Energy-saving Trajectory Research Articles

Energy-Efficient Speed Planning for Autonomous Driving in Dynamic Traffic Scenarios

In the field of autonomous driving, velocity planning is of paramount importance for handling dynamic obstacle scenarios. To avoid unnecessary acceleration and deceleration, self-driving vehicles need to find an energy-optimized velocity trajectory. Moreover, in complex traffic environments, the vehicle trajectory must consider the spatio-temporal coupling problem to avoid unrealistic driving paths. To address these challenges, this paper proposes a hierarchical planner that first plans the path and then performs speed planning based on the already planned path. Specifically, we focus on the energy consumption factor and use dynamic programming for speed planning while combining safety and comfort considerations. The optimal energy-saving trajectory is obtained by combining the speed profile with the optimal path. To cope with complex scenarios on real roads, we propose an adaptive trajectory adjustment strategy based on model predictive control to track by adaptively selecting tracking modes. Finally, hardware-in-the-loop experimental validation demonstrates that our proposed method significantly reduces energy consumption compared with the traditional decoupling method while ensuring that the autonomous vehicle adapts well to complex traffic scenarios.

Study on Energy-Saving Train Trajectory Optimization Based on Coasting Control in Metro Lines

With increasing energy consumption in urban rail transit systems, researchers have paid significant attention to energy-saving train control. In this paper, we propose an effective train trajectory optimization method to reduce the energy consumption based on coasting control, in which coasting control regimes are added to balance running time and energy consumption. For better determining the starting points of coasting control regimes, the whole train running process is divided into several subintervals. Then, aiming to achieve energy efficiency, coasting regimes are added to the subintervals with high energy-saving effects, in which more energy consumption can be reduced with the same running time addition. Based on this, a coasting control method is proposed to generate energy-saving trajectories considering train dynamics, safety, and punctuality. In addition, the proposed method can solve the multisection energy-saving train trajectory optimization problem to obtain optimal running time schemes and related trajectories. Finally, numerical examples based on one of the Beijing metro lines are implemented to verify the effectiveness of the proposed method. The results show that, for the single-section train control problem, the proposed coasting control algorithm can achieve significant energy-saving effects compared to the practical trajectory and calculate energy-saving trajectory in shorter computation times compared to the dynamic programming method. Meanwhile, for the multisection train control problem, energy consumption can be further reduced by optimizing trajectories and running times integratedly.

Energy-saving trajectory optimization of a fluidic soft robotic arm

Soft robots have attracted increasing attention due to inherent environmental adaptability and reliable human–machine interaction. However, there is relatively few research about their energy efficiency which acts as an important indicator. This paper firstly derives the time-domain model of energy consumption for a fluidic soft robotic arm. With the introduction of forward kinematics, the trajectory of the soft robotic arm is optimized for energy saving under motion constraints and solved using interior point method. A series of experiments are implemented to evaluate the performance of the proposed model and the optimized trajectory. The results show that the time-based model can capture the dynamical energy behaviors of the fluidic soft actuators under various motions. It is also found that the energy consumption of the soft robotic arm is effectively reduced when the trajectory optimization is applied. This work can provide further reference to the energy-based optimization of the soft robots.

Energy-Saving Trajectory Planning for Robotic High-Speed Milling of Sculptured Surfaces

In the field of sculptured surfaces machining, the robot trajectory planning, under high-order complex constraints, aiming at minimizing energy or time, is always a challenge. The complexity of curvature characteristics of sculptured surfaces and the nonlinearity of relevant constraints are the main reasons. This article proposes an efficient planning method of minimum-energy robot trajectory, for high-speed machining of sculptured surfaces. First, the energy characteristic model of the robot machining system (RMS) is established, to acquire the energy-optimal feedrate, under velocity constraints, to use in the subsequent trajectory planning. Next, a trajectory planning model, with complex constraints, is developed. The proposed method transforms the original trajectory planning into a minimal modification of the initial objective-optimal B-spline feedrate curve (BFC). Furthermore, two main coupling problems, which influence the minimal change of curves, in the direct evolution-based BFC modification, are addressed. Based on the derived solutions, a novel modification algorithm of BFC, with a callback mechanism (CBM), is proposed. Finally, the performance of the proposed method and the specific algorithm is validated by two case studies. Results show that the proposed method can significantly improve the efficiency of robot trajectory planning, aiming at minimum energy, while exhibiting excellent performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by how to automatically and effectively acquire the energy-saving trajectory of a robotic system, executing sculptured surface machining. This article suggests a new approach that transforms the original trajectory optimization into a minimal modification of the initial energy-optimal BFC, obtained under velocity constraints. This approach calculates the complex energy formula only once. A CBM, for constraint-based BFC modification, is proposed, to revise the redundant reduction of the feedrate. The proposed modification algorithm significantly decreases the detrimental effect of two coupling problems, on the adjustment of the BFC. Results show that the computational efficiency of the proposed method is significantly superior to the one of the direct optimization method. The performance of the proposed modification algorithm is obviously advanced compared to the existing B-spline evolution algorithm. In blade machining, after optimizing the trajectory using the proposed method and algorithm, both energy and time decrease by more than 45% compared to the conservative feed. The proposed algorithm improves significantly the trajectory compared to the prior art algorithm. Technologists benefit from reduced planning time and improved machining performance. However, incorporation into a CAM or manufacturing system has not yet been realized and remains a significant work to be done in the future.

Energy-saving trajectory planning by using Fourier sine series and tracking control for a mechatronic elevator system

In this paper, Lagrangian method is proposed to formulate dynamic model for a mechatronic elevator system, which includes mechanical and electrical parts. Lagrange equations, i.e. the torque balance equation and voltage balance equation, are derived from the kinetic energy, potential energy and virtual work of the mechatronic system. From the dynamic equations, the energy balance equation is obtained including electromagnetic energy, electrical dissipation energy, mechanical kinetic energy, mechanical dissipation energy, and the mechanical potential energy. The minimum-energy trajectory by using Fourier sine series (FSS) and power series are found by real-coded genetic algorithm. From simulation results, it is found that FSS with less order can find the minimum input energy more quickly than the power series. The four-degree (4-D) FSS trajectory is given as a reference one for the adaptive tracking control. It is found that the proposed adaptive controller has good tracking performance for the 4-D FSS angular displacement and velocity.

Influence of data clouds fusion from 3D real-time vision system on robotic group dead reckoning in unknown terrain

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain. The navigation scheme uses the benefits of the original real-time technical vision system &#x0028 TVS &#x0029 based on a dynamic triangulation principle. The method uses TVS output data with fuzzy logic rules processing for resolution stabilization. Based on previous researches, the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group. According to the comparative analysis of approximation methods, in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory. The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.

Energy-saving trajectory planning for robots using the genetic algorithm with assistant chromosomes

Fossil fuel depletion and global warming are becoming increasingly important problems. Many trajectory plans for robot manipulators are developed by prioritizing operation efficiency, such as operating time and controllability, without considering energy consumption. In this study, the energy consumption problem is examined. This study discusses the application of a genetic algorithm (GA) to solve the problem of minimizing the energy consumption of a robot manipulator with nonlinear friction in the joints. The GA can search a wide area for an optimal solution; however, a long computation time is required. A gradient method can be used to quickly find a solution; however, the solution has a high probability of being a local optimum. This paper proposes a method that combines a gradient method and GA to quickly determine an optimal solution. In addition, the validity of the proposed method is examined.

Food availability drives plastic self-repair response in a basal metazoan- case study on the ctenophore Mnemiopsis leidyi A. Agassiz 1865

Many marine invertebrates including ctenophores are capable of extensive body regeneration when injured. However, as for the invasive ctenophore Mnemiopsis leidyi, there is a constant subportion of individuals not undergoing whole body regeneration but forming functionally stable half-animals instead. Yet, the driving factors of this phenomenon have not been addressed so far. This study sheds new light on how differences in food availability affect self-repair choice and regeneration success in cydippid larvae of M. leidyi. As expected, high food availability favored whole-body regeneration. However, under low food conditions half-animals became the preferential self-repair mode. Remarkably, both regenerating and half-animals showed very similar survival chances under respective food quantities. As a consequence of impaired food uptake after injury, degeneration of the digestive system would often occur indicating limited energy storage capacities. Taken together, this indicates that half-animals may represent an alternative energy-saving trajectory which implies self-repair plasticity as an adaptive trade-off between high regeneration costs and low energy storage capacities. We conclude that self-repair plasticity could lead to higher population fitness of ctenophores under adverse conditions such as in ships’ ballast water tanks which is postulated to be the major vector source for the species’ spreading around the globe.

A study on the vibration suppression of the manipulator with flexible element

For the rapid motion and the saving energy of transport by the manipulator, it requires a study for lighter mechanisms such as flexible link. In this report, a flexible link is modeled with rigid body connected by three springs. And, the equations of motion of manipulator driven by two motors are derived in consideration of the deflection of flexible element and the characteristics of driving source. For the case to transport the object from the initial position to the desired position by the manipulator, the energy-saving trajectory influenced by vibration is calculated by the iterative dynamic programming method, and the effectiveness of the control method is evaluated by the numerical simulation. Furthermore, the effectiveness of the numerical simulation is confirmed by the experiment of a flexible link which is driven by a single motor with energy-saving trajectory.

Energy-saving trajectory planning for an inverse ball drive robot with Mecanum wheels

This paper proposes a new energy-saving trajectory planning method for a robot with an inverse ball driven by Mecanum wheels. The slip due to the structure of the ball drive robot is used to formulate the energy consumption. The measured torque pattern is used as a basis to save energy through a proper path-planning process. To change the path of the robot, an improved path-planning algorithm is used to generate an energy-efficient path. Simulations and experiments were performed to demonstrate the efficiency of the approach.

The point-to-point multi-region energy-saving trajectory planning for a mechatronic elevator system

A mechatronic elevator system driven by a permanent magnet synchronous motor (PMSM) is completely modeled by the mechanical and electrical equations. The electrical energy equation, including input, dissipation, magnetic and kinetic energies, is formulated for energetic analysis. The adjusting fraction, defined as the flight time from null to maximum acceleration with respect to the total acceleration time, is optimized by the self-learning particle swarm optimization (SLPSO) method in minimizing the input absolute electrical energy (IAEE). In this paper, multi-region trajectories of high-degree polynomials with constraints of maximum acceleration and velocity are planned, and the flight time and the IAEE are compared numerically. The main contribution of this paper is to propose a methodology in the point-to-point (PTP) multi-region energy-saving trajectory planning for any mechatronic system.

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

The cooperative energy-efficient trajectory planning for multiple high-speed train movements is considered in this paper. We model all the high-speed trains as the agents that can communicate with others and propose a local trajectory planning control model using the Model Predictive Control (MPC) theory. After that we design an online distributed cooperative optimization algorithm for multiple train trajectories planning, under which each train agent can regulate the trajectory planning procedure to save energy using redundancy trip time through tuning ACO’s heuristic information parameter. Compared to the existing literature, the vital distinctions of our work lies not only on the online cooperative trajectory planning but also on the distributed mechanism for multiple high-speed trains. Experimental studies are given to illustrate the effectiveness of the proposed methods with the practical operational data of Wuhan-Guangzhou High-speed Railway in China.

Adaptive Tracking Control of a PMSM-Toggle System with a Clamping Effect

This paper discusses an adaptive control (AC) designed to track an energy-saving point-to-point (ESPTP) trajectory for a mechatronic system, which is a toggle mechanism driven by a permanent magnet synchronous motor (PMSM) with a clamping unit. To generate the PTP trajectory, we employed an adaptive real-coded genetic algorithm (ARGA) to search for the energy-saving trajectory for a PMSM-toggle system with a clamping effect. In this study, a high-degree polynomial was used, and the initial and final conditions were taken as the constraints for the trajectory. In the ARGA, the parameters of the polynomials were determined by satisfying the desired fitness function of the input energy. The proposed AC was established by the Lyapunov stability theory in the presence of a mechatronic system with uncertainties and the impact force not being exactly known. The trajectory was tracked by the AC in experimental results so as to be compared with results produced by trapezoidal and high-degree polynomials during motion.

Energy-saving trajectory planning for a toggle mechanism driven by a PMSM

In this paper, energy-saving trajectories are planned for a toggle mechanism driven by a permanent magnet synchronous motor (PMSM). The point-to-point (PTP) trajectory is described by a high-degree polynomial, which satisfies the end conditions of displacement, velocity, acceleration and jerk at the initial and final times. The real-coded genetic algorithm (RGA) method is employed to obtain the mechatronic system’s parameters, and to determine the coefficients of the polynomial, and its fitness function is the inverse of various input energies. The numerical simulations and experiments are compared among several degrees’ polynomials during the whole operation motion. Finally, it is found that the input absolute electrical energy (IAEE) occurs when the highest-degree polynomial is chosen. From the percentage of relative error with respect to the absolute input energy of a 7-degree polynomial, it is found that the percentage of relative error achieves 32% as the degree is 24 with enough for minimum input energy degree. The proposed methodology described in this research has the contribution. It can be applied to any mechatronic system which is required to design a minimum-energy point to point trajectory.

Energy-saving trajectory planning for a toggle mechanism driven by a PMSM

This paper proposes a methodology to plan an energy-saving trajectory for a toggle mechanism by a permanent magnet synchronous motor (PMSM). The system is first identified by the numerical methods. The trajectory is described by a high-degree polynomial, which is a point-to-point (PTP) function and satisfies the end conditions of displacement, velocity, acceleration and jerk at the initial and final times. The real-coded genetic algorithm (RGA) is employed to determine the coefficients of the polynomial with the fitness function, which is the inverse of the absolute value of the input electric energy. In this paper, some discussions for planning an energy-saving trajectory are made, and three cases of the fitness functions are used.

Collision-Free and Energy-Saving Trajectory Planning for Large-Scale Redundant Manipulator Using Improved PSO

The large-scale boom system, such as the five-arm concrete pump truck with the arm length of 36–65 meters, usually operates in an unknown dynamic outdoor environment. The motion safety and the energy consumption are thus the two vital measurements to the effectiveness of the trajectory planning for the large-scale boom system. Due to the redundancy of the large-scale boom system and some drawbacks of the original particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented to solve the inverse kinematic problem of the redundant large-scale boom system. By the improved PSO algorithm, the energy-saving trajectory planning of the large-scale boom system that operates in a workspace without obstacles and with obstacles is optimized, which considers different important degrees of the subgoals, respectively. The optimal results from the simulation study and the practical application verify the effectiveness of the proposed planning strategy. At the same time, the performance of the improved strategy is compared with that of the traditional, and the superiority is further demonstrated.