Path Planning Optimization Problem Research Articles

Trajectory Planning Design for Parallel Parking of Autonomous Ground Vehicles with Improved Safe Travel Corridor

In this paper, the concept of symmetry is utilized to design the trajectory planning for parallel parking of autonomous ground vehicles—that is, the construction and the solution of the optimization-based trajectory planning approach are symmetrical. Parking is the main factor that troubles most drivers for their daily driving travel, and it can even lead to traffic congestion in severe cases. With the rise of new intelligent and autonomous vehicles, automatic parking seems to have become a trend. Traditional geometric planning methods are less adaptable to parking scenarios, while the parking paths planned by graph search methods may only achieve local optimality. Additionally, significant computational time is often required by numerical optimization methods to find a parking path when a good initial solution is not available. This paper presents a hierarchical trajectory planning approach for high-quality parallel parking of autonomous ground vehicles. The approach begins with a graph search layer to roughly generate an initial solution, which is refined by a numerical optimization layer to produce a high-quality parallel parking trajectory. Considering the high dimensionality and difficulty of finding an optimal solution for the path planning optimization problem, this paper proposes an improved safe travel corridor (I-STC) with the construction of collision constraints isolated from surrounding environmental obstacles. By constructing collision constraints of the I-STC based on the initial solution, the proposed method avoids the complexities and non-differentiability of traditional obstacle avoidance constraints, and simplifies the problem modeling the subsequent numerical optimization process. The simulation results demonstrate that the I-STC is capable of generating parallel parking trajectories with both comfort and safety.

A Lightweight Mobile-Anchor-Based Multi-Target Outdoor Localization Scheme Using LoRa Communication

In recent years, multi-target localization has been identified as an essential technology for delivering on the promise of the Internet of Things (IoT). Multi-target localization has been well-studied and widely deployed in conventional communications systems, but the techniques used in such systems are largely unable to meet the power, cost, and accuracy requirements needed for IoT. In this article, we propose a lightweight Mobile Anchor multi-tarGet IoT loCalization system (MAGIC), which is capable of localizing multiple targets in a low-power and cost efficient manner. MAGIC utilizes a single mobile anchor consisting of a LoRa gateway and a commercial smartphone in order to localize multiple IoT devices embedded with LoRa tags. The anchor moves and communicates with the LoRa tags from multiple locations, greatly reducing the deployment cost compared to a more traditional system with multiple stationary anchors. We formulate the path planning optimization problem of the mobile anchor to minimize the path length while fully accounting for environmental uncertainties. To efficiently solve the optimization problem, we design an online moving strategy that generates the path in real-time as the anchor moves. Through simulation and experimental studies we demonstrate the effectiveness of the proposed scheme both in an empty baseline environment and in a complicated setting around the buildings. Results indicate that our scheme can significantly enhance the localization accuracy and reduce the required path length for the anchor. Compared to the existing outdoor localization systems, the proposed approach shows advantages not only in the localization accuracy but also the overall cost including device cost, spectrum usage and energy consumption.

Learning-Based UAV Path Planning for Data Collection With Integrated Collision Avoidance

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV noncooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task. In this article, we consider a path-planning optimization problem to maximize the collected data from multiple IoT nodes under realistic constraints. The considered multi-UAV noncooperative scenarios involve a random number of other UAVs in addition to the typical UAV, and UAVs do not communicate or share information among each other. We translate the problem into a Markov decision process (MDP) with parameterized states, permissible actions, and detailed reward functions. Dueling double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (D3QN) is proposed to learn the decision-making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). The proposed algorithm can adapt to various missions in various scenarios, e.g., different numbers and positions of IoT nodes, different amount of data to be collected, and different numbers and positions of other UAVs. Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.

Comparison of Eulerian and Hamiltonian circuits for evolutionary-based path planning of an autonomous surface vehicle for monitoring Ypacarai Lake

An evolutionary-based path planning is designed for an autonomous surface vehicle (ASV) used in environmental monitoring tasks. The main objective is that the ASV covers the maximum area of a large mass of water such as the Ypacarai Lake while taking water samples for sensing pollution conditions. Such coverage problem is transformed into a path planning optimization problem through the placement of a set of data beacons located at the shore of the lake and considering the relationship between the distance traveled by the ASV and the area of the lake covered. The optimal set of beacons to be visited by the ASV has been modeled through two different approaches such as Hamiltonian and Eulerian circuits. When Hamiltonian circuits are used, all the beacons should be visited only once. In the case of Eulerian circuits, the only limitation is that repeated routes cannot exist between two beacons. Both models have important implications on the possible trajectories of ASV throughout the lake. In this paper, we compare the application of both models for the optimization of the proposed evolutionary-based path planning. Due to the complexity of the optimization problem, a metaheuristic technique like a Genetic Algorithm (GA) is used to obtain quasi-optimal solutions in both models. The models have been compared by simulation and the results reveal that the Eulerian circuit approach can achieve an improvement of 2% when comparing to the Hamiltonian circuit approach.

Fast Link Scheduling Policies for Persistent Data Ferrying

The persistent data-ferrying problem has a naturally cascaded structure between vehicle and communication dynamics, allowing the wireless link scheduling optimization to be encapsulated within a path-planning optimization problem. This work derives and analyzes policies to solve the inner link scheduling problem quickly. Given a path and properties about the communication environment, the link scheduling problem is formulated as a binary integer linear program with unique ferrying constraints. The performances of several heuristic link scheduling policies are analyzed in terms of computation effort and achievable throughput of the solution. The performance characteristics are then validated through simulation, showing that the greedy knapsack solution achieves within 99% of the optimal allocation in less than 2% of the computation time compared to traditional solvers. The quickness of this policy reduces the computational complexity needed to solve the full data-ferry problem.

A new genetic algorithm approach to smooth path planning for mobile robots

Purpose The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve. Design/methodology/approach The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points. Findings This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed. Research limitations/implications There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research. Originality/value The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.

Distributed Path Planning for a UAV Communication Chain by Dual Decomposition

In this paper, a distributed approach to Model Predictive Control (MPC)-based path planning for a UAV (Unmanned Aerial Vehicle) communication chain under radio path loss constraints is proposed. It is based on the dynamic dual decomposition method and reformulates the centralized path planning optimization problem with coupled constraints into an equivalent distributed path planning optimization problem, where the resulting sub-problems are completely decoupled. The MPC-based optimization sub-problems are computed autonomously within each UAV, using convex quadratic programming and gradient iterations, with the requirement that each UAV communicates its current measured position and the computed optimal velocity trajectory to its neighbouring UAVs.