Résultats de recherche pour « Autonomous unmanned vehicles »

This paper explores the use of autonomous unmanned vehicles to support power grid operations. With built-in batteries and the capability to carry additional battery energy storage, the rising number of autonomous vehicles can represent a substantial amount of capacity that is currently underutilized in the power grid. Unlike traditional electric vehicles that require drivers, the operations of autonomous vehicles can be performed without human intervention. To guide idle vehicles to autonomously support power grids, we propose a tractable optimization-based method to effectively integrate these "mobile batteries" into grid operations. During real-time operations, the vehicles are strategically routed to target locations to maintain power balance and reduce operating costs. Numerical studies have confirmed both the validity and the scalability of the proposed algorithm to efficiently integrate autonomous vehicles into routine power system operations.

We study models of coordination, negotiation and collaboration in multi-agent systems (MAS). More specifically, we investigate scalable models and protocols for various distributed consensus coordination problems in large-scale MAS. Examples of such problems include conflict avoidance, leader election and coalition formation. We are particularly interested in application domains where robotic or unmanned vehicle agents interact with each other in real-time, as they try to jointly complete various tasks in complex dynamic environments, and where decisions often need to be made “on the fly”. Such MAS applications, we argue, necessitate a multi-tiered approach to learning how to coordinate effectively. One such collaborative MAS application domain is ensembles of autonomous micro unmanned aerial vehicles (micro-UAVs). A large ensemble of micro-UAVs on a complex, multi-stage mission comprised of many diverse tasks with varying time and other resource requirements provides an excellent framework for studying multi-tiered learning how to better coordinate. A variety of tasks and their resource demands, complexity and unpredictability of the overall environment, types of coordination problems that the UAVs may encounter in the course of their mission, multiple time scales at which the overall system can use learning and adaptation in order to perform better in the future, and multiple logical and organizational levels at which large ensembles of micro-UAVs can be analyzed and optimized, all suggest the need for a multi-tiered approach to learning. We outline our theoretical and conceptual framework that integrates reinforcement learning and meta-learning, and discuss potential benefits that our framework could provide for enabling autonomous micro-UAVs (and other types of autonomous vehicles) to coordinate more effectively.

The rapid development of robotics has benefited by more and more people putting their attention to it. With the demand for robots is growing for the purpose of fulfilling tasks instead of humans, how to control the robot better is becoming a hot topic. For obstacle avoidance, we proposed algorithms for both 2D planar environments and 3D space environments. The example cases we raise are those that need to be addressed but have always been ignored. In addition, we put efforts into trajectory planning for robots. The two scenarios we set are self-driving cars on the road and reconnaissance and surveillance of drones. For future expectations, there are some possible directions. How to combine traditional navigation algorithms and high-tech algorithms together so as to fulfill the tasks perfectly while the computational efficiency is not too high is a worthy topic. In addition, extending the obstacle avoidance algorithms to more competitive situations. Moreover, cooperation among multi robots are worth attention by researchers. All in all, there is still a long way to go for the development of navigation and control of mobile robots. Despite this, we believe we do not need to wait for too long time to see the revolution of robots.

We investigated the potential of multi-layer extreme learning machines (MLELMs) for trajectory tracking in autonomous unmanned vehicles, focusing on an unmanned surface vehicle (USV). MLELMs were used to design position and attitude controllers within a two-loop control architecture, ensuring that the USV accurately follows a reference trajectory. The performance of an MLELM-based controller in a tracking task was evaluated via numerical simulations of a USV dynamic model governed by nonlinear equations. The computational results confirmed the feasibility of the MLELM to accomplish this task with appropriate accuracy, demonstrating its potential applicability in control systems.

Artificial Intelligence (AI) is becoming a hot topic in the field of robotic research in the last few decades. Autonomous Unmanned Vehicles (AUV) are being used for different tasks like rescue, search, monitoring, aerial operations as well as underwater operations even AUV can aid where human reachability is impossible. Localization, tracking, and mapping are fundamentals of an autonomous system. The main problem of AUV, which attracts researchers, is the simultaneous localization and mapping, where no external positioning source is available like GPS. There are many proposed techniques and algorithms which can be used to solve this problem of AUV like GPS (Global Positioning System), Motion Capture System (MCS), Visual System, etc. with limitations. Some probabilistic solutions like Graph SLAM, EKF based SALM, and Fast SLAM are also available for this problem. EKF based SLAM is used for the non-linear model but has different issues (like inconsistency) when the map becomes large and complex. It does not work well with the non-Gaussian distribution. Fast SLAM algorithm is used with the non-Gaussian distribution. It can provide high speed computation and good accuracy but has issues during the resampling processes like particle depletion and degeneracy. On the other hand, Graph based SLAM can deal with large and complex maps and can process a large number of landmarks accurately and it can perform much better than EKF and Fast SLAM.

ABSTRACT To address the challenge of parallel parking in narrow environments, a multi‐segment path planning method is proposed for autonomous unmanned vehicles (AUVs), which integrates circular arc and dual clothoid curves, ensuring continuous path curvature and minimal longitudinal space occupancy. First, considering the narrow environmental constraints and the parameters of AUV, the planning for the preparatory phase path and the parking‐in phase path are completed, respectively. The role of the preparatory phase path is to guide the AUV to the target point, and the parking‐in phase path can guide the AUV into the target parking space. Then, by using the particle swarm optimization (PSO) algorithm with a penalty function framework, a multi‐constraint nonlinear programming path function model is established to seek out the parking path. Subsequently, the pure pursuit (PP) algorithm is employed to control the movement of AUV along the planned path. Finally, the simulation result and real‐vehicle experiment are conducted, which show that the parking path planning method proposed in this article enables AUV to safely complete parking operations in the narrow environment.