In the process of trajectory optimization for robot manipulator, the path that is generated may deviate from the intended path because of the adjustment of trajectory parameters, if there is limitation of end-effector path in Cartesian space for specific tasks, this phenomenon is dangerous. This paper proposes a methodology that is based on the Pareto front to address this issue, and the methodology takes into account both the multi-objective optimization of robotic arm and the quality of end-effector path. Based on dung beetle optimizer, this research proposes improved non-dominated sorting dung beetle optimizer. This paper interpolates manipulator trajectory with quintic B-spline curves, achieves multi-objective trajectory optimization that simultaneously optimizes traveling time, energy consumption, and mean jerk, proposes a trajectory selection strategy that is based on Pareto solution set by introducing the concept of Fréchet distance, and the strategy enables the end-effector to approach the desired path in Cartesian space. Simulation and experimental results validate the effectiveness and practicability of the proposed methodology on the Sawyer robot manipulator.

This article illustrates the use of unsupervised probabilistic learning techniques for the analysis of planetary reentry trajectories. A three-degree-of-freedom model was employed to generate optimal trajectories that comprise the training datasets. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. We find that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. Using the diffusion coordinates on the graph of training samples, the probabilistic framework subsequently augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path planning algorithm. In this framework, the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-time.

At present, the study on autonomous unmanned ground vehicle navigation in an unstructured environment is still facing great challenges and is of great significance in scenarios where search and rescue robots, planetary exploration robots, and agricultural robots are needed. In this paper, we proposed an autonomous navigation method for unstructured environments based on terrain constraints. Efficient path search and trajectory optimization on octree map are proposed to generate trajectories, which can effectively avoid various obstacles in off-road environments, such as dynamic obstacles and negative obstacles, to reach the specified destination. We have conducted empirical experiments in both simulated and real environments, and the results show that our approach achieved superior performance in dynamic obstacle avoidance tasks and mapless navigation tasks compared to the traditional 2-dimensional or 2.5-dimensional navigation methods.

Bipedal robots by their nature show both hybrid and underactuated system features which are not stable and controllable at every point of joint space. They are only controllable on certain fixed equilibrium points and some trajectories that are periodically stable between these points. Therefore, it is crucial to determine the trajectory in the control of walking robots. However, trajectory optimization causes a heavy computational load. Conventional methods to reduce the computational load weaken the optimization accuracy. As a solution, a variable time interval trajectory optimization method is proposed. In this study, optimization accuracy can be increased without additional computational time. Moreover, a five-link planar biped walking robot is designed, produced, and the dynamic walking is controlled with the proposed method. Finally, cost of transport (CoT) values are calculated and compared with other methods in the literature to reveal the contribution of the study. According to comparisons, the proposed method increases the optimization accuracy and decreases the CoT value.

This paper presents an open framework, through which, conventional general purpose aerial munitions can be converted into smart munitions. The retrofit consists of a smart adaptation kit (SAK) having a dedicated Guidance and Control Module (GCM). The adaptation kit along with the GCM ensures that the SAK glide optimally towards the designated target. To reduce cost, the number of control surfaces of the SAK has been kept to a bare minimum, which resulted in an under actuated system. The methodology proposed utilises the theory of gain-scheduled control and leads to an efficient procedure for the design of the controllers, which accurately track reference trajectories defined in an inertial reference frame. The paper illustrates the application of this procedure to the design of stabilisation and tracking controller for the SAK. The design phase is summarised, and the performance of the resulting controllers is assessed in simulation using dynamic model of the vehicle. Simulation results show that apart from improved circular error probable (CEP) of hitting the target, munition ballistic range has also significantly increased with the proposed modification.

This paper presents biped walking control using a library of optimal trajectories. Biped walking control is formulated as an optimal control problem. We use a parametric trajectory optimization method to find the periodic steady-state walking trajectory. As a second stage, we use Differential Dynamic Programming to generate a library of optimal trajectories and locally linear models of the optimal control law, which are used to construct a more global control law. The proposed controller is compared with a trajectory tracking controller using optimal gains. The utility and performance of the proposed method are evaluated using simulated walking control of a planar five-link biped robot.

A computational algorithm is developed to find a dynamic motion trajectory of a mobile manipulator with flexible links and joints that will allow the robot to carry a maximum load between two specified end positions. A compact form of the linearized state space dynamic equations is organized as well as constraint equations. Then, the problem of finding a maximum load carrying capacity on flexible mobile manipulators is formulated as a trajectory optimization problem.