Journal articles: 'Collision avoidance; Global path planning'

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Chen, Gang, Dan Liu, Yifan Wang, Qingxuan Jia, and Xiaodong Zhang. "Path planning method with obstacle avoidance for manipulators in dynamic environment." International Journal of Advanced Robotic Systems 15, no. 6 (November 1, 2018): 172988141882022. http://dx.doi.org/10.1177/1729881418820223.

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Obstacle avoidance is of great importance for path planning of manipulators in dynamic environment. To help manipulators successfully perform tasks, a method of path planning with obstacle avoidance is proposed in this article. It consists of two consecutive phases, namely, collision detection and obstacle-avoidance path planning. The collision detection is realized by establishing point-cloud model and testing intersection of axis-aligned bounding boxes trees, while obstacle-avoidance path planning is achieved through preplanning a global path and adjusting it in real time. This article has the following contributions. The point-cloud model is of high resolution while the speed of collision detection is improved, and collision points can be found exactly. The preplanned global path is optimized based on the improved D-star algorithm, which reduces inflection points and decreases collision probability. The real-time path adjusting strategy satisfies the requirement of reachability and obstacle avoidance for manipulators in dynamic environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available to manipulators of any degree of freedom in dynamic environment.

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Xia, Guoqing, Zhiwei Han, Bo Zhao, and Xinwei Wang. "Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization." Complexity 2020 (April 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/3095426.

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An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

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Maw, Aye Aye, Maxim Tyan, Tuan Anh Nguyen, and Jae-Woo Lee. "iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV." Applied Sciences 11, no. 9 (April 27, 2021): 3948. http://dx.doi.org/10.3390/app11093948.

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Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

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Wang, Zhenfei, Chuchu Zhang, Junfeng Wang, Zhiyun Zheng, and Lun Li. "Research on Path Planning Algorithm for Crowd Evacuation." Symmetry 13, no. 8 (July 24, 2021): 1339. http://dx.doi.org/10.3390/sym13081339.

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In recent years, crowded stampede incidents have occurred frequently, resulting in more and more serious losses. The common cause of such incidents is that when large-scale populations gather in a limited area, the population is highly unstable. In emergency situations, only when the crowd reaches the safe exit as soon as possible within a limited evacuation time to complete evacuation can the loss and casualties be effectively reduced. Therefore, the safety evacuation management of people in public places in emergencies has become a hot topic in the field of public security. Based on the analysis of the factors affecting the crowd path selection, this paper proposes an improved path-planning algorithm based on BEME (Balanced Evacuation for Multiple Exits). And pedestrian evacuation simulation is carried out in multi-exit symmetrical facilities. First, this paper optimizes the update method of the GSDL list in the BEME algorithm as the basis for evacuating pedestrians to choose an exit. Second, the collision between pedestrians is solved by defining the movement rule and collision avoidance strategy. Finally, the algorithm is compared with BEME and traditional path-planning algorithms. The results show that the algorithm can further shorten the global evacuation distance of the symmetrical evacuation scene, effectively balance the number of pedestrians at each exit and reduce the evacuation time. In addition, this improved algorithm uses a collision avoidance strategy to solve the collision and congestion problems in path planning, which helps to maximize evacuation efficiency. Whether the setting of the scene or the setting of the exit, all studies are based on symmetric implementation. This is more in line with the crowd evacuation in the real scene, making the experimental results more meaningful.

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Zhu, Shinan, Weiyi Zhu, Xueqin Zhang, and Tao Cao. "Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988141989897. http://dx.doi.org/10.1177/1729881419898979.

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Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

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Polvara, Riccardo, Sanjay Sharma, Jian Wan, Andrew Manning, and Robert Sutton. "Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles." Journal of Navigation 71, no. 1 (October 10, 2017): 241–56. http://dx.doi.org/10.1017/s0373463317000753.

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The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

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de Oliveira, Guilherme, Kevin de Carvalho, and Alexandre Brandão. "A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities." Sensors 19, no. 5 (March 1, 2019): 1049. http://dx.doi.org/10.3390/s19051049.

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This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities. The proposed algorithm does not require any prior information but assumes that a mapping algorithm is used. If enough information is available, a global path planner finds sub-optimal collision-free paths within the known map. For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution. The strategy was tested in a real, cluttered environment experiment using the Pioneer P3-DX and the Xbox 360 kinect sensor, to validate and evaluate the algorithm efficiency.

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Li, Xingyu, Bo Tang, John Ball, Matthew Doude, and Daniel W. Carruth. "Rollover-Free Path Planning for Off-Road Autonomous Driving." Electronics 8, no. 6 (May 31, 2019): 614. http://dx.doi.org/10.3390/electronics8060614.

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Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving.

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Hee, Lee Gim, and Marcelo H. Ang Jr. "An Integrated Algorithm for Autonomous Navigation of a Mobile Robot in an Unknown Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 4 (July 20, 2008): 328–35. http://dx.doi.org/10.20965/jaciii.2008.p0328.

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Global path planning algorithms are good in planning an optimal path in a known environment, but would fail in an unknown environment and when reacting to dynamic and unforeseen obstacles. Conversely, local navigation algorithms perform well in reacting to dynamic and unforeseen obstacles but are susceptible to local minima failures. A hybrid integration of both the global path planning and local navigation algorithms would allow a mobile robot to find an optimal path and react to any dynamic and unforeseen obstacles during an operation. However, the hybrid method requires the robot to possess full or partial prior information of the environment for path planning and would fail in a totally unknown environment. The integrated algorithm proposed and implemented in this paper incorporates an autonomous exploration technique into the hybrid method. The algorithm gives a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment.

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Lazarowska, Agnieszka. "Decision support system for collision avoidance at sea." Polish Maritime Research 19, Special (October 1, 2012): 19–24. http://dx.doi.org/10.2478/v10012-012-0018-2.

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ABSTRACT The paper presents design and realization of computer decision support system in collision situations of passage with greater quantity of met objects. The system was implemented into the real ship electro-navigational system onboard research and training ship m/v HORYZONT II. The radar system with Automatic Radar Plotting Aid constitutes a source of input data for algorithm determining safe trajectory of a ship. The article introduces radar data transmission details. The dynamic programming algorithm is used for the determination of safe optimal trajectory of own ship. The system enables navigational data transmission from radar system and automatic determining of safe manoeuvre or safe trajectory of a ship. Further development of navigator’s decision support system is also presented. Path Planning Subsystem is proposed for the determination of global optimal route between harbours with the use of Ant Colony Optimization algorithms.

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Karakaya, Suat, Gurkan Kucukyildiz, and Hasan Ocak. "A dynamic path planning method for wheeled mobile robots (Dyna-bug)." Global Journal of Computer Sciences: Theory and Research 7, no. 3 (December 11, 2017): 123–28. http://dx.doi.org/10.18844/gjcs.v7i3.2791.

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Abstract In this study, a hybrid path-planning scheme is presented. The main contribution of this paper is merging the static grid costs of the global map and the immediate environmental structure of the local map. The stationary condition of the map and the instant local goal is weighted by certain coefficients in order to determine the next move of the wheeled mobile robot (WMR). Thus, the cost function is defined in terms of the grid costs and the dynamic parameters. The main assumption is that the WMR on which this scheme is executed must be equipped with a field scanning sensor. The sensor readings in each processing cycle are pre-processed before plugging in the cost function. The passages in the local map are extracted from the sensor data, then the optimal collision-free point lying on the passages is obtained via the cost function. Keywords: Path planning, collision avoidance, mobile robot.

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Blackmore, Lars, Masahiro Ono, and Brian C. Williams. "Chance-Constrained Optimal Path Planning With Obstacles." IEEE Transactions on Robotics 27, no. 6 (December 2011): 1080–94. http://dx.doi.org/10.1109/tro.2011.2161160.

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Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For robust execution, we must take into account uncertainty, which arises due to uncertain localization, modeling errors, and disturbances. Prior work handled the case of set-bounded uncertainty. We present here a chance-constrained approach, which uses instead a probabilistic representation of uncertainty. The new approach plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold. Failure occurs when the vehicle collides with an obstacle or leaves an operator-specified region. The key idea behind the approach is to use bounds on the probability of collision to show that, for linear-Gaussian systems, we can approximate the nonconvex chance-constrained optimization problem as a disjunctive convex program. This can be solved to global optimality using branch-and-bound techniques. In order to improve computation time, we introduce a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality. We present an empirical validation with an aircraft obstacle avoidance example.

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Rashid, Abdulmuttalib, Abduladhem Ali, and Mattia Frasca. "Polygon Shape Formation for Multi-Mobile Robots in a Global Knowledge Environment." Iraqi Journal for Electrical and Electronic Engineering 15, no. 1 (June 1, 2019): 76–88. http://dx.doi.org/10.37917/ijeee.15.1.8.

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In coordination of a group of mobile robots in a real environment, the formation is an important task. Multimobile robot formations in global knowledge environments are achieved using small robots with small hardware capabilities. To perform formation, localization, orientation, path planning and obstacle and collision avoidance should be accomplished. Finally, several static and dynamic strategies for polygon shape formation are implemented. For these formations minimizing the energy spent by the robots or the time for achieving the task, have been investigated. These strategies have better efficiency in completing the formation, since they use the cluster matching algorithm instead of the triangulation algorithm.

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Wu, Peng, Shaorong Xie, Hengli Liu, Ming Li, Hengyu Li, Yan Peng, Xiaomao Li, and Jun Luo. "Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring." Industrial Robot: An International Journal 44, no. 1 (January 16, 2017): 64–74. http://dx.doi.org/10.1108/ir-04-2016-0127.

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Purpose Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance. Design/methodology/approach The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer. Findings The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials. Originality/value The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

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Younes, Younes Al, and Martin Barczyk. "Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon." Sensors 21, no. 16 (August 18, 2021): 5547. http://dx.doi.org/10.3390/s21165547.

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Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.

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Ravankar, Abhijeet, Ankit Ravankar, Arpit Rawankar, Yohei Hoshino, and Yukinori Kobayashi. "ITC: Infused Tangential Curves for Smooth 2D and 3D Navigation of Mobile Robots." Sensors 19, no. 20 (October 10, 2019): 4384. http://dx.doi.org/10.3390/s19204384.

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Navigation is an indispensable component of ground and aerial mobile robots. Although there is a plethora of path planning algorithms, most of them generate paths that are not smooth and have angular turns. In many cases, it is not feasible for the robots to execute these sharp turns, and a smooth trajectory is desired. We present `ITC: Infused Tangential Curves’ which can generate smooth trajectories for mobile robots. The main characteristics of the proposed ITC algorithm are: (1) The curves are tangential to the path, thus maintaining G 1 continuity, (2) The curves are infused in the original global path to smooth out the turns, (3) The straight segments of the global path are kept straight and only the sharp turns are smoothed, (4) Safety is embedded in the ITC trajectories and robots are guaranteed to maintain a safe distance from the obstacles, (5) The curvature of ITC curves can easily be controlled and smooth trajectories can be generated in real-time, (6) The ITC algorithm smooths the global path on a part-by-part basis thus local smoothing at one point does not affect the global path. We compare the proposed ITC algorithm with traditional interpolation based trajectory smoothing algorithms. Results show that, in case of mobile navigation in narrow corridors, ITC paths maintain a safe distance from both walls, and are easy to generate in real-time. We test the algorithm in complex scenarios to generate curves of different curvatures, while maintaining different safety thresholds from obstacles in vicinity. We mathematically discuss smooth trajectory generation for both 2D navigation of ground robots, and 3D navigation of aerial robots. We also test the algorithm in real environments with actual robots in a complex scenario of multi-robot collision avoidance. Results show that the ITC algorithm can be generated quickly and is suitable for real-world scenarios of collision avoidance in narrow corridors.

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Singh, Yogang, Marco Bibuli, Enrica Zereik, Sanjay Sharma, Asiya Khan, and Robert Sutton. "A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment." Journal of Marine Science and Engineering 8, no. 9 (August 19, 2020): 624. http://dx.doi.org/10.3390/jmse8090624.

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Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key characteristics of formation control and cooperative motion planning. In this framework, two layers of offline planning and online planning are integrated and applied on a practical marine environment. In offline planning, an optimal path is generated from a constrained A* path planning approach, which is later smoothed using a spline. This optimal trajectory is fed as an input for the online planning where virtual target (VT) based multi-agent guidance framework is used to navigate the swarm of USVs. This VT approach combined with a potential theory based swarm aggregation technique provides a robust methodology of global and local collision avoidance based on known positions of the USVs. The combined approach is evaluated with the different number of USVs to understand the effectiveness of the approach from the perspective of practicality, safety and robustness.

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Wen, Naifeng, Rubo Zhang, Guanqun Liu, and Junwei Wu. "Online Heuristically Planning for Relative Optimal Paths Using a Stochastic Algorithm for USVs." Journal of Navigation 73, no. 2 (December 23, 2019): 485–508. http://dx.doi.org/10.1017/s0373463319000791.

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This paper attempts to solve a challenge in online relative optimal path planning of unmanned surface vehicles (USVs) caused by current and wave disturbance in the practical marine environment. The asymptotically optimal rapidly extending random tree (RRT*) method for local path optimisation is improved. Based on that, an online path planning (OPP) scheme is proposed according to the USV's kinematic and dynamic model. The execution efficiency of RRT* is improved by reduction of the sampling space that is used for randomly learning environmental knowledge. A heuristic sampling scheme is proposed based on the proportional navigation guidance (PNG) method that is used to enable the OPP procedure to utilise the reference information of the global path. Meanwhile, PNG is used to guide RRT* in generating feasible paths with a small amount of gentle turns. The dynamic obstacle avoidance problem is also investigated based on the International Regulations for Preventing Collisions at Sea. Case studies demonstrate that the proposed method efficiently plans paths that are relatively easier to execute and lower in fuel expenditure than traditional schemes. The dynamic obstacle avoidance ability of the proposed scheme is also attested.

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Xue, Dong, Jing Yao, and Jun Wang. "H∞Formation Control and Obstacle Avoidance for Hybrid Multi-Agent Systems." Journal of Applied Mathematics 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/123072.

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A new concept ofH∞formation is proposed to handle a group of agents navigating in a free and an obstacle-laden environment while maintaining a desired formation and changing formations when required. With respect to the requirements of changing formation subject to internal or external events, a hybrid multiagent system (HMAS) is formulated in this paper. Based on the fact that obstacles impose the negative effect on the formation of HMAS, theH∞formation is introduced to reflect the above disturbed situation and quantify the attenuation level of obstacle avoidance via theH∞-norm of formation stability. An improved Newtonian potential function and a set of repulsive functions are employed to guarantee the HMAS formation-keeping and collision-avoiding from obstacles in a path planning problem, respectively. Simulation results in this paper show that the proposed formation algorithms can effectively allow the multiagent system to avoid penetration into obstacles while accomplishing prespecified global objective successfully.

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Williams, Robert L., and Jianhua Wu. "Dynamic Obstacle Avoidance for an Omnidirectional Mobile Robot." Journal of Robotics 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/901365.

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We have established a novel method of obstacle-avoidance motion planning for mobile robots in dynamic environments, wherein the obstacles are moving with general velocities and accelerations, and their motion profiles are not preknown. A hybrid system is presented in which a global deliberate approach is applied to determine the motion in the desired path line (DPL), and a local reactive approach is used for moving obstacle avoidance. A machine vision system is required to sense obstacle motion. Through theoretical analysis, simulation, and experimental validation applied to the Ohio University RoboCup robot, we show the method is effective to avoid collisions with moving obstacles in a dynamic environment.

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Raheem, Firas A., and Umniah I. Hameed. "Heuristic D* Algorithm Based on Particle Swarm Optimization for Path Planning of Two-Link Robot Arm in Dynamic Environment." Al-Khwarizmi Engineering Journal 15, no. 2 (May 28, 2019): 108–23. http://dx.doi.org/10.22153/kej.2019.01.004.

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Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved. In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO) technique. Moreover, this work focuses on computational part of motion planning in completely changing dynamic environment at every motion sample domains. Since the environment type that discussed here is a known dynamic environment, the solution approach can be off-line. The main advantage of the off-line planning is that a global optimal path solution is always obtained, which is able to overcome all the difficulties caused by the dynamic behavior of the obstacles. A mixing approach of robot path planning using the heuristic method D* algorithm based on optimization technique is used. The heuristic D* method is chosen for finding the shortest path. Furthermore, to insure the path length optimality and for enhancing the final path, PSO technique has been utilized. The robot type has been used here is the two-link robot arm which represents a more difficult case than the mobile robot. Simulation results are given to show the effectiveness of the proposed method which clearly shows a completely safe and short path.

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Yu, Chris, Henrik Schumacher, and Keenan Crane. "Repulsive Curves." ACM Transactions on Graphics 40, no. 2 (May 3, 2021): 1–21. http://dx.doi.org/10.1145/3439429.

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Curves play a fundamental role across computer graphics, physical simulation, and mathematical visualization, yet most tools for curve design do nothing to prevent crossings or self-intersections. This article develops efficient algorithms for (self-)repulsion of plane and space curves that are well-suited to problems in computational design. Our starting point is the so-called tangent-point energy , which provides an infinite barrier to self-intersection. In contrast to local collision detection strategies used in, e.g., physical simulation, this energy considers interactions between all pairs of points, and is hence useful for global shape optimization: local minima tend to be aesthetically pleasing, physically valid, and nicely distributed in space. A reformulation of gradient descent based on a Sobolev-Slobodeckij inner product enables us to make rapid progress toward local minima—independent of curve resolution. We also develop a hierarchical multigrid scheme that significantly reduces the per-step cost of optimization. The energy is easily integrated with a variety of constraints and penalties (e.g., inextensibility, or obstacle avoidance), which we use for applications including curve packing, knot untangling, graph embedding, non-crossing spline interpolation, flow visualization, and robotic path planning.

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Landry, Chantal, Dietmar Hömberg, René Henrion, and Matthias Gerdts. "Path planning and collision avoidance for robots." Numerical Algebra, Control and Optimization 2, no. 3 (August 2012): 437–63. http://dx.doi.org/10.3934/naco.2012.2.437.

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Lin, Yucong, and Srikanth Saripalli. "Sampling-Based Path Planning for UAV Collision Avoidance." IEEE Transactions on Intelligent Transportation Systems 18, no. 11 (November 2017): 3179–92. http://dx.doi.org/10.1109/tits.2017.2673778.

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N. Abdulnabi, Ali. "Obstacle Avoidance Techniques for Robot Path Planning." DJES 12, no. 1 (March 1, 2019): 56–65. http://dx.doi.org/10.24237/djes.2019.12107.

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This paper presents a collision-free path planning approaches based on Bézier curve and A-star algorithm for robot manipulator system. The main problem of this work is to finding a feasible collision path planning from initial point to final point to transport the robot arm from the preliminary to the very last within the presence of obstacles, a sequence of joint angles alongside the path have to be determined. To solve this problem several algorithms have been presented among which it can be mention such as Bug algorithms, A-Star algorithms, potential field algorithms, Bézier curve algorithm and intelligent algorithms. In this paper obstacle avoidance algorithms were proposed Bézier and A-Star algorithms, through theoretical studies and simulations with several different cases, it's found verify the effectiveness of the methods suggested. It's founded the Bézier algorithm is smoothing accurate, and effective as compare with the A-star algorithm, but A-star is near to shortest and optimal path to free collision avoidance. The time taken and the elapsed time to traverse from its starting position and to reach the goal are recorded the tabulated results show that the elapsed time with different cases to traverse from the start location to destination using A-star Algorithm is much less as compared to the time taken by the robot using Bézier Algorithm to trace the same path. The robot used was the Lab-Volt of 5DOF Servo Robot System Model 5250 (RoboCIM5250)

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Liang, Quan, Dong Hai Su, Jie Wang, and Ye Mu Wang. "The Closed Impeller Milling Avoidance Programming Algorithm." Advanced Materials Research 753-755 (August 2013): 1270–73. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.1270.

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An automatically correction algorithm was promoted to solve the problem that most of the commercial CAD/CAM software can only detect the collision, but can not generate collision-free tool path on cutting path planning. The algorithm uses the configuration space theory to map the boundary into the 2D configuration space, and then the 3D space interference collision problem was transformed into 2D space. Finally, the algorithm was used to generate cutting path of a closed impellers runner. From the result of cutting path planning, the proposed algorithm was able to avoid the interference in the process of tool path planning about closed impeller.

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Ramadhan, Duaa, Abdulmuttalib Rashid, and Osama Rashid. "Two Dimensional Path Planning with Static Polygon Obstacles Avoidance." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 65–72. http://dx.doi.org/10.37917/10.37917/ijeee.sceeer.3rd.10.

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This paper presents the designing of path planning system in an environment contains a set of static polygon obstacles localized and distributed randomly by using differential drive mobile robot. In this paper the designed algorithm (two dimensional path planning algorithm) is proposed in order of investigate the path planning of mobile robot with free collision using the visibility binary tree algorithm. The suggested algorithm is compared with the virtual circles tangents algorithm in the time of arrival and the longest of the path to the target. The aim of this paper is to get an algorithm has better performance than the other algorithms and get less time of arrival and shortest path with free collision.

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Ramadhan, Duaa, Abdulmuttalib Rashid, and Osama Rashid. "Two Dimensional Path Planning with Static Polygon Obstacles Avoidance." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 65–72. http://dx.doi.org/10.37917/ijeee.sceeer.3rd.10.

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This paper presents the designing of path planning system in an environment contains a set of static polygon obstacles localized and distributed randomly by using differential drive mobile robot. In this paper the designed algorithm (two dimensional path planning algorithm) is proposed in order of investigate the path planning of mobile robot with free collision using the visibility binary tree algorithm. The suggested algorithm is compared with the virtual circles tangents algorithm in the time of arrival and the longest of the path to the target. The aim of this paper is to get an algorithm has better performance than the other algorithms and get less time of arrival and shortest path with free collision.

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Keerthana, V., C. Kiruthiga, P. Kiruthika, V. Sowmiya, and R. Manikadan. "NAVIGATION OF MOBILE ROBOT- ALGORITHM FOR PATH PLANNING & COLLISION AVOIDANCE- A REVIEW." International Journal of Research -GRANTHAALAYAH 5, no. 1 (January 31, 2017): 198–205. http://dx.doi.org/10.29121/granthaalayah.v5.i1.2017.1735.

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The ﬁeld of autonomous mobile robotics has recently gained many researchers’ interests. Due to the speciﬁc needs required by various applications of mobile robot systems, especially in navigation, designing a real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective of our project is applications based mobile robot systems, especially in navigation, designing real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. The main objective behind using the obstacle avoidance approach is to obtain a collision-free trajectory from the starting point to the target in monitoring environments. The ability of the robot to follow a path, detects obstacles, and navigates around them to avoid collision. It also shows that the robot has been successfully following very congested curves and has avoided any obstacle that emerged on its path. Motion planning that allows the robot to reach its target without colliding with any obstacles that may exist in its path. To avoid collision in the mobile robot environment, providing a path planning& line following approach. Line following, path planning, collision avoidance, back propagation, improved memory, detecting long distance obstacles. Cheap and economical than the former one. Also work with back propagation technique.

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Nayl, Thaker. "ROBOTIC MOTION PLANNING USING CONVEX OPTIMIZATION METHODS." Iraqi Journal for Computers and Informatics 45, no. 2 (December 1, 2019): 20–23. http://dx.doi.org/10.25195/ijci.v45i2.49.

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Collision avoidance techniques tend to derive the robot away of the obstacles in minimal total travel distance. Most ofthe collision avoidance algorithms have trouble get stuck in a local minimum. A new technique is to avoid local minimum in convexoptimization-based path planning. Obstacle avoidance problem is considered as a convex optimization problem under system state andcontrol constraints. The idea is by considering the obstacles as a convex set of points which represents the obstacle that encloses inminimum volume ellipsoid, also the addition of the necessary offset distance and the modified motion path is presented. In the analysis,the results demonstrated the effectiveness of the suggested motion planning by using the convex optimization technique.

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Arokiasami, Willson Amalraj, Prahlad Vadakkepat, Kay Chen Tan, and Dipti Srinivasan. "Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework." Unmanned Systems 06, no. 04 (October 2018): 231–50. http://dx.doi.org/10.1142/s2301385018500061.

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Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.

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Ikeda, Takeshi, Yoshiki Hirao, Seiji Furuno, and Fusaomi Nagata. "Path planning for collision avoidance using a glid-like space." Artificial Life and Robotics 23, no. 1 (October 4, 2017): 80–86. http://dx.doi.org/10.1007/s10015-017-0398-6.

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Xu, Zhao, Jinwen Hu, Yunhong Ma, Man Wang, and Chunhui Zhao. "A Study on Path Planning Algorithms of UAV Collision Avoidance." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 1 (February 2019): 100–106. http://dx.doi.org/10.1051/jnwpu/20193710100.

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The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.

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Holdsworth, R., J. Lambert, and N. Harle. "Inflight path planning replacing pure collision avoidance, using ADS-B." IEEE Aerospace and Electronic Systems Magazine 16, no. 2 (2001): 27–32. http://dx.doi.org/10.1109/62.904241.

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35

Brandt, Thorsten, and Thomas Sattel. "PATH PLANNING FOR AUTOMOTIVE COLLISION AVOIDANCE BASED ON ELASTIC BANDS." IFAC Proceedings Volumes 38, no. 1 (2005): 210–15. http://dx.doi.org/10.3182/20050703-6-cz-1902.01245.

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Dang, Lifeng, Liangan Zhang, and Qin Shi. "Research on Collision Avoidance Path Planning of Double SCARA Robot." IOP Conference Series: Earth and Environmental Science 632 (January 14, 2021): 032051. http://dx.doi.org/10.1088/1755-1315/632/3/032051.

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Diao, Shipu, Xindu Chen, Lei Wu, Mingjiang Yang, and Junhui Liu. "The Optimal Collision Avoidance Trajectory Planning of Redundant Manipulators in the Process of Grinding Ceramic Billet Surface." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7405831.

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The intelligent manufacturing system (IMS) is widely used in the surface machining of the workpiece. In the process of ceramic surface grinding, the intelligent machine (manipulator) in IMS is required to automatically plan the collision avoidance trajectory in a complex environment. This paper presents an optimal trajectory planning method of the use of redundant manipulators in the surface grinding of ceramic billet, which is based on trajectory evaluation. The collision avoidance trajectory can be optimized, taking into account several parameters in the trajectory, including the length of the collision avoidance path, the weighted sum of the strokes of all joints, and the duration of the collision avoidance trajectory. Firstly, get the planning task. Secondly, set the planning parameters and obtain a number of collision avoidance trajectories. Finally, the evaluation function is used to evaluate the collision avoidance trajectories and get the optimal collision avoidance trajectory. The performance of the proposed optimal collision avoidance trajectory planning method is validated in different evaluation functions.

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Wang, Tian Qi, Liang Yu Li, Tie Jun Gao, and Jun Jie He. "The Collision Avoidance Path Planning of K-Joint Automatic Welding Robot." Advanced Materials Research 317-319 (August 2011): 723–26. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.723.

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A four freedom robot able to carry out automatic welding of offshore platform is introduced in this paper. The kinematic design of the robot has been designed according to the welding seam feature of T/Y/K-joint. The path planning of T/Y and the obstacle avoidance technique are used to study the robot’s collision avoidance welding on K-joint. In the process of K-joint path planning, the geometrical method was adopted to study the welding torch’s posture in branch gap. And for ensuring the postural stability of robot, the robot posture of a series welding points in single branch pipe condition was adjusted gradually in K-joint condition. The automatic welding process of K-joint was simulated in Solidworks. The result shows that the method could satisfy the collision avoidance requirement in the K-joint welding project.

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Zhao, Tao, Haodong Li, and Songyi Dian. "Multi-robot path planning based on improved artificial potential field and fuzzy inference system1." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 7621–37. http://dx.doi.org/10.3233/jifs-200869.

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In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems.

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Ren, Jia, Jing Zhang, and Yani Cui. "Autonomous Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on an Improved Velocity Obstacle Method." ISPRS International Journal of Geo-Information 10, no. 9 (September 16, 2021): 618. http://dx.doi.org/10.3390/ijgi10090618.

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Focusing on the collision avoidance problem for Unmanned Surface Vehicles (USVs) in the scenario of multi-vessel encounters, a USV autonomous obstacle avoidance algorithm based on the improved velocity obstacle method is proposed. The algorithm is composed of two parts: a multi-vessel encounter collision detection model and a path re-planning algorithm. The multi-vessel encounter collision detection model draws on the idea of the velocity obstacle method through the integration of characteristics such as the USV dynamic model in the marine environment, the encountering vessel motion model, and the International Regulations for Preventing Collisions at Sea (COLREGS) to obtain the velocity obstacle region in the scenario of USV and multi-vessel encounters. On this basis, two constraint conditions for the motion state space of USV obstacle avoidance behavior and the velocity obstacle region are added to the dynamic window algorithm to complete a USV collision risk assessment and generate a collision avoidance strategy set. The path re-planning algorithm is based on the premise of the minimum resource cost and uses an improved particle swarm algorithm to obtain the optimal USV control strategy in the collision avoidance strategy set and complete USV path re-planning. Simulation results show that the algorithm can enable USVs to safely evade multiple short-range dynamic targets under COLREGS.

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Jiang, Kai, and Chun Gui Li. "Path Planning Based on Fuzzy Logic Algorithm for Robots in Hierarchical Control." Applied Mechanics and Materials 644-650 (September 2014): 701–4. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.701.

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To find the optimal path of mobile robots,a novel robot path planning strategy based on hierarchical control fuzzy algorithm has been proposed.The path planning strategy which developed to overcome the collision and avoidance problem in path planning of robot is inspired by fuzzy control concept,in order to achieving a target that making robots to follow a non-collision rapid and accurate path in uncertain environment.Simulation results showed that the strategy using fuzzy algorithm could meet the feasibility and validity demand.

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42

Zhang, Qiqian, Weiwei Xu, Honghai Zhang, and Han Li. "The Obstacle-Avoidance Path Planning for UAV Based on IOCAD." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 2 (April 2020): 238–45. http://dx.doi.org/10.1051/jnwpu/20203820238.

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To plan the path for UAV flying in the complex, dense and irregular obstacles environment, this paper proposed an obstacle collision-avoidance detection model and designed an UAV path planning algorithm based on irregular obstacles collision-avoidance detection (IOCAD), which includes irregular obstacles pretreatment method. The proposed method uses the grid method to model the environment. Rough set theory and convexity filling are used to pretreat the obstacles, and the ray method is used to select the available points. The intersection detection and the distance detection are held for the obstacle to the flight path. The objective function minimizes the distance from the obstacle to the flight path to get planned paths. The simulation results show that the proposed method can effectively plan the paths with the constraints of the assumed environment and UAV performances. It is shown that the performance of the proposed method is sensitive to the grid length and safety distance. The optimized values for the grid length and safety distance are 0.5 km and 0.4 km respectively.

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Liang, Yan Hua, and Cheng Tao Cai. "Research on Path Planning of Mine Rescue Robots Based on Fuzzy Control." Applied Mechanics and Materials 44-47 (December 2010): 3593–600. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3593.

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After a coal mine disaster, especially a gas and coal dust explosion, the space-restricted and unstructured underground terrain and explosive gas require coal mine rescue robots with good obstacle-surmounting performance. This paper tackles path-planning or collision avoidance problem. The collision avoidance problem in path planning of mobile robots was inspired from fuzzy control concept. The deduction of the path-planning algorithm is followed. Fuzzy rules and fuzzy inference based on experiences is built, it constructed a reasonable and applicable control reactive rule table. The simulation results are presented to validate the approach. And it is also proved that the control arithmetic is available in both static and dynamic obstacles in coal mine environment.

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D’Amato, Egidio, Massimiliano Mattei, and Immacolata Notaro. "Bi-level Flight Path Planning of UAV Formations with Collision Avoidance." Journal of Intelligent & Robotic Systems 93, no. 1-2 (May 11, 2018): 193–211. http://dx.doi.org/10.1007/s10846-018-0861-1.

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Nguyen, Quoc Cuong, Youngjun Kim, Sehyung Park, and HyukDong Kwon. "End-effector path planning and collision avoidance for robot-assisted surgery." International Journal of Precision Engineering and Manufacturing 17, no. 12 (December 2016): 1703–9. http://dx.doi.org/10.1007/s12541-016-0197-3.

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46

Wang, Pengwei, Song Gao, Liang Li, Binbin Sun, and Shuo Cheng. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm." Energies 12, no. 12 (June 19, 2019): 2342. http://dx.doi.org/10.3390/en12122342.

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Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are also verified.

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Machmudah, Affiani, and Setyamartana Parman. "Bezier Curve Collision-Free Route Planning Using Meta-Heuristic Optimization." International Journal of Artificial Intelligence & Robotics (IJAIR) 3, no. 1 (May 31, 2021): 1–14. http://dx.doi.org/10.25139/ijair.v3i1.3821.

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A collision-free route is very important for achieving sustainability in a manufacturing process and vehicle robot trajectories that commonly operate in a hazardous environment surrounded by obstacles. This paper presents a collision avoidance algorithm using a Bezier curve as a route path. The route planning is modeled as an optimization problem with the objective optimization is to minimize the route length considering an avoiding collision constraint. The collision-avoidance algorithm based on curve point analysis is developed incorporating metaheuristic optimizations, namely a Genetic Algorithm (GA) and a Grey Wolf Optimizer (GWO). In the collision avoidance algorithm, checking of curve point's position is important to evaluate the individual fitness value. The curve points are analyzed in such a way so that only the paths which are outside the obstacle area are selected. In this case, besides the minimum length as a fitness function, the constraint is the position of curve points from an obstacle. With the help of meta-heuristic optimization, the developed collision avoidance algorithm has been applied successfully to different types of obstacle geometries. The optimization problem is converted to the maximization problem so that the highest fitness value is used to measure the performance of the GA and GWO. In general, results show that the GWO outperforms the GA, where it exhibits the highest fitness value. However, the GA has shown better performance for the narrow passage problem than that of the GWO. Thus, for future research, implementing the hybrid technique combining the GA and the GWO to solve the advanced path planning is essential.

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Tam, CheeKuang, Richard Bucknall, and Alistair Greig. "Review of Collision Avoidance and Path Planning Methods for Ships in Close Range Encounters." Journal of Navigation 62, no. 3 (June 15, 2009): 455–76. http://dx.doi.org/10.1017/s0373463308005134.

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Efficient marine navigation through obstructions is still one of the many problems faced by the mariner. Many accidents can be traced to human error, recently increased traffic densities and the average cruise speed of ships impedes the collision avoidance decision making process further in the sense that decisions have to be made in reduced time. It seems logical that the decision making process be computerised and automated as a step forward to reduce the risk of collision. This article reviews the development of collision avoidance techniques and path planning for ships, particularly when engaged in close range encounters. In addition, previously published works have been categorised and their shortcomings highlighted in order to identify the ‘state of the art’ and issues in close range marine navigation.

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Lazarowska, Agnieszka. "Review of Collision Avoidance and Path Planning Methods for Ships Utilizing Radar Remote Sensing." Remote Sensing 13, no. 16 (August 18, 2021): 3265. http://dx.doi.org/10.3390/rs13163265.

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The paper presents a comparative analysis of recent collision avoidance and real-time path planning algorithms for ships. Compared methods utilize radar remote sensing for target ships detection. Different recently introduced approaches are briefly described and compared. An emphasis is put on input data reception using a radar as a remote sensing device applied in order to detect moving obstacles such as encountered ships. The most promising methods are highlighted and their advantages and limitations are discussed. Concluding remarks include proposals of further research directions in the development of collision avoidance methods utilizing radar remote sensing.

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BABA, Norio, and Naoyuki KUBOTA. "Path Planning and Collision Avoidance of a Robot Manipulator Using Genetic Algorithm." Journal of the Robotics Society of Japan 11, no. 2 (1993): 299–302. http://dx.doi.org/10.7210/jrsj.11.299.

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Journal articles on the topic 'Collision avoidance; Global path planning'

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