جستجوی مقالات مرتبط با کلیدواژه "path planning" در نشریات گروه "مکانیک"

The Artificial Potential Fields approach is amongst the widely used path planning methods in continuous environments. However, the implementation of it in multi-robot path planning encounters challenges such as the local-minima and an increase in traffic probability with the rise in the number of robots. The purpose of the proposed method is to improve multi-robot path planning in complex environments. A new adaptive potential function is introduced that reduces the probability of the robots entering an area at the same time and thus reducing the probability of traffic. Also, new potential functions have been proposed that lead to smoother paths with less traverse time when the robot encounters obstacles. In these functions, in addition to the position of robots and obstacles, heading of the robot and the position of the target are also considered. In order to evaluate this method, a distributed software architecture has been designed and implemented in the framework of the robot operating system. In this architecture, as robots move, new robots can join the operation or new tasks can be assigned to robots. Two series of real-time simulations are carried out in the Gazebo environment. The results show that the use of the proposed potential functions leads to a decrease in the convergence of the robots. In the simulation done for 2 robots, proposed method has resulted in a 35% reduction in the traversal time. While in case of 15 robots in the same map, a 50% reduction in the traversal time has been achieved.

The behavior of self-driving cars is categorized into different strategic, tactical and operational levels in a hierarchical structure. In this research, a study at the local planning level aims to design an automatic turning system and Focus on path and maneuver planning (single and multi-point). The turn around maneuver as a common scenario in the required conditions, includes one or more forward and backward movements, in which the vehicle must be in the proper position in the opposite lane of the road and with a heading angle of 180 degrees. Moreover, the condition of collision avoidance is considered during the maneuver. For this purpose, the four-wheeled model of the vehicle with the relevant geometry is described; Next, on the basis of the deduced minimum permissible road width, a decision is made according to the objective of the minimum number of commands. Then the path design is presented using the proposed geometrical method with circle arcs for each initial position and orientation and generalized to the backward turning. The proposed method has advantages such as low computational cost and can be used for different situations of the initial states of the vehicle. The result of the turning simulation with sample vehicle data and continuous evaluation of the most feasible maneuver confirms the efficiency of the chosen planning approach. In a case study for the Samand car, while proper planning was done, a distance reduction of about 11% for the road with a width of 12 meters is observed compared to the path planned by one of the references.

Multi-robot path planning problem involves some challenges. One of them is the exponential increase in the size of the search space as a result of increasing the number of robots in the operating environment. Therefore, there is a need for algorithms with high computational performance to plan optimal and collision-free paths in a limited time. In this article, a centralized path planning algorithm is presented. The algorithm is a heuristic incremental search, in which the D* Lite algorithm has been adapted for the multi-robot case. The concept of occupancy time has been embedded into the environment model to avoid path interference. A centralized function has been designed to update the environment model and robot data. To evaluate the method, two groups of simulations of static and dynamic types were carried out. The static simulations focused on studying the effect of algorithm parameters, and it was shown that the algorithm can plan paths for up to 40 robots in an environment having 55 percent free space. The dynamic simulations were carried out in Gazebo, a real-time and dynamic physical simulator. The results were compared to a baseline method based on potential fields. The number of robots was increased to 14, and it was demonstrated that for 9 robots and more, the potential field approach either fails or has a rapid increase in computation time, while the proposed method can find feasible solutions in a limited time.

Due to the high fatalities of head-on accidents, the design of intelligent systems to prevent such severe collisions is essential. In this study, path planning for head-on collision avoidance with a deviated vehicle from the opposite lane has been investigated. The main approach is based on a model predictive controller with 2 seconds of prediction horizon and a linearized prediction model with low errors near the operational conditions. A conservative method is used for lateral motion prediction of the deviated vehicle and based on that, the collision avoidance constraints of the model predictive planner are simply modeled by a new approach. Moreover, a novel method to choose the proper swerve direction of evasive maneuver is proposed. This method is based on keeping the ego vehicle away from dangerous directions and has different criteria for far and close encounters. The final algorithm is capable to control the steering of the prediction model with a constrained lateral acceleration and calculates safe and maneuverable paths for the aforementioned scenario. Four simulations are conducted to validate the algorithm in far and close encountering, and critical conditions of choosing swerve direction. Results show the robustness of the path planner, even to sudden deviations at close distances and with high lateral accelerations.

Path design is one of the open research bottlenecks to develop autonomy in unmanned robots. In urban spaces and closed environments, with increasing the number of obstacles and constraints, the computational complexity required for the completed path planning algorithms increases with order O (n2). The main purpose of this manuscript is to present a path planning algorithm based on the random sampling method for the vertical take-off and landing vehicle (VTLV) and its real-time test in the xPC-Target toolboxes. For this purpose, the rapidly exploring random tree (RRT) algorithm is presented. The proposed algorithm is completely probabilistic and also the non-holonomic constraints of the VTLV are integrated into the vertices of the search tree. In order to validate and evaluate the proposed path planning algorithm before performing risky and costly field tests, the processor-in-the-loop (PIL) test, in MATLAB xPC-Target software environment is considered. In the PIL test, two test scenarios with different complexity have been developed. The results show that the proposed rapidly exploring random tree algorithm is able to plan a primary path for the robot by using its random nature in a rapid manner. Also, due to the involvement of the robot dynamic model in the process of generating random vertices and search tree edges, both types of kinematic and dynamic (kinodynamic) constraints have been considered in the planned path and therefore lead to the design of a practical path with high intercept ability through the robot.

With the increasing number of road accidents and driver assistance systems development, the automated vehicles importance has increased more than ever. As the issue of automated vehicles comes up, attending to their safety, and the impact of the other vehicles in traffic flow on their performance dramatically increased. One of the most important problems for automated vehicles is designing and controlling the trajectory regarding the surrounding vehicles in transient dynamic traffic conditions during complicated maneuvers. Although various studies have been performed in the field of lane change in dynamic traffic conditions and even in critical high speed, considering the transient dynamic conditions has been limited to the beginning of the maneuver and no solution has been provided for the surrounding vehicles’ immediate changes during the maneuver. The algorithm presented in this paper is able to design new safe optimized trajectories according to the sudden decisions of the surrounding vehicles during the lane change maneuver, also ensures collision avoidance in the whole maneuver via vehicle’s simultaneous longitudinal and lateral control. After evaluating the decision-making unit’s performance by real driving tests, the presented algorithm is simulated with different scenarios in complicated transient dynamic traffic conditions by using MATLAB software and its desired performance has been proven in the dynamic environment of IPG CarMaker, in the presence of surrounding vehicles.

Recent developments in mission implementation and agent control have drawn attention to the performing of cooperative missions. Decision-making systems are among the most important parts of such missions. The aim of these systems is efficient task assignment and path planning. This paper combines a task assignment approach and a path planning method to tackle collaborative problems. The task assignment approach, used in this paper, is based on an improved linear programming approach for its high accuracy. The path planning method is based on Dubins curve to handle the limitation movements of fixed-wing fighters, investigated in this paper. The proposed methods perform based on the utilized fighter’s performance characteristics and flight constraints in a dynamic environment with moving targets. The path planning approach is based on specific constraints, including certain output vectors of each point as well as input ones to the target points, minimum rotational angle, and performance factors of fighters. In the next step, Dubins path planning output is used as the necessary coefficient to solve the task assignment problem of cooperative fighters. By using a heuristic hierarchical approach based on linear programming, a comprehensive path planning and task assignment of a fleet of fighters is completed. The presented results show optimal performance and higher speed to solve rather than the classical approaches, which is capable to cover the dynamic environment as well.

Today, unmanned aerial vehicles are highly considered for both military and commercial fields due to low cost, high maneuverability, and good survival. One of the most important design challenges of multi-UAV systems is mission planning. In the broad class of unmanned aerial vehicles (UAV), Quadrotor is an important member. The capabilities of this vehicle in load transportation has attracted the attention of many research groups around the world. In this paper, path planning based on the minimum energy consumption is studied for load transportation. The purpose of the present study is to investigate the effect of proposed cost function in order to obtain optimal path for transporting loads based on reducing the energy consumption of quadrotors. The results indicate the 35.29% energy consumption of multi UAV compared to the energy consumption of an individual quadrotor. This also leads to an increase in load carrying capacity. On the other hand, the leader- follower formation is preserved until the end of the path based on the defined relationships. The simulation results illustrate the power and efficiency of the method to overcome the high nonlinearity nature of the problem such as path optimization of multi-rotor helicopters.