A Perception-Based Fuzzy Route Planing Algorithm for Autonomous Unmanned Ground Vehicles

2018 ◽  
Vol 06 (04) ◽  
pp. 251-266
Author(s):  
Phillip J. Durst ◽  
Christopher T. Goodin ◽  
Cindy L. Bethel ◽  
Derek T. Anderson ◽  
Daniel W. Carruth ◽  
...  
Keyword(s):  
Path Planning ◽  
Urban Area ◽  
Route Planning ◽  
Unmanned Ground Vehicles ◽  
Membership Functions ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Sensor Error ◽  
Planning Algorithm ◽  
Error Costs

Path planning plays an integral role in mission planning for ground vehicle operations in urban areas. Determining the optimum path through an urban area is a well-understood problem for traditional ground vehicles; however, in the case of autonomous unmanned ground vehicles (UGVs), additional factors must be considered. For an autonomous UGV, perception algorithms rather than platform mobility will be the limiting factor in operational capabilities. For this study, perception was incorporated into the path planning process by associating sensor error costs with traveling through nodes within an urban road network. Three common perception sensors were used for this study: GPS, LIDAR, and IMU. Multiple set aggregation operators were used to blend the sensor error costs into a single cost, and the effects of choice of aggregation operator on the chosen path were observed. To provide a robust path planning ability, a fuzzy route planning algorithm was developed using membership functions and fuzzy rules to allow for qualitative route planning in the case of generalized UGV performance. The fuzzy membership functions were then applied to several paths through the urban area to determine what sensors were optimized in each path to provide a measure of the UGV’s performance capabilities. The research presented in this paper shows the impacts that sensing/perception has on ground vehicle route planning by demonstrating a fuzzy route planning algorithm constructed by using a robust rule set that quantifies these impacts.

A Multi-Objective Optimization Approach for Multi-Vehicle Path Planning Problems considering Human-Robot Interactions

2022 ◽  
pp. 1-14
Author(s):  
Venkata Sirimuvva Chirala ◽  
Saravanan Venkatachalam ◽  
Jonathon Smereka ◽  
Sam Kassoumeh
Keyword(s):  
Path Planning ◽  
Human Robot Interaction ◽  
Planning Problem ◽  
Optimization Approach ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Robot Interaction ◽  
Ground Vehicle ◽  
Multi Objective ◽  
Vehicle Path

Abstract There has been unprecedented development in the field of unmanned ground vehicles (UGVs) over the past few years. UGVs have been used in many fields including civilian and military with applications such as military reconnaissance, transportation, and search and research missions. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. The level of autonomy given to these UGVs is a critical factor to consider. In many applications of multi-robotic systems like “search-and-rescue” missions, teamwork between human and robots is essential. In this paper, given a team of manned ground vehicles (MGVs) and unmanned ground vehicles (UGVs), the objective is to develop a model which can minimize the number of teams and total distance traveled while considering human-robot interaction (HRI) studies. The human costs of managing a team of UGVs by a manned ground vehicle (MGV) are based on human-robot interaction (HRI) studies. In this research, we introduce a combinatorial, multi objective ground vehicle path planning problem which takes human-robot interactions into consideration. The objective of the problem is to find: ideal number of teams of MGVs-UGVs that follow a leader-follower framework where a set of UGVs follow an MGV; and path for each team such that the missions are completed efficiently.

scholarly journals Analysis of the Hybrid Global Path Planning Algorithm for Different Environments

2019 ◽  
Vol 20 (1) ◽  
pp. 1-11
Author(s):  
Paulius Skačkauskas ◽  
Edgar Sokolovskij
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Experimental Tests ◽  
Important Research ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Research Fields ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Planning Approaches

Abstract To achieve the overall goal of realising an efficient and advantageous participation of autonomous ground vehicles in the transport system as fast as possible, a lot of work is being done in different and specific research fields. One of the most important research fields, which has a large impact on safe autonomous ground vehicle realisation, is the development of path planning algorithms. Therefore, this work describes in detail the development and application of a hybrid path planning algorithm. The described algorithm is based on classical and heuristic path planning approaches and can be applied in unstructured and structured environments. The efficiency of the algorithm was investigated by applying the algorithm and executing theoretical and experimental tests. The theoretical and experimental tests were executed while optimising different complexity paths. Results analysis demonstrated that the described algorithm can generate a smooth, dynamically feasible and collision-free path.

Path planning of collision avoidance for unmanned ground vehicles: A nonlinear model predictive control approach

2020 ◽  
pp. 095965182093784
Author(s):  
Peng Hang ◽  
Sunan Huang ◽  
Xinbo Chen ◽  
Kok Kiong Tan
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Nonlinear Model Predictive Control ◽  
Unmanned Ground Vehicles ◽  
Lateral Stability ◽  
Ground Vehicles ◽  
Moving Obstacles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In addition to the safety of collision avoidance, the safety of lateral stability is another critical issue for unmanned ground vehicles in the high-speed condition. This article presents an integrated path planning algorithm for unmanned ground vehicles to address the aforementioned two issues. Since visibility graph method is a very practical and effective path planning algorithm, it is used to plan the global collision avoidance path, which can generate the shortest path across the static obstacles from the start point to the final point. To improve the quality of the planned path and avoid uncertain moving obstacles, nonlinear model predictive control is used to optimize the path and conduct second path planning with the consideration of lateral stability. Considering that the moving trajectories of moving obstacles are uncertain, multivariate Gaussian distribution and polynomial fitting are utilized to predict the moving trajectories of moving obstacles. In the collision avoidance algorithm design, a series of constraints are taken into consideration, including the minimum turning radius, safe distance, control constraint, tracking error, etc. Four simulation conditions are carried out to verify the feasibility and accuracy of the comprehensive collision avoidance algorithm. Simulation results indicate that the algorithm can deal with both static and dynamic collision avoidance, and lateral stability.

Real-Time Energy-Efficient Path Planning for Unmanned Ground Vehicles Using Mission Prior Knowledge

2013 ◽  
Author(s):  
Amir Sadrpour ◽  
Jionghua (Judy) Jin ◽  
A. Galip Ulsoy
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Prior Knowledge ◽  
Energy Efficient ◽  
Energy Requirement ◽  
Sensory Information ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Energy Prediction ◽  
Planning Algorithm

Surveillance missions that involve unmanned ground vehicles (UGVs) include situations where a UGV has to choose between alternative paths to complete its mission. Currently, UGV missions are often limited by the available on-board energy. Thus, we propose a dynamic most energy-efficient path planning algorithm that integrates mission prior knowledge with real-time sensory information to identify the mission’s most energy-efficient path. Our proposed approach predicts and updates the distribution of energy requirement of alternative paths using recursive Bayesian estimation through two stages: (1) exploration — road segments are explored to reduce their energy prediction uncertainty; (2) exploitation — the most energy-efficient path is selected using the collected information in the exploration stage and is traversed. Our simulation results show that the proposed approach outperforms offline methods, as well as a method that only relies on exploitation to identify the most energy-efficient path.

Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

2020 ◽  
pp. 095440702097831
Author(s):  
Yimin Chen ◽  
Chuan Hu ◽  
Yechen Qin ◽  
Mingjun Li ◽  
Xiaolin Song
Keyword(s):  
Feedback Control ◽  
Path Planning ◽  
Obstacle Avoidance ◽  
Output Feedback ◽  
Output Feedback Control ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Velocity Signal ◽  
Moving Obstacles ◽  
Avoidance Strategy

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

scholarly journals A Green Ant-Based method for Path Planning of Unmanned Ground Vehicles

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 1820-1832 ◽  
Author(s):  
Mohammad Rzea Jabbarpour ◽  
Houman Zarrabi ◽  
Jason J. Jung ◽  
Pankoo Kim
Keyword(s):  
Path Planning ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles

scholarly journals Towards Energy Efficient Follow Behaviors for Unmanned Ground Vehicles Over Rugged Terrains

2012 ◽  
Author(s):  
Madan M. Dabbeeru ◽  
Joshua D. Langsfeld ◽  
Petr Svec ◽  
Satyandra K. Gupta
Keyword(s):  
State Space ◽  
Energy Efficient ◽  
The State ◽  
Unmanned Ground Vehicles ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Rugged Terrain ◽  
Simulation Results ◽  
The Relationship

This paper focuses on the development of a follow behavior for an unmanned ground vehicle (UGV) in collaborative scenarios. The scenario being studied involves a human traveling over a rugged terrain on foot. The UGV follows the human. We present an approach for automatically generating a reactive energy-efficient follow behavior that maps the vehicle’s states into motion goals. We start by partitioning the state space that encodes the relationship between the state of the vehicle and the human’s state, and the environment. For each cell in the partitioned state space, we either directly generate the motion goal for the vehicle to execute or a function that produces the motion goal. The motion goal defines not only the location towards which the vehicle should move but also specifies a zero activity zone around the human within which the vehicle is supposed to slow down and remain stationary to save its energy until it gets outside the margin caused by the movement of the human. Our approach utilizes off-line simulations to assess the performance of the generated behavior. Our simulation results show that the automatically generated follow behavior significantly outperforms a simple conservative tracking rule in terms of distance traveled and violation of proximity constraints. We anticipate that the approach presented in this paper will ultimately enable us to implement energy efficient follow behaviors on physical UGVs.

Sign in / Sign up

Export Citation Format

Share Document