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Since many global path planning algorithms cannot achieve the planned path with both safety and economy, this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles.

First, combining satellite image and the Voronoi field algorithm (VFA) generates rasterized environmental information and establishes navigation area boundary. Second, establishing a hazard function associated with navigation area boundary improves the evaluation function of the A^* algorithm and uses the improved A^* algorithm for global path planning. Finally, to reduce the number of redundant nodes in the planned path and smooth the path, node optimization and gradient descent method (GDM) are used. Then, a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained.

The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries. The node reduction rate is between 33.52% and 73.15%, and the smoothness meets the navigation requirements. This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’ autonomous obstacle avoidance decision-making.

This study establishes navigation area boundary for the environment based on the VFA and uses the improved A* algorithm to generate a navigation path that takes into account both safety and economy. This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method. The proposed global path planning method solves the requirements of path safety and smoothness.

This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning.

A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment.

The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment.

This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

Aiming at obtaining a high-quality global path for a mobile robot which works in complex environments, a modified particle swarm optimization (PSO) algorithm, named random-disturbance self-adaptive particle swarm optimization (RDSAPSO), is proposed in this paper.

A perturbed global updating mechanism is introduced to the global best position to avoid stagnation in RDSAPSO. Moreover, a new self-adaptive strategy is proposed to fine-tune the three control parameters in RDSAPSO to dynamically adjust the exploration and exploitation capabilities of RDSAPSO. Because the convergence of PSO is paramount and influences the quality of the generated path, this paper also analytically investigates the convergence of RDSAPSO and provides a convergence-guaranteed parameter selection principle for RDSAPSO. Finally, a RDSAPSO-based global path planning (GPP) method is developed, in which the feasibility-based rule is applied to handle the constraint of the problem.

In an attempt to validate the proposed method, it is compared against six state-of-the-art evolutionary methods under three different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the path optimality. Moreover, the computation time of the proposed method is comparable with those of the other compared methods.

Therefore, the proposed method can be considered as a vital alternative in the field of GPP.

Path planning in unknown or partly unknown environment is a quite complex task, partly because it is an evolving globally optimal path affected by the motion of the robot and the changing of environmental information. The purpose of this paper is to propose an online path planning approach for a mobile robot, which aims to provide a better adaptability to the motion of the robot and the changing of environmental information.

This approach treats the globally optimal path as a changing state and estimates it online with two steps: prediction step, which predicts the globally optimal path based on the motion of the robot; and updating step, which uses the up‐to‐date environmental information to refine the prediction.

Simulations and experiments show that this approach needs less time to reach the destination than some classical algorithms, provides speedy convergence and can adapt to unexpected obstacles or very limited prior environmental information. The better performances of this approach have been proved in both field and indoor environments.

Compared with previous works, the paper's approach has three main contributions. First, it can reduce the time consumed in reaching the destination by adopting an online path planning strategy. Second, it can be applied in such environments as those with unexpected obstacles or with only limited prior environmental information. Third, both motion error of the robot and the changing of environmental information are considered, so that the global adaptability to them is improved.

It is possible with classical path planning algorithms to plan a path in a static environment if the instant position of the vehicle is known and the target and obstacle positions are constant. In a dynamic case, these methods used for the static environment are insufficient. The purpose of this study is to find a new method that can provide a solution to the four-rotor unmanned aerial vehicle (UAV) path planning problem in static and dynamic environments.

As a solution to the problem within the scope of this study, there is a new hybrid method in which the global A* algorithm and local the VFH+ algorithm are combined.

The performance of the designed algorithm was tested in different environments using the Gazebo model of a real quadrotor and the robot operating system (ROS), which is the widely used platform for robotic applications. Navigation stacks developed for mobile robots on the ROS platform were also used for the UAV, and performance benchmarks were carried out. From the proposed hybrid algorithm, remarkable results were obtained in terms of both planning and implementation time compared to ROS navigation stacks.

This study proposes a new hybrid approach to the path planning problem for UAVs operating in both static and dynamic environments.

This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment.

The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method.

Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method.

Therefore, this can provide a new method for the area of path planning of intelligent robot.

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.

Unmanned aerial/ground vehicles (UAV/UGV) collaboration systems are increasingly being used to perform reconnaissance and rescue missions autonomously, especially in disaster areas. The paper aims to discuss this issue.

To improve visibility, this study proposes a path-planning algorithm based on map matching. Continuous ground images are first collected aerially using the UAV vision system. Subsequently, a global map of the ground environment is created by processing the collected images using the methods of image correction, image mosaic and obstacle recognition. The local map of the ground environment is obtained using the 2D laser radar sensor of the UGV. A set of features for both global and local maps is established. Unknown values during map matching are determined via the least squares method. Based on the matched mapping, the traditional A* algorithm is used for the planning of global path in the global map, and the dynamic window method is used for adjustment of the local map.

Simulation experiments were carried out to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm can construct a global map of the wide environment and effectively bypass the obstacles missed by the UAV.

Prior to map matching, there is a need to extract the edge of obstacles in the global map.

This paper proposed a path planning algorithm based on map matching, yielding insights into the application of the UAV/UGV collaboration systems in disaster areas.

The purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).

Based on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.

The assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.

The work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

This paper aims to provide a novel obstacle avoidance method based on multi-information inflation map.

In this paper, the multi-information inflation map is introduced, which considers different information, including a two-dimensional grid map and a variety of sensor information. The static layer of the map is pre-processed at first. Then sensor inputs are added in different semantic layers. The processed information in semantic layers is used to update the static layer. The obstacle avoidance algorithm based on the multi-information inflation map is able to generate different avoidance paths for different kinds of obstacles, and the motion planning based on multi-information inflation map can track the global path and drive the robot.

The proposed method was implemented on a self-made mobile robot. Four experiments are conducted to verify the advantages of the proposed method. The first experiment is to demonstrate the advantages of the multi-information inflation map over the layered cost map. The second and third experiments verify the effectiveness of the obstacle avoidance path generation and motion planning. The fourth experiment comprehensively verifies that the obstacle avoidance algorithm is able to deal with different kinds of obstacles.

The multi-information inflation map proposed in this paper has better performance than the layered cost maps. As the static layer is pre-processed, the computational efficiency is higher. Sensor information is added in semantic layers with different cost attenuation coefficients. All layers are reset before next update. Therefore, the previous state will not affect the current situation. The obstacle avoidance and motion planning algorithm based on the multi-information inflation map can generate different paths for different obstacles and drive a robot safely and control the velocity according to different conditions.