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This 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.

First, 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.

The feasibility of the designed system is validated through online flight experiments in indoor environments with obstacles.

The proposed trajectory planning system is integrated into a quadrotor platform. It is easily implementable onboard and computationally efficient.

The 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.

Wheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments.

Given a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR.

The authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments.

In this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.

A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.

A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.

This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

The purpose of this paper is to propose an efficient algorithm for trajectory planning of unmanned aerial vehicles (UAVs) in 2D spaces. This paper has been motivated by the challenge to develop a fast trajectory planning algorithm for autonomous UAVs through mid‐course waypoints (WPs). It is assumed that there is no prior knowledge of these WPs, and their configuration is computed as in‐flight procedure.

Since the off‐line techniques cannot be applied, it is required to apply an online trajectory planning algorithm. For this reason, based on the optimal control and the geometry, each segment of trajectory is designed with respect to a local frame. The algorithm is implemented as a real‐time manner in terms of the down‐range variable.

The proposed algorithm tries to find not only a feasible trajectory (the constraint includes the maximum heading angle rate) but also an optimal trajectory (the objective locally is to minimize the length of the path). This online trajectory planning algorithm gradually produces a smooth 2D trajectory aiming at reaching the mid‐course WPs and the final target so that they are smoothly connected with each other. The mid‐course WPs are described through the given down‐range, cross‐range, and heading angle.

Based on geometrical principles, this algorithm is capable of re‐planning the trajectory as in‐flight manner, and the computational burden approaches the online capabilities for UAVs with high velocity.

This paper aims to propose a series of approaches to solve the problem of the mobile robot motion control and autonomous navigation in large-scale outdoor GPS-denied environments.

Based on the model of mobile robot with two driving wheels, a controller is designed and tested in obstacle-cluttered scenes in this paper. By using the priori “topology-geometry” map constructed based on the odometer data and the online matching algorithm of 3D-laser scanning points, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach.

The problem of motion control for a differential drive mobile robot is investigated in this paper first. At the same time, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

The purpose of this paper is to propose an automatic interpolation algorithm for robot spraying trajectories based on cubic Non-Uniform Rational B-Splines (NURBS) curves, to solve the problem of sparse and incomplete trajectory points of the head and heel of the shoe sole when extracting robot motion trajectories using structured-light 3D cameras and to ensure the robot joints move smoothly, so as to achieve a good effect of automatic spraying of the shoe sole with a 7-degree-of-freedom (DOF) robot.

Firstly, the original shoe sole edge trajectory position points acquired by the 3D camera are fitted with NURBS curves. Then, the velocity constraint at the local maximum of the trajectory curvature is used as the reference for curve segmentation and S-shaped acceleration and deceleration planning. Immediately, real-time interpolation is performed in the time domain to obtain the position and orientation of each point of the robot motion trajectory. Finally, the inverse kinematics of the anthropomorphic motion of the 7-DOF robot arm is used to obtain the joint motion trajectory.

The simulation and experiment prove that the shoe sole spraying trajectory is complete, the spraying effect is good and the robot joint movement is smooth, which show that the algorithm is feasible.

This study is of good practical value for improving the quality of automated shoe sole spraying, and it has wide applicability for different shoe sole shapes.

The purpose of this paper is to propose a new trajectory planning algorithm for industrial robots, which can let the robots move through a desired spatial trajectory, avoid colliding with other objects and achieve accurate movements. Trajectory planning algorithms are the soul of motion control of industrial robots. A predefined space trajectory can let the robot move through the desired spatial coordinates, avoid colliding with other objects and achieve accurate movements.

The mathematical expressions of the proposed algorithm are deduced. The speed control, position control and orientation control strategies are realized and verified with simulations, and then implemented on a six degrees of freedom (6-DOF) industrial robot platform.

A fixed-distance trajectory planning algorithm based on Cartesian coordinates was presented. The linear trajectory, circular trajectory, helical trajectory and parabolic trajectory in Cartesian coordinates were implemented on the 6-DOF industrial robot.

A simple and efficient algorithm is proposed. Enrich the kind of trajectory which the industrial robot can realize. In addition, the industrial robot can move more concisely, smoothly and precisely.

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

Two and one half‐dimensional (2.5D) grid maps are useful for navigation in outdoor environment or on non‐flat surface. However, little attention has been given to how to find an optimal path in a 2.5D grid map. The purpose of this paper is to develop a path‐planning method in a 2.5D grid map, which aims to provide an efficient solution to robot path planning no matter whether the robot is equipped with the prior knowledge of the environment.

A 2.5D grid representation is proposed to model non‐flat surface for mobile robots. According to the graph extracted from the 2.5D grid map, an improved searching approach derived from A* algorithm is presented for the shortest path planning. With reasonable assumption, the approach is improved for the path planning in unknown environment.

It is confirmed by experiments that the proposed planning approach provide a solution to the problem of optimal path planning in 2.5 grid maps. Furthermore, the experiment results demonstrate that our 2.5D D* method leads to more efficient dynamic path planning for navigation in unknown environment.

This paper proposes a path‐planning approach in a 2.5D grid map which is used to represent a non‐flat surface. The approach is capable of efficient navigation no matter whether the global environmental information is available at the beginning of exploration.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.