A Fast and Efficient Approach to Path Planning for Unmanned Vehicles

2006 ◽  
Author(s):  
Jayesh Amin ◽  
Jovan Boškovic ◽  
Raman Mehra
Keyword(s):  
Path Planning ◽  
Unmanned Vehicles ◽  
Efficient Approach

Path planning for unmanned vehicles with localization constraints

2019 ◽  
Vol 13 (5) ◽  
pp. 993-1009 ◽  
Author(s):  
Kaarthik Sundar ◽  
Sivakumar Rathinam ◽  
Rajnikant Sharma
Keyword(s):  
Path Planning ◽  
Unmanned Vehicles

scholarly journals Heuristics for Two Depot Heterogeneous Unmanned Vehicle Path Planning to Minimize Maximum Travel Cost

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2461 ◽  
Author(s):  
Jungyun Bae ◽  
Woojin Chung
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Target Location ◽  
Travel Cost ◽  
Unmanned Vehicles ◽  
Traveling Salesman ◽  
Planning Problem ◽  
Travel Costs ◽  
Primal Dual ◽  
Multiple Depot

A solution to the multiple depot heterogeneous traveling salesman problem with a min-max objective is in great demand with many potential applications of unmanned vehicles, as it is highly related to a reduction in the job completion time. As an initial idea for solving the min-max multiple depot heterogeneous traveling salesman problem, new heuristics for path planning problem of two heterogeneous unmanned vehicles are proposed in this article. Specifically, a task allocation and routing problem of two (structurally) heterogeneous unmanned vehicles that are located in distinctive depots and a set of targets to visit is considered. The unmanned vehicles, being heterogeneous, have different travel costs that are determined by their motion constraints. The objective is to find a tour for each vehicle such that each target location is visited at least once by one of the vehicles while the maximum travel cost is minimized. Two heuristics based on a primal-dual technique are proposed to solve the cases where the travel costs are symmetric and asymmetric. The computational results of the implementation have shown that the proposed algorithms produce feasible solutions of good quality within relatively short computation times.

scholarly journals Trajectory tracking method based on the circulation of feasible path planning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252542
Author(s):  
Yi Yu ◽  
Peng Han
Keyword(s):  
Path Planning ◽  
Trajectory Tracking ◽  
Control Method ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Path Generation ◽  
Trajectory Tracking Control ◽  
Core System ◽  
Tracking Method ◽  
Feasible Path

The control method is the central point of the unmanned vehicles. As the core system to guarantee the properties of self-decision and trajectory tracking of the unmanned vehicles, a new kind of trajectory tracking method based on the circulation of feasible path planning for the unmanned vehicles are proposed in this article which considered the dynamics and kinematics characteristics of vehicles. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform of the trajectory tracking simulation of unmanned vehicles is built considering the mechanical properties of system elements and the mechanical characteristics. Finally, the proposed trajectory tracking method is verified. The tracking error would be reduced to make sure the vehicles move along the pre-set virtual track.

Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework

2018 ◽  
Vol 06 (04) ◽  
pp. 231-250 ◽  
Author(s):  
Willson Amalraj Arokiasami ◽  
Prahlad Vadakkepat ◽  
Kay Chen Tan ◽  
Dipti Srinivasan
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Path Generation ◽  
Ground Vehicles ◽  
Agent Architectures ◽  
Time Path ◽  
Multi Agent

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.

scholarly journals A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

2021 ◽  
Vol 9 (11) ◽  
pp. 1243
Author(s):  
Charis Ntakolia ◽  
Dimitrios V. Lyridis
Keyword(s):  
Fuzzy Logic ◽  
Path Planning ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Computation Time ◽  
Computational Effort ◽  
Unmanned Vehicles ◽  
Maritime Transportation ◽  
Solution Quality ◽  
Multi Objective

Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time.

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