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This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point.

Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability.

An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

The border control becomes challenging when a protected region is large and there is a limited number of border patrols. This research paper proposes a novel heuristic-based patrol path planning scheme in order to efficiently patrol with resource scarcity.

The trespasser influencing score, which is determined from the environmental characteristics and trespassing statistic of the region, is used as a heuristic for measuring a chance of approaching a trespasser. The patrol plan is occasionally updated with a new trespassing statistic during a border operation. The performance of the proposed patrol path planning scheme was evaluated and compared with other patrol path planning schemes by the empirical experiment under different scenarios.

The result from the experiment indicates that the proposed patrol planning outperforms other patrol path planning schemes in terms of the trespasser detection rate, when more environment-aware trespassers are in the region.

The experiment was conducted through simulated agents in simulated environment, which were assumed to mimic real behavior and environment.

This research paper contributes a heuristic-based patrol path planning scheme that applies the environmental characteristics and dynamic statistic of the region, as well as a border surveillance problem model that would be useful for mobile sensor planning in a border surveillance application.

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.

The purpose of this paper is to improve the D* algorithm which has been used usually in robotics for mobile robot navigation in unknown or dynamic environments.

First, the model of 2D workspace with some obstacles is expressed in regularity grids. The optimal path is planned by using the improved D* algorithm by searching in the neighbor grid cells in 16 directions. It makes the robot that the smallest turning angle drops to π/8. The robot moving angle discrete precision is raised and the unnecessary cost of path planning is reduced so the robot motion path is smoother. Then, the improved D* algorithm is simulated in MOBOTSIM software environment and is tested by the WiRobotX80 mobile robot.

To search in the neighbor grid cells in 16 directions instead of eight directions by using D* algorithms for path planning.

The map should be expressed in regularity grids.

The improved D* algorithm is effective and it can result in a higher quality path than the conventional D* algorithm at the same map environment.

With the development of technology, the application scenarios of mobile robots are becoming more and more extensive, accompanied by a variety of application scenarios suitable and safe path planning algorithms are indispensable for mobile robots.

The purpose of this paper to improve the safety performance of your bot during the execution of tasks. The methods are synthesized in three main areas: setting appropriate safety distances based on the actual radius of the robot, turn penalty reduces the number of turns by applying an additional penalty to the number of turns in a heuristic function and path smoothing is used to improve path reliability by reducing the number of right-angle turns.

A suitable safety distance greatly improves the safety of mobile robots and facilitates their development. Optimization of turns in the path of mobile robots improves the travel efficiency of robots. Enhancing the safety of mobile robots has become a research hotspot for path-planning algorithms.

This paper proposes a path planning scheme for mobile robots with safe distances, which provides readers with a comprehensive and systematic progress of path planning research. It helps readers to get inspiration from enhancing the safety of mobile robots.

This paper aims to develop path‐planning techniques that support a general selective disassembly planner in a virtual reality environment.

The paper presents an automatic selective disassembly planning and two path‐planning techniques that support it. The first one is based on single translations, while the second is based on the generation of a random search tree. The methods used have been adapted and modified from available robotic path‐planning methods for their use in disassembly path planning.

The paper finds that the proposed techniques are applicable to the automatic generation of disassembly sequences.

The paper provides an automatic tool that can be integrated in simulation software for the analysis and validation of disassembly operation.

Maintenance operations have a great impact in the security and life expectancy of any product. This is especially true for some applications such as aerospace that must pass rigorous security checking procedures. Geometric reasoning and virtual reality can help in reducing costs and design time by moving testing from physical mock‐ups to virtual ones.

The paper shows the integration of path‐planning techniques in automatic disassembly‐planning methods.

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.

The purpose of this paper is to describe a shape‐shifting robot with diverse configurations, named “AMOEBA‐I”, which has been developed for search and rescue operations. The accessibility of this robot to unstructured environment is efficiently enhanced by changing its configuration. So the shape and reconfiguration of the robot should be considered in AMOEBA‐I path planning to improve work ability of the robot in complex environment. The unique accessibility of AMOEBA‐I is thus fully displayed.

An auto‐adapted path‐planning method is presented for AMOEBA‐I by introducing the reconfigurable ability of the robot into the modified potential field method. The modified potential field method solves the local minimum problem and goal‐unreachable with nearby obstacles (GUWNO) effectively. A method of the shape‐shifting robot's passing through the narrow space is studied by combining the corner detection with the modified potential field method.

The ability of the robot to automatically change configuration to pass through a narrow space is proven through the experiment. Simulation results show that the robot can change its own configurations to perform auto‐adapted path planning corresponding to the environmental variation. Therefore, the proposed method can improve the probability of completing the path planning. As a result, this method will shorten the path length and complete the rescue operation more effectively.

The paper presents an effective auto‐adapted path‐planning method that integrates the reconfigurable ability of the robot into the modified potential field method in order to realize the auto‐adapted path planning.

Path planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots.

First, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking.

The simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework.

The work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.

The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve.

The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points.

This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed.

There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research.

The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.