Browse Topic: Trajectory control
The application trend of automated driving is gaining significant concern, making it increasingly crucial to validate automated driving within the stochastic simulated traffic flow environment from both time and cost perspectives. The stochastic traffic flow model attempts to encapsulate the variability inherent in traffic conditions through a stochastic process. This approach is particularly important as it accounts for the unpredictable nature of traffic, which is often not fully captured by traditional deterministic testing scenarios. However, while stochastic traffic flow models have made strides in simulating the behavior of traffic participants, there remains a significant oversight in the simulation of vehicles’ driving trajectories, leading to unrealistic portrayals of their behaviors. The trajectories of vehicles are a critical component in the overall behavior of traffic flow, and their accurate representation is essential for the simulation to reflect real-world driving
Hypersonic platforms provide a challenge for flight test campaigns due to the application's flight profiles and environments. The hypersonic environment is generally classified as any speed above Mach 5, although there are finer distinctions, such as “high hypersonic” (between Mach 10 to 25) and “reentry” (above Mach 25). Hypersonic speeds are accompanied, in general, by a small shock standoff distance. As the Mach number increases, the entropy layer of the air around the platform changes rapidly, and there are accompanying vortical flows. Also, a significant amount of aerodynamic heating causes the air around the platform to disassociate and ionize. From a flight test perspective, this matters because the plasma and the ionization interfere with the radio frequency (RF) channels. This interference reduces the telemetry links' reliability and backup techniques must be employed to guarantee the reception of acquired data. Additionally, the flight test instrumentation (FTI) package needs
ABSTRACT This paper presents two techniques for autonomous convoy operations, one based on the Ranger localization system and the other a path planning technique within the Robotic Technology Kernel called Vaquerito. The first solution, Ranger, is a high-precision localization system developed by Southwest Research Institute® (SwRI®) that uses an inexpensive downward-facing camera and a simple lighting and electronics package. It is easily integrated onto vehicle platforms of almost any size, making it ideal for heterogeneous convoys. The second solution, Vaquerito, is a human-centered path planning technique that takes a hand-drawn map of a route and matches it to the perceived environment in real time to follow a route known to the operator, but not to the vehicle. Citation: N. Alton, M. Bries, J. Hernandez, “Autonomous Convoy Operations in the Robotic Technology Kernel (RTK)”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI
ABSTRACT This work investigates the effects of obstacle uncertainty on the speed, distance, and feasibility of a planned traversal path. Simulation results for artificial and real-world environments are used to numerically quantify how geometric uncertainty within a map affects path traversal cost. A significant outcome of this research is the discovery of a relationship between increasing uncertainty and path cost. As obstacle uncertainty increases, previously planned routes can become infeasible as they effectively become blocked off due to uncertainty in the obstacle geometry. This paper illustrates a method that can serve to increase the speed, simplicity, and reliability of path planning, while allowing uncertainty to be included in the mobility analysis. Citation: S. Tau, S. Brennan, K. Reichard, J. Pentzer, D. Gorsich, “The Effects of Obstacle Dimensional Uncertainty on Path Planning in Cluttered Environments”, In Proceedings of the Ground Vehicle Systems Engineering and
ABSTRACT Autonomous vehicles rely on path planning to guide them towards their destination. These paths are susceptible to interruption by impassable hazards detected at the local scale via on-board sensors, and malicious disruption. We define robustness as an additional parameter which can be incorporated into multi-objective optimization functions for path planning. The robustness at any point is the output of a function of the isochrone map at that point for a set travel time. The function calculates the sum of the difference in area between the isochrone map and the isochrone map with an impassable semi-circle hazard inserted in each of the four cardinal directions. We calculate and compare two different Pareto paths which use robustness as an input parameter with different weights. Citation: T. Jonsson Damgaard, M. Rittri, P. Franz, A. Halota “Robust Path Planning in the Battlefield,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA
ABSTRACT Future autonomous combat vehicles will need to travel off-road through poorly mapped environments. Three-dimensional topography may be known only to a limited extent (e.g. coarse height), but this will likely be noisy and of limited resolution. For ground vehicles, 3D topography will impact how far ahead the vehicle can “see”. Higher vantage points and clear views provide much more useful path planning data than lower vantage points and occluded views from trees and structures. The challenge is incorporating this knowledge into a path planning solution. When should the robot climb higher to get a better view or else continue moving along the shortest path predicted by current information? We investigated the use of Deep Q-Networks (DQN) to reason over this decision space, comparing performance to conventional methods. In the presence of significant sensor noise, the DQN was more successful in finding a path to the target than A* for all but one type of terrain. Citation: E
ABSTRACT Robot path-planning is a central task for navigation and most path-planners perform well in mapped environments with explicit obstacle boundaries. However, many obstacle fields are better defined by the probability of obstacles and obstacle geometries rather than by explicit locations. Few tools and data structures exist, other than repeated simulations, to predict robot mobility in these situations. Previously, it was shown that geometric obstacle properties could be used to estimate properties of paths routing around these obstacles, looking only at maps and avoiding the task of path planning [1]. This required knowing obstacle geometries relative to travel direction. This work presents a method for representing obstacle geometry, at arbitrary orientations and positions, and therefore a probabilistic model for determining if space near an obstacle is occupied. This paper explains the theory behind this method, uses this method to calculate the portion of a straight path
ABSTRACT Route 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 a UGV, perception, rather than mobility, will be the limiting factor in determining operational areas. Current ground vehicle route planning techniques do not take perception concerns into account, and these techniques are not suited for route planning for UGVs. For this study, perception was incorporated into the route planning process by including expected sensor accuracy for GPS, LIDAR, and inertial sensors into the path planning algorithm. The path planner also accounts for additional factors related to UGV performance capabilities that affect autonomous navigation
ABSTRACT This paper presents a novel adaptive sampling method using intelligent UAVs in battlefields to help soldiers with awareness of environments. A UAV can perform as a robotic wingman in soldier formations, compensating for that cannot be scouted by soldiers, even being exposed to enemy fire. With portable size, the UAV is easily carried and flown for scouting tasks anytime. The flexibility of UAVs makes it possible to collect measurements sequentially. Each measurement is adaptively designed and determined from the Bayesian perspective to increase the fidelity of battlefields. Wavelet structure is considered to optimize measurement projections to substantially reduce the number of measurements based on compressive sensing framework. More specifically, each measurement is optimized by maximizing the posterior variance inferred from existing informative data. A motion planning algorithm for UAVs is designed based on the distribution of optimal measurements, striking a balance
ABSTRACT This research proposes a human-multirobot system with semi-autonomous ground robots and UAV view for contaminant localization tasks. A novel Augmented Reality based operator interface has been developed. The interface uses an over-watch camera view of the robotic environment and allows the operator to direct each robot individually or in groups. It uses an A* path planning algorithm to ensure obstacles are avoided and frees the operator for higher-level tasks. It also displays sensor information from each individual robot directly on the robot in the video view. In addition, a combined sensor view can also be displayed which helps the user pin point source information. The sensors on each robot monitor the contaminant levels and a virtual display of the levels is given to the user and allows him to direct the multiple ground robots towards the hidden target. This paper reviews the user interface and describes several initial usability tests that were performed. This research
Resupply missions are critical logistical parts of modern warfare. Supply vehicles carrying fuel and ammunition are high-value targets meaning that the route chosen to approach such a mission is sensitive to risk and a critical time of delivery. We address the problem of a supply vehicle that needs to find a secure path to link up with a mobile frontline unit that has a fixed known itinerary. This paper presents a resupply path planning algorithm, the Adaptive Intercepting Path Planning (AIPP) algorithm, that balances risk and travel time to find the most suitable rendezvous point among several. The algorithm generates the least risky route that meets the rendezvous deadline
Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on. Secondly, data driven or AI based vehicle models have been reviewed, and their specific applications in automatic driving and their modeling and training
In contemporary trajectory planning research, it is common to rely on point-mass model for trajectory planning. However, this often leads to the generation of trajectories that do not adhere to the vehicle dynamics, thereby increasing the complexity of trajectory tracking control. This paper proposes a local trajectory planning algorithm that combines sampling and sequential quadratic optimization, considering the vehicle dynamics model. Initially, the vehicle trajectory is characterized by utilizing vehicle dynamic control variables, including the front wheel angle and the longitudinal speed. Next, a cluster of sampling points for the anticipated point corresponding to the current vehicle position is obtained through a sampling algorithm based on the vehicle's current state. Then, the trajectory planning problem between these two points is modeled as a sequential quadratic optimization problem. By employing an offline method, the optimal trajectory set between the present position and
Autonomous driving technology represents a significant direction for future transportation, encompassing four key aspects: perception, planning, decision-making, and control. Among these aspects, vehicle trajectory planning and control are crucial for achieving safe and efficient autonomous driving. This paper introduces a Combined Model Predictive Control algorithm aimed at ensuring collision-free and comfortable driving while adhering to appropriate lane trajectories. Due to the algorithm is divided into two layers, it is also called the Bi-Level Model Predictive Control algorithm (BLMPC). The BLMPC algorithm comprises two layers. The upper-level trajectory planner, to reduce planning time, employs a point mass model that neglects the vehicle's physical dimensions as the planning model. Additionally, obstacle avoidance cost functions are integrated into the planning process. In the upper trajectory planner, the fifth-order polynomial algorithm is also used to smooth the planned
Vehicle navigation in off-road environments is challenging due to terrain uncertainty. Various approaches that account for factors such as terrain trafficability, vehicle dynamics, and energy utilization have been investigated. However, these are not sufficient to ensure safe navigation of optionally manned ground vehicles that are prone to detection using thermal infrared (IR) seekers in combat missions. This work is directed towards the development of a vehicle IR signature aware navigation stack comprised of global and local planner modules to realize safe navigation for optionally manned ground vehicles. The global planner used A* search heuristics designed to find the optimal path that minimizes the vehicle thermal signature metric on the map of terrain’s apparent temperature. The local planner used a model-predictive control (MPC) algorithm to achieve integrated motion planning and control of the vehicle to follow the path waypoints provided by the global planner. Vehicle
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