Energy‐optimal transport trajectory planning and online trajectory modification for holonomic robots

Abstract

To maximize energy efficiency, we suggest minimum-energy trajectory planning and online trajectory modification algorithms for three-wheeled omni-directional mobile robots (TOMRs). First, minimum-energy multisection trajectory planning for various constraints is performed using Pontryagin's minimum principle. Two strategies are established to formulate an efficient minimum-energy, multisection trajectory generation algorithm: an online trajectory modification and a control algorithm that maintains energy efficiency are investigated using novel heuristic functions. Compared with the conventional trajectory using the trapezoidal velocity profile, the generated transport trajectory can save energy.

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

“…They must both meet the kinematic and dynamic constraints of the robot manipulator, and the trajectory must be continuous, smooth, and impact-free within the performance requirements of the robot manipulator's components; that is, the speed and acceleration must not have sudden changes [10]. At present, the research on optimal trajectory planning mainly focuses on time-optimal trajectory planning, energy-optimal trajectory planning, impact-optimal trajectory planning, and hybrid optimal trajectory planning [11][12][13].…”

Time‐optimal L1/L2 norms optimal control for linear time‐invariant systems

“…Under certain constraint conditions, the optimal control problem finds an admissible control law to transfer the state variable from the initial state to the final state, which makes the cost function of the dynamic system reach the maximum or minimum value. In the optimal control problem, we often design different cost functions according to the actual requirements, such as the shortest time control, 1 the least fuel consumption control, 2 and the minimum energy control 3,4 …”

“…In the optimal control problem, we often design different cost functions according to the actual requirements, such as the shortest time control, 1 the least fuel consumption control, 2 and the minimum energy control. 3,4 In recent years, the sparse optimal control with L 0 -norm as the cost function has attracted enormous attention in many research fields as coding, compressed sensing, and machine learning. For example, sparse optimal control was applied to networked control systems 5 to reduce the data size of packets.…”

Characterization of motion power loss of off‐road wheeled robot in a slippery terrain

“…In this realm, a major body of research has dealt with power issue of wheeled mobile robots. Many researches in this area have concentrated their efforts on ascertainment of effect of straight and rotational transport trajectories of the robot on its power consumption (Effati et al, 2020; Jaramillo‐Morales et al, 2020; Kaplan et al, 2017; Kim & Kim, 2014a, 2014b, 2017, 2021). Overall outcome of the researches indicates that power provided for the robot is devoted for five systems (sensors, actuators, communication, control, and motion) (Kagan et al, 2020; Sadrpour et al, 2013; Stefek et al, 2020; Xie et al, 2018).…”