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Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment.

First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively.

Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper.

Only one step climbing motion planning is considered in this method.

With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint.

The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

The purpose of this paper is to characterise the TWIN-RRT* algorithm which solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory and retrieves feasible, “low cost”, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinematic constraints in a 2D environment.

It was designed to work equally well with higher degree of freedom agents in different applications. It provides a practical implementation of feasible and fast planning, namely where a closed loop is required. Initial and final configurations are allowed to be exactly the same.

The TWIN-RRT* algorithm computes an efficient path for a single agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*.

It uses efficiency as cost function, which can be adjusted to the requirements of any given application. TWIN-RRT also shows compliance with kinematic constraints.

The practical application where this work has been used consists of an autonomous mobile robot that picks up golf balls in a driving range. The multiple targets are the golf balls and the optimum path is a requirement to reduce the time and energy to refill as quickly as possible the balls dispensing machine.

The new random sampling algorithm – TWIN-RRT* – is able to generate feasible efficient paths towards multiple targets retrieving closed-loop paths starting and finishing at the same configuration.

Streamlining automated processes is currently undertaken by developing optimization methods and algorithms for robotic manipulators. This paper aims to present a new approach to improve streamlining of automatic processes. This new approach allows for multiple robotic manipulators commonly found in the industrial environment to handle different scenarios, thus providing a high-flexibility solution to automated processes.

The developed system is based on a spatial discretization methodology capable of describing the surrounding environment of the robot, followed by a novel path-planning algorithm. Gazebo was the simulation engine chosen, and the robotic manipulator used was the Universal Robot 5 (UR5). The proposed system was tested using the premises of two robotic challenges: EuRoC and Amazon Picking Challenge.

The developed system was able to identify and describe the influence of each joint in the Cartesian space, and it was possible to control multiple robotic manipulators safely regardless of any obstacles in a given scene.

This new system was tested in both real and simulated environments, and data collected showed that this new system performed well in real-life scenarios, such as EuRoC and Amazon Picking Challenge.

The new proposed approach can be valuable in the robotics field with applications in various industrial scenarios, as it provides a flexible solution for multiple robotic manipulator path and motion planning.

In the running of multiple automated guided vehicles (AGVs) in warehouses, delay problems in motions happen unavoidably as there might exist some disabled components of robots, the instability of networks and the interference of people walking. Under this case, robots would not follow the designed paths and the coupled relationship between temporal and space domain for paths is broken. And there is no doubt that other robots are disturbed by the ones where delays happen. Finally, this brings about chaos or even breakdown of the whole system. Therefore, taking the delay disturbance into consideration in the path planning of multiple robots is an issue worthy of attention and research.

This paper proposes a prioritized path planning algorithm based on time windows to solve the delay problems of multiple AGVs. The architecture is a unity consisting of three components which are focused on scheduling AGVs under normal operations, delays of AGVs, and recovery of AGVs. In the components of scheduling AGVs under normal operations and recovery, this paper adopts a dynamic routing method based on time windows to ensure the coordination of multiple AGVs and efficient completion of tasks. In the component for scheduling AGVs under delays, a dynamical prioritized local path planning algorithm based on time windows is designed to solve delay problems. The introduced planning principle of time windows would enable the algorithm to plan new solutions of trajectories for multiple AGVs, which could lower the makespan. At the same time, the real-time performance is acceptable based on the planning principle which stipulates the parameters of local time windows to ensure that the computation of the designed algorithm would not be too large.

The simulation results demonstrate that the proposed algorithm is more efficient than the state-of-the-art method based on homotopy classes, which aims at solving the delay problems. What is more, it is validated that the proposed algorithm can achieve the acceptable real-time performance for the scheduling in warehousing applications.

By introducing the planning principle and generating delay space and local adjustable paths, the proposed algorithm in this paper can not only solve the delay problems in real time, but also lower the makespan compared with the previous method. The designed algorithm guarantees the scheduling of multiple AGVs with delay disturbance and enhances the robustness of the scheduling algorithm in multi-AGV system.

This paper aims to present a distributed Bayesian approach with connectivity maintenance to manage a multi-agent network search for a target on a two-dimensional plane.

The Bayesian framework is used to compute the local probability density functions (PDFs) of the target and obtain the global PDF with the consensus algorithm. An inverse power iteration algorithm is introduced to estimate the algebraic connectivity λ₂ of the network. Based on the estimated λ₂, the authors design a potential field for the connectivity maintenance. Then, based on the detection probability function, the authors design a potential field for the search target. The authors combine the two potential fields and design a distributed gradient-based control for the agents.

The inverse power iteration algorithm can distributed estimate the algebraic connectivity by the agents. The agents can efficient search the target with connectivity maintenance with the designed distributed gradient-based search algorithm.

Previous study has paid little attention to the multi-agent search problem with connectivity maintenance. Our algorithm guarantees that the strongly connected graph of the multi-agent communication topology is always established while performing the distributed target search problem.

Feature extraction from 3D datasets is a current problem. Machine learning is an important tool for classification of complex 3D datasets. Machine learning classification techniques are widely used in various fields, such as text classification, pattern recognition, medical disease analysis, etc. The aim of this study is to apply the most popular classification and regression methods to determine the best classification and regression method based on the geodesics.

The feature vector is determined by the unit normal vector and the unit principal vector at each point of the 3D surface along with the point coordinates themselves. Moreover, different examples are compared according to the classification methods in terms of accuracy and the regression algorithms in terms of R-squared value.

Several surface examples are analyzed for the feature vector using classification (31 methods) and regression (23 methods) machine learning algorithms. In addition, two ensemble methods XGBoost and LightGBM are used for classification and regression. Also, the scores for each surface example are compared.

To the best of the author’s knowledge, this is the first study to analyze datasets based on geodesics using machine learning algorithms for classification and regression.

The purpose of this paper is to propose an efficient method, called kinodynamic velocity obstacle (KidVO), for motion planning of omnimobile robots considering kinematic and dynamic constraints (KDCs).

The suggested method improves generalized velocity obstacle (GVO) approach by a systematic selection of proper time horizon. Selection procedure of the time horizon is based on kinematical and dynamical restrictions of the robot. Toward this aim, an omnimobile robot with a general geometry is taken into account, and the admissible velocity and acceleration cones reflecting KDCs are derived, respectively. To prove the advantages of the suggested planning method, its performance is compared with GVOs, the so-called Hamilton-Jacobi-Bellman equation and the rapidly exploring random tree.

The obtained results of the presented scenarios which contain both computer and real-world experiments for complicated crowded environments indicate the merits of the suggested methodology in terms of its near-optimal behavior, successful obstacle avoidance both in static and dynamic environments and reaching to the goal pose.

This paper proposes a novel method for online motion planning of omnimobile robots in dynamic environments while considering the real capabilities of the robot.

Many existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.

The authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.

The proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.

Infeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.

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.

As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots.

The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness.

According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented.

EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study.

The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios.

This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.