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.

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.

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.

scholarly journals Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 642
Author(s):  
Luis Miguel González de Santos ◽  
Ernesto Frías Nores ◽  
Joaquín Martínez Sánchez ◽  
Higinio González Jorge
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Execution Time ◽  
Point Cloud ◽  
Indoor Environments ◽  
Calculation Algorithm ◽  
Time Restrictions ◽  
Planning Algorithm ◽  
Inspection Tasks ◽  
Path Planning Algorithm

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

scholarly journals Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

2021 ◽  
Vol 10 (5) ◽  
pp. 439-448
Author(s):  
Hrishikesh Dey ◽  
Rithika Ranadive ◽  
Abhishek Chaudhari
Keyword(s):  
Path Planning ◽  
Velocity Profile ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Autonomous Driving ◽  
Navigation Algorithm ◽  
Velocity Planning ◽  
Planning Algorithm ◽  
Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

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