturbance generated during rapid tele‐aiming are expanded into a lumped disturbance state. Subsequently, a strong tracking filter is employed to filter and estimate the system output and the lumped disturbance, and the output of the filter is input into the multi‐model extended state super‐twisting observer. The final second‐order state estimation and lumped disturbance estimation of the augmented system are conducted in accordance with the weighted sum of all super‐twisting observers. Lastly, a non‐singular terminal sliding mode controller is designed to compensate for the lumped disturbance and achieve the fast trajectory tracking. To verify the effectiveness of the proposed observer‐based controller, a series of manoeuvring target tracking experiments was performed. As revealed by the experimental results, the proposed controller can quickly control the reconnaissance robot to aim at and track the manoeuvring target with random abrupt motion characteristics under measurement noise disturbance.

Multi-stimulus synergistic control soft actuators based on laterally heterogeneous MXene structure

Untethered intelligent soft robots capable of autonomous, continuous, directional, and fast movements under external stimuli have great potential in environmental, industrial, military, and medical fields. This paper used an unconventional dual-flow filtration method to prepare a laterally heterogeneous MXene-NiFe2O4/Polycarbonate bilayer membrane. Based on this, a one-piece untethered soft crawling robot with multi-stimulus synergistic control is developed, which can achieve relatively fast crawling up to 3.77 mm/s and directional turning locomotion. In addition, a cross-shaped robot that can realize biaxial flipping locomotion is developed by using the inverse snapping property of the polycarbonate films under light and humidity excitation. The preparation method has also been extended to unconventional multi-flow filtration and applied to bionic flowers and asymmetric grippers with biological gradients. This work provides an effective way for designing soft crawling robots with fast actuation performance and adjustable trajectories that can be used in areas such as bionic soft robots and biomedical systems.

Finite-time robust control of robotic manipulator with input deadzone and time-varying disturbance compensation

In this paper, the problem of trajectory tracking for robotic manipulator with input deadzone and time-varying disturbances is investigated using a continuous non-singular terminal sliding mode control (SMC). The robotic manipulator plays a vital role in industrial as well as many other applications. However, it faces numerous problems for accurate and fast trajectory tracking response in the presence of unknown uncertainties and external disturbances, that always have adverse affects on system closed-loop performance. This paper proposes a valid control design scheme to address the aforementioned practical issue. First, a non-singular terminal sliding mode surface is designed. Then, a robust control law is developed to guarantee the finite-time converges of system states to the origin. In addition, the sign function is substituted by saturation function, which can effectively reduce the chattering driven by the high-frequency switching item in the traditional SMC method. Moreover, the Lyapunov stability criterion is used to prove the stability of the closed-loop system. The efficiency of the designed control scheme is validated with numerical simulations.

Risk-Aware Fast Trajectory Planner for Uncertain Environments Based on Probabilistic Surrogate Reliability and Risk Contours

This letter presents the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> isk- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> ware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</u> ast <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</u> rajectory plann <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">er</u> (RAFTER) for autonomous vehicles in dynamic uncertain environments, which is based on the probabilistic surrogate reliability of other traffic participants and risk contours. In contrast to the conventional risk metric, RAFTER not only provides the upper bound of the probability of constraint violation but deduces an infimum on the risk which the controlled plant can stand by the probabilistic reliable surrogate model. Such a risk-aware algorithm is capable of perceiving uncertainty and handling robustness. A series of covering disks are constructed utilizing a concise geometric configuration for a lower conservatism representation of the vehicle profile, which attains a desirable tradeoff between the quantity and area of occupation. Safe travel corridors are built on the dilated map via covering disks, significantly reducing the computational burden of a reliability-based optimization procedure for optimal trajectory planning. The effectiveness of the proposed method is confirmed by two numerical simulations derived from real scenarios.

On-line implementation of model free controller for oxygen stoichiometry and pressure difference control of polymer electrolyte fuel cell

This paper proposes and validates a model free controller to improve the real time operating conditions of Proton Exchange Membrane Fuel Cells (PEMFC). This approach is based on an ultra-local model that does not depend on a precise knowledge of the system. It is perfectly adapted to a complex system such as the fuel cell, while benefiting from the ease of online implementation and low computational cost. The designed controller is used to regulate both the oxygen stoichiometry and the membrane inlet pressure, which are crucial operating conditions for the fuel cell's lifetime. The objectives of the proposed control strategy are twofold: preventing the starvation failure, and limiting the potential for mechanical degradation of the membrane during a large pressure difference. The performance of the proposed control strategy is initially evaluated by a simulation environment for both oxygen stoichiometry and inlet pressure difference control of fuel cell stack. An online validation on 1.2 KW fuel cell stack is conducted to control the membrane pressure drop. Two case studies are comprehensively investigated in relation to stoichiometry control: set point tracking and rejection of unmeasured disturbances caused by current variations. Simulations and experimental results reveal that the proposed controller provides significantly better performance in terms of fast trajectory tracking, and ensures less overshoot compared to the Fuzzy PID and PID controller. This efficiency is proven using the Integral Absolute Error (IAE), Integral Squared Error (ISE) and Integral of the Square input (ISU) performance indexes.

Kidney function in patients with bipolar disorder with and without lithium treatment compared with the general population in northern Sweden: results from the LiSIE and MONICA cohorts

The clinical relevance of lithium nephropathy is subject to debate. Kidney function decreases with age and comorbidities, and this decline might lead to attribution bias when erroneously ascribed to lithium. We aimed to investigate whether patients with bipolar or schizoaffective disorder had faster decline in estimated glomerular filtration rate (eGFR) compared with the general population, whether observed differences in the steepness of the decline were attributable to lithium, and whether such changes depended on the length of lithium exposure. In this cross-sectional cohort study, we used clinical data from the Lithium-Study into Effects and Side-effects (LiSIE) retrospective cohort study, which included patients with bipolar disorder or schizoaffective disorder whose medical records were reviewed up to Dec 31, 2017, and the WHO Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) study, covering a representative sample of the general population in northern Sweden aged 25-74 years. The primary outcome was the age-associated decline of creatinine-based eGFR, assessed using linear regression. We adjusted for sex and grouped for different lengths of lithium exposure (never or <1 year, 1-5 years, >5-10 years, and >10 years). For patients with moderate-to-severe kidney disease we identified the underlying nephropathy in the case records. From LiSIE, we included 785 patients (498 [63%] female and 287 [37%] male), with a mean age of 49·8 years (SD 13·2; range 25-74). From MONICA, we included 1549 individuals (800 [52%] female and 749 [48%] male), with a mean age of 51·9 years (13·8; 25-74). No ethnicity data were collected. Adjusted for duration of lithium exposure, eGFR declined by 0·57 mL/min/1·73 m2/year (95% CI 0·50-0·63) in patients with bipolar disorder or schizoaffective disorder and by 0·57 mL/min/1·73 m2/year (0·53-0·61) in the reference population. Lithium added 0·54 mL/min/1·73 m2 (0·43-0·64) per year of treatment (p<0·0001). After more than 10 years on lithium, decline was significantly steeper than in all other groups including the reference population (p<0·0001). Lithium nephropathy was judged to be the commonest cause of moderate-to-severe chronic kidney disease, but comorbidities played a role. The effect of lithium on eGFR showed a high degree of inter-individual variation. Steeper eGFR decline in patients with bipolar disorder or schizoaffective disorder can be attributed to lithium, but the trajectory of kidney function decline varies widely. Comorbidities affecting kidneys should be treated assertively as one possible means to affect the trajectory. In patients with a fast trajectory, a trade-off is required between continuing lithium to treat mental health problems and discontinuing lithium for the sake of renal health. Norrbotten County Research and Learning Fund Sweden, Visare Norr (Northern County Councils Regional Federation Fund), Swedish Kidney Foundation (Njurfonden), Swedish Kidney Association (Njurförbundet), Norrbotten section. For the Swedish translation of the Summary see Supplementary Materials section.

Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the control’s accuracy but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by pneumatic artificial muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000°s-2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we can tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics, and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Using PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

Fault-Tolerant Guidance of Rocket Vertical Landing Phase Based on MPC Framework

For the vertical landing process of reusable rockets, the landing accuracy is likely to be affected by disturbances and faults during flight. In this paper, a fault-tolerant guidance method based on the MPC framework is put forward. First, we propose a piecewise guidance algorithm that combines a trajectory optimization algorithm based on convex optimization with the MPC framework. With the fast trajectory optimization algorithm and the MPC framework that recursively introduces the real-time state, this algorithm forms a robust closed loop. Then, we design an integrated guidance, navigation, and control (GNC) system to enhance the fault tolerance and robustness of the guidance method. Simulation experiments verify that this method is fault-tolerant to various fault conditions including navigation system failures, control system failures, drag coefficient deviations, and atmospheric density deviations. This guidance method is robust enough to overcome disturbances and faults, and it has great potential for online use.

Optimization-Based Trajectory Planning for Autonomous Parking With Irregularly Placed Obstacles: A Lightweight Iterative Framework

This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.

Toward Fast Trajectory Recovery for Servo Motor Systems Under Instantaneous Large Disturbance

The instantaneous large disturbances, which are typically characterized by large amplitudes and short durations, generally force the actual trajectory of a servo motor system to deviate from the desired trajectory dramatically. In view of this, a novel fast trajectory recovery scheme is proposed mainly from the perspective of trajectory planning. To this end, a two-loop constrained trajectory replanning based control framework is developed to recover the deviated trajectory in a minimum-time manner under various modeling uncertainties, kinematic and dynamic constraints. Comparative experiments on a servo motor testbed show superiorities of the proposed approach over other related algorithms.

The Position of Body Mass in a Network of Human Life History Indicator

Body mass is widely recognized as a morphological trait which is important for fitness optimization both in humans and other animals. Here, we propose that body mass is a part of fast life history trajectory – the fitness optimization pattern which emerges from harsh environment with a function to maximize reproductive output. To test this prediction, we measured body mass index (BMI) and a set of life history indicators in a large sample of reproductive individuals (N = 1,504; 32% males; Mage = 27.20; SD = 9.2). The data were collected via an online survey. Bivariate correlations showed that BMI was positively related to reproductive success, childhood poverty, and short-term mating success; furthermore, it was negatively associated to physical health, age of the first menarche, and economic reasons against reproduction. The Network Analysis confirmed that BMI is positively related to short-term mating success and reproductive success, and negatively to physical health and economic reasons against reproduction. Furthermore, centrality metrics showed that BMI has relatively low centrality indices, and thus represents a peripheral node in the network. The present data confirm that body mass is a morphological trait which participates in the fast life history trajectory of fitness optimization. A body mass which is slightly higher than population mean (but below obesity levels) probably represents an adaptive response to depriving economic conditions in childhood and contributes to the maximization of reproductive fitness. Finally, we highlight that the relations between body mass and life history may differ between and within species.

Fast Joint Multi-Robot Trajectory Optimization by GPU Accelerated Batch Solution of Distributed Sub-Problems.

We present a joint multi-robot trajectory optimizer that can compute trajectories for tens of robots in aerial swarms within a small fraction of a second. The computational efficiency of our approach is built on breaking the per-iteration computation of the joint optimization into smaller, decoupled sub-problems and solving them in parallel through a custom batch optimizer. We show that each of the sub-problems can be reformulated to have a special Quadratic Programming structure, wherein the matrices are shared across all the problems and only the associated vector varies. As result, the batch solution update rule reduces to computing just large matrix vector products which can be trivially accelerated using GPUs. We validate our optimizer’s performance in difficult benchmark scenarios and compare it against existing state-of-the-art approaches. We demonstrate remarkable improvements in computation time its scaling with respect to the number of robots. Moreover, we also perform better in trajectory quality as measured by smoothness and arc-length metrics.

Fast trajectory planning and control of a lab-scale 3D gantry crane for a moving target in an environment with obstacles

In this work, the real-time optimal trajectory planning, together with a cascaded tracking controller, is presented for a three-dimensional (3D) gantry crane in an environment with static obstacles and a dynamically moving target considering dynamic constraints and control input limits. State-of-the-art trajectory optimization-based approaches require long computation times and cannot quickly respond to changes in the target state. The focus of this paper lies on a novel trajectory planning algorithm, which consists of two steps. First, an offline trajectory planner is implemented to compute a time-optimal, collision-free, and dynamically feasible trajectory database that connects all possible initial states of the gantry crane from a predefined starting subspace to the target states in a target subspace. Second, based on linear constrained quadratic programming, the online trajectory replanner makes use of this trajectory database to generate an optimal trajectory in real time that accounts for all changes in the target state. Additionally, a trajectory tracking controller is developed to take into account the dynamic constraints of the gantry crane and to compensate for possible model inaccuracies, disturbances, and other non-modeled effects. Both simulation and experimental results are presented to demonstrate the performance of the proposed trajectory (re)planning algorithm and the control concept.

Fast Trajectory Generation Method for Midcourse Guidance Based on Convex Optimization

To generate the midcourse guidance trajectory for intercepting the high-speed and high maneuvering target, which is a strongly nonlinear and strongly constrained problem, a two-stage convex optimization method is proposed to solve the optimal trajectory quickly. In the first stage, an initial trajectory generation method is proposed, by which the trajectory’s terminal state is close to the terminal position. And the generated trajectory is used as the initial solution of the convex optimization method. In the second stage, the original nonconvex optimization problem is transformed into a convex optimization problem by linearization and relaxation methods and then solved discretely. In the numerical simulation, the effectiveness of the proposed method is verified, and the robustness of the method is verified in different initial and terminal states. Then, several ablation experiments are operated to verify the advantage of the rapid initial trajectory generation method in the first stage. Finally, compared with the gauss pseudospectral method (GPM), the proposed method is proved to be efficient and has the potential of online trajectory generation.

Heuristic decomposition planning for fast spacecraft reorientation under multiaxis constraints

Spacecraft are required to achieve fast attitude reorientation in many space missions. However, the attitude maneuver will be limited by the bounded and pointing constraints. The presence of these complex multiaxis constraints will largely reduce the feasible attitude space and greatly degrade the solving efficiency of the planning algorithm. For the time-optimal spacecraft reorientation problem under multiaxis constraints, a new heuristic decomposition planning on the virtual domain (HDPV) method is proposed in this paper. The parameterized attitude path is first determined on the virtual domain. Then the virtual-domain-based variable-step approach is presented to rapidly check the pointing constraints and generate the sets of candidate rotational-path decomposition nodes. The composite-position heuristics is developed to select the best node during the decomposition optimization, which considers the maneuver time and connection smoothness of paths. The total continuous parameterized path under keep-out and keep-in constraints is yielded through recursive planning. Finally, the time-optimal path parameterization approach is adopted to minimize the maneuver time along the total path and generate a fast attitude trajectory under bounded constraints on the time domain. Simulation results demonstrate the high computational efficiency and good optimization effect of the proposed method.