Trajectory Planning Approach Research Articles

Constrained Motion Control of an Independent Metering System with Uncertain Loads

Independent metering systems (IMSs) have been applied and researched in mobile machinery due to their advantages of reduced throttling energy losses and remarkable advances under negative load through decoupling actuator throttling control. Although IMSs have the control flexibility to deal with negative workloads, the control performance of the IMSs is challenged by uncertain loads in mobile operations, limiting the control accuracy. In addition, if the motion reference is improperly specified and exceeds the constraints, the pressure of the actuator may oscillate significantly and potentially result in control instability. In this study, a constrained adaptive robust control strategy is proposed for an IMS. An adaptive robust control strategy is designed for the meter-in and meter-out throttling to achieve precision motion control despite the nonlinearities and uncertainties of the electro-hydraulic IMS. The value of the uncertain workload is estimated in real-time and utilized in the model-based controller to improve control accuracy. In addition, a constrained trajectory planning approach is presented to handle out-of-constraint references and ensure motion tracking performance. This effectively prevents pressure fluctuations caused by the inappropriate reference.

Trajectory planning and control for autonomous vehicles: a “fast” data-aided NMPC approach

A huge research effort is being spent worldwide by automotive companies and academic institutions for developing vehicles with high levels of autonomy, ranging from advanced driving-assisted systems to fully automated vehicles. Nonlinear Model Predictive Control (NMPC) has the potential to become a key technology in this context, thanks to its capability to deal with linear and nonlinear systems, manage physical constraints and satisfy multi-objective performance criteria. However, NMPC is based on the on-line solution of a nonconvex optimization problem and this operation may require a high computational cost, compromising its real-time implementation. In this paper, a “fast” data-aided NMPC approach is developed, aimed at trajectory planning and control for autonomous vehicles. In particular, a Set Membership approximation method is used to derive from data tight bounds on the optimal NMPC control law. These bounds are used to restrict the search domain of the underlying NMPC optimization process, allowing a significant reduction of the computation time. The proposed NMPC trajectory planning and control approach is tested in simulation and compared with other state-of-the-art methods, considering different road scenarios.

Trajectory Planning of UAV-Enabled Data Uploading for Large-Scale Dynamic Networks: A Trend Prediction Based Learning Approach

The unmanned aerial vehicle (UAV) enabled communication technology is regarded as an efficient and effective solution to provide emergency data uploading for some special cases where existing cellular infrastructures cannot provide reliable services to large-scale ground users (GUs). In the face of large-scale dynamic networks where the number and location of GUs change all the time, to maximize the throughput of data upload tasks under the premise of meeting the requirements of fairness, QoS and energy consumption, we propose a UAV trajectory planning approach based on deep reinforcement learning in combination with the prediction of GUs' movement trend in future time slots. Simulation results validate that our proposed approach converges more quickly and achieves better performance in dynamic networks.

Optimal trajectory planning of the industrial robot using hybrid S-curve-PSO approach

PurposeThe paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations.Design/methodology/approachThe proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory.FindingsThe proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance.Originality/valueThe novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.

Holistic Approach for Aircraft Trajectory Optimization Using Optimal Control

This paper deals with an optimal control approach for commercial aircraft trajectory planning, focusing on the vertical path and the minimization of the fuel burned. The main contribution is the optimization of the whole trajectory, also called the mission, without prior separation into different flight phases (climb, cruise, and descent), contrary to traditional approaches that consider the flight phases sequentially. The proposed optimal control problem (OCP) is formulated and then solved using a direct collocation method. Initial results yield optimal trajectories with a gradual climb during the cruise (climb cruise), which correspond to theoretical trajectories that minimize fuel consumption. Furthermore, a penalization is introduced to ensure vertical profiles featuring horizontal cruise levels on so-called flight levels, compliant with current air traffic management regulations. The use of direct methods to address this OCP induces large nonlinear optimization problems that are solved using an interior point method. This methodology is likely to lead to poor local optima, but a simple multistart heuristic shows that the solutions found for standard problems appear to be globally optimal. Such an approach does not need to impose a priori the cruise flight levels and is suitable for commercial aircraft flight planning purposes. It leads to fuel saving and subsequently to important improvements against environmental impact by reducing emissions.

Formation Control for Connected and Autonomous Vehicles Based on Distributed Consensus Embedded With Risk Potential Field

Cooperative control for multiple vehicles is a promising technology with the capability to improve traffic efficiency and fuel savings. Given its potential for both commercial and military applications, multiple unmanned vehicle formation has attracted considerable attention recently. In this paper, the use of formation control for connected and autonomous vehicles was explored and a novel distributed formation control approach was proposed. To begin, the evolution mechanism of multi-lane formation was investigated and a formation transition model based on a finite state machine was constructed. A bi-level formation control scheme was then proposed; this framework’s upper and lower levels were used to perform trajectory planning and MPC-based control, respectively. A novel trajectory planning approach was constructed by combining the distributed consensus algorithm and the potential field method. Moreover, additional acceleration constraints were imposed on the trajectory planning algorithm. Finally, three scenarios were designed to validate the proposed formation control algorithm using Webots. The results illustrate that a formation deployed with the proposed formation control algorithm can handle abnormal situations and realize consensus within 12s.

Piecewise Trajectory and Angle Constraint Based Fractional-order Sliding Mode Control

This paper proposes an approach for piecewise trajectory planning and fractional-order sliding-mode guidance of a two-stage launched miniature munition with the laser beam riding steered guidance technology. In the proposed approach, firstly, the polynomial description is used as an expected trajec- tory. Then consider the random external disturbances/parametric uncertainties and other factors deteriorate the tracking control performances of the miniature munition. To reduce the chatter- ing caused by modeling errors and external disturbances and achieve fast speed and high accuracy tracking performances for miniature munition, based on adaptive multi-power reaching strategy, an integrated guidance and control (IGC) method with the fractional-order sliding-mode guidance law (FOSMGL) is introduced. The designed fractional-order sliding surfaces ultimately and successively allow both the trajectory and the attitude angle converge to expected value in a finite time. In addition, considering the case of a moving target, a feedfor- ward compensation mechanism, which decreases the lag of the guidance system, is imposed using an additional compensated term obtained from the attitude angle error. The convergence and reachability of the proposed algorithm in finite-time are analyzed using the Lyapunov function method. The validity and practicability of the proposed method are verified by a numerical example.

A Tube-MPC Approach to Autonomous Multi-Vehicle Racing on High-Speed Ovals

Autonomous vehicle racing has emerged as vibrant, innovative technology development, demonstration platform in recent years. Universities, companies demonstrate their achievements on various vehicles - from 1:10th to full-scale prototypes. One of those platforms is the Dallara AV-21, and the spec-vehicle for the Indy Autonomous Challenge. This paper outlines the robust model predictive control (MPC) concept used within the software stack of the TUM Autonomous Motorsport team. It is based on a simplified friction-limited point mass model and a set of low-level feedback controllers. The remaining model uncertainties are managed via introducing a constraint-tightening approach based on a Tube-MPC approach. In contrast to classical tracking controllers, the optimization problem is formulated to freely optimize the trajectory while staying within certain maximum deviations of the reference. This approach allows to rely on a coarse output of the trajectory planning approach while maintaining smoothness requirements in steering, throttle, and brake actuation. The paper highlights the advantages of the proposed robust reoptimization concept compared to pure tracking formulations. It showcases the performance compared to a classical LQR controller and an MPC, which utilizes a vehicle model with a more sophisticated tire model. The controller achieved a top speed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{265 kmh}^{-1}$</tex-math></inline-formula> and lateral accelerations 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">${\text{21 ms}}^{-2}$</tex-math></inline-formula> during a two-vehicle competition involving dynamic overtaking maneuvers on the Las Vegas Motor Speedway, a famous racetrack with turns banked 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">$20^{\circ }$</tex-math></inline-formula> .

Chase and Track: Toward Safe and Smooth Trajectory Planning for Robotic Navigation in Dynamic Environments

In this article presents a trajectory planning approach toward safe and smooth robot motion in dynamic environments. We develop a hierarchical planning framework with a global planner generating the shortest path between the robot and the navigation target. Specially, a virtual target (VT) is set to run on the global path with a designed velocity. At the local level, the robot chases the VT and tracks the global path when traveling through the dynamic environment. We employ the model predictive control (MPC) framework for the local path generation. In particular, the prediction horizon of the MPC is adaptively changed concerning the distance between the robot and the VT. It implicitly reflects the crowdedness of the environment, which helps reduce the environmental uncertainty. Besides, we develop an event-triggered mechanism that executes the local plan aperiodically to release the computational burden. Based on the local chasing and tracking performance, we develop a global path replanning scheme in response to the untraversable area emerging in the dense environment. The developed framework is validated through extensive experiments in dynamic environments, demonstrating that the robot can reach the target faster and showcase a safer and smoother trajectory in the navigation.

An Approach for Elliptical Trajectory Planning with Vertical Straight Line Segments of Pick‐and‐Place Robot Operation with Height Clearance

This study deals with elliptical trajectory planning with vertical straight‐line segments for pick‐and‐place robot operation with height clearance. It is significant for trajectory planning for the pick‐and‐place operation with the different height clearance between the picking point and placing point, or with the height clearance for picking and placing the product in the box. In this study, we propose an optimal asymmetric bisected elliptical path with two vertical straight line segments and a method to generate a motion profile suited to geometry of the optimal asymmetric bisected elliptical path based on the radius of curvature. The simulation result demonstrates that the proposed trajectory planning approach enables to reduce workspace and cycle period of the pick‐and‐place operation by minimizing the length and height of the path and guarantees continuity and smoothness of velocity, acceleration and jerk, and results in smoothness of working action of robot actuators.

Set-Based Intent-Expressive Trajectory Planning and Intent Estimation

This letter proposes a novel approach to intent-expressive motion planning and intent estimation for agents/robots with uncertain discrete-time affine dynamics. In contrast to the more commonly considered stochastic settings, our intent-expressive trajectory planning approach is set-based and leverages the active model discrimination framework for designing optimal inputs to attain certain target/goal states, while allowing an observer/teammate to clearly infer the intended goal based only on observations of a partial trajectory before it has reached its target/goal state, despite worst-case uncertainties. Further, in tandem with the planning algorithm, we also propose an intent estimation algorithm that can be used by the observer/teammate to determine the intended goal from observations of a noisy, partial trajectory.

Trajectory planning method with grinding compensation strategy for robotic propeller blade sharpening application

A propeller is the main part of a water-borne carrier's drive system. Geometrical shape accuracy and sharpness on the cutting side of the propeller's blade play vital roles to minimize fluid resistance and improve transmission efficiency. For Jet Ski and small boats, the significant thickness variation along the blade edge is embedded in the lost-wax casting process. The automatic trajectory planning for the robotic propeller blade's sharpening operation is challenging due to its complicated curved shape and the tiny gaps between two connected blades. The trajectory planning for robotic grinding based on the edge thickness distribution of the standard model may result in over- or under-cutting due to the highly inconsistent shape deformation caused by the casting process. To address the aforementioned challenges, this study proposes a fully automated robot grinding system. The proposed method combines a novel trajectory planning approach based on a parametric and point cloud model with a 3D scanning on edge-based trajectory adjustment approach. Moreover, to improve the grinding accuracy, a laser-vision sensor-based detection algorithm is also developed for grinding compensation to counteract the geometrical variation caused by the casting process. The effectiveness of the proposed system is validated through numerous experimental trials using a 6-DOF industrial robot. The research findings showcase that the proposed technique is feasible, stable, and effective for robotic propeller blade edge sharpening operations.

Motion Planning and Cooperative Manipulation for Mobile Robots With Dual Arms

With large work space and dexterous manipulation capability, mobile robots with dual arms can be used in complex operation scenes such as active detection, disassembly, spraying, cleaning and assembly, is a promising direction for robot development. In this paper, a hybrid control approach is proposed for a mobile robot with dual arms to fulfil an explosive disposal task. Firstly, the motion planning for mobile base and trajectory planning for dual arms are developed, and a hierarchical task planner is designed using the finite state machine to make the robot system to deal with events sensed by active vision. A rapidly-exploring random tree-based motion planning is developed in task space for the mobile base to realize dynamic collision avoidance, while the trajectory planning approach is proposed in the framework of a dual-arm master-slave coordinated mechanism. Next, for mobile robots to complete complex task such as explosive disposal using two dexterous hands, the impedance control approach with slippage detection is developed by considering slippage tendency and slippage intensity to produce a stable in-hand manipulation. Furthermore, the Faster R-CNN is employed to determine the grasping region for robot manipulation through object detection and learning. Finally, the explosive disposal scene is designed to justify the effectiveness and good performance of the proposed methods.

Robot-assisted ultrasonic impact strengthening strategy for aero-engine blades

• Robot-assisted UIT system is developed for aero-engine blades. • A novel non-interference trajectory planning approach is proposed. • Strengthening system can operate without collision even in restricted space. • The proposed strategy can improve fatigue performance of TA19 blades. As a vital component of aero-engine, blade is susceptible to damage from long-term service under cyclic loads. Ultrasonic impact treatment (UIT) can improve the mechanical properties of the material surface. However, applying UIT onto aero-engine blade remains a challenge due to the complex structure. This study investigates robot-assisted ultrasonic impact strengthening strategy for the complex surface in the restricted space. On the one hand, a novel robot-assisted UIT system is developed for a wide range of complex surface features. On the other hand, a non-interference trajectory planning method is introduced for complex surfaces in the restricted space of the blade. The breadth-first search (BFS) strategy based on the criterion of the induced ray is proposed for interference inspecting and optimizing the pose of the UIT tool. Experiments are conducted on real aero-engine blades. The results of numerical and experimental case studies have demonstrated that the proposed strategy can effectively cover the fatigue vulnerable areas without collision. Moreover, compressive residual stresses are generated both on the pressure and suction surface, and the fatigue limit of the UIT blades is improved by 26% as compared with the as-received blades.

A Trajectory Planning Approach for Delta Robots Considering Both Motion Smoothness and Dynamic Stress

Abstract By considering both motion smoothness and dynamic stress, a trajectory planning method for delta robots, with the goal of determining the two optimal normalized time factors that dominate the motion path in operation space, is presented in this article. First, based on the semianalytical elastodynamics model of parallel robots, which considers the compliance of the limbs and joints, a dynamic stress model of the kinematic chain was built. Two indices were proposed to reflect the motion smoothness and dynamic stress. A sensitivity analysis and an optimization of the normalized time factors for a modified fifth-order B-spline approach were conducted in isight and matlab. The results show that the two normalized time factors have important impacts on the motion smoothness and dynamic stress. A comparison showed that the trajectory planning approach based on the modified fifth-order B-spline reduced the dynamic stress while improving the motion smoothness. The approach proposed in this work can also be applied in trajectory planning for other parallel or hybrid robots.

Digital fabrication of ribbed concrete shells using automated robotic concrete spraying

Doubly-curved ribbed concrete shells are a materially efficient means of spanning large areas such as roofs and floors. However, the fabrication of such structures poses challenges in terms of formwork manufacturing as well as material deposition. This has led to their decline compared to more prismatic shapes such as flat slabs which can be manufactured more economically. This paper presents a novel fabrication process called Automated Robotic Concrete Spraying (ARCS) by which glass fibre reinforced concrete (GFRC) is sprayed onto a curved formwork to create thin shell components of variable thickness. The trajectory planning and generation algorithm developed and implemented in ARCS to create such components is presented. Two sets of prototype shells were fabricated: one which forms the segments of a larger structural floor demonstrator and another consisting of a single component with deep ribs on a thin shell. The sequencing used to generate the spray paths for each component is outlined, with each prototype using two different strategies to add ribs onto the fabricated shells. While the fabrication process has been used in conjunction with a pin-bed mould actuating flexible formwork to create the spraying surface, the trajectory planning approach is adaptable enough such that any formwork can be utilised. Combined with the output speed of material deposition, ARCS offers the potential to enable mass production and customisation of doubly-curved ribbed structural concrete shells of variable thickness as an industrial process at an architectural scale. • A new fabrication process to create curved concrete shells with ribs is presented. • A robotic trajectory planning algorithm leveraging isolines of geodesics was formulated. • Two sets of ribbed concrete shell prototypes were fabricated using the process developed. • The high throughput of the fabrication process lends itself to offsite mass production.

A potential field-based trajectory planning and tracking approach for automatic berthing and COLREGs-compliant collision avoidance

A potential field-based Extended Dynamic Window Approach (EDWA) is proposed to fulfill the real-time trajectory planning for automatic berthing. To avoid falling into a local minimum, an nonuniform Theta* (NT*) is introduced to search the global path considering the risk of obstacles. Three potential fields are established: the attractive potential field guides the unmanned surface vehicle (USV) along its global path; the grid-based repulsive potential field is designed to keep the USV away from shores; and the COLREGs-compliant repulsive potential field is to avoid collision from other USVs. Taking the dynamic constraints of the USV into account, EDWA generates predicted trajectories of both constant force and deceleration phases. The objective function is subsequently produced to select them optimally according to the established potential fields. Finally, a linear MPC is presented to track the designed berthing trajectory considering the real-time requirement. The whole automatic berthing scheme is simulated on the Dalian port. The results indicate that the USVs avoid the potential collisions in accordance with COLREGs, and successfully reach their desired berthing spots with reasonable small deviations.

Adaptive Nonlinear Model Predictive Horizon Using Deep Reinforcement Learning for Optimal Trajectory Planning

This paper presents an adaptive trajectory planning approach for nonlinear dynamical systems based on deep reinforcement learning (DRL). This methodology is applied to the authors’ recently published optimization-based trajectory planning approach named nonlinear model predictive horizon (NMPH). The resulting design, which we call ‘adaptive NMPH’, generates optimal trajectories for an autonomous vehicle based on the system’s states and its environment. This is done by tuning the NMPH’s parameters online using two different actor-critic DRL-based algorithms, deep deterministic policy gradient (DDPG) and soft actor-critic (SAC). Both adaptive NMPH variants are trained and evaluated on an aerial drone inside a high-fidelity simulation environment. The results demonstrate the learning curves, sample complexity, and stability of the DRL-based adaptation scheme and show the superior performance of adaptive NMPH relative to our earlier designs.

Velocity Constraints Based Approach for Online Trajectory Planning of High-Speed Parallel Robots

High-speed parallel robots have been extensively utilized in the light industry. However, the influence of the nonlinear dynamic characteristics of high-speed parallel robots on system’s dynamic response and stable operation cannot be ignored during the high-speed reciprocating motion. Thus, trajectory planning is essential for efficiency and stability from pick-and-place (PAP) actions. This paper presents a method for planning the equal-height pick-and-place trajectory considering velocity constraints to improve the PAP efficiency and stability of high-speed parallel robots. The velocity constraints in the start-and-end points can reduce vibration from picking and placing, making the trajectory more suitable to complex beltline situations. Based on velocity constraints, trajectory optimization includes trajectory smoothness and joint torque to optimize cycle time is carried out. This paper proposes an online trajectory optimization solution. By using back propagation (BP) neural networks, the solution is simplified and can be solved in real-time. Simulation and experiments were carried out on the SR4 parallel robot. The results show that the proposed method improves the efficiency, smoothness, and stability of the robot. This paper proposes an online trajectory planning method which is velocity constraints based and can improve the efficiency and stability of high-speed parallel robots. The work of this research is conducive to finely applying high-speed parallel robots.

A Backstepping Approach to Nonlinear Model Predictive Horizon for Optimal Trajectory Planning

This paper presents a novel trajectory planning approach for nonlinear dynamical systems; a multi-rotor drone, built on an optimization-based framework proposed by the authors named the Nonlinear Model Predictive Horizon. In the present work, this method is integrated with a Backstepping Control technique. The goal is to remove the non-convexity of the optimization problem in order to provide real-time computation of reference trajectories for the vehicle which respects its dynamics while avoiding sensed static and dynamic obstacles in the environment. Our method is applied to two models of multi-rotor drones to demonstrate its flexibility. Several simulation and hardware flight experiments are presented to validate the proposed design and demonstrate its performance improvement over earlier work.