Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

The motion control of unmanned ground vehicles is essential in the industry of automation. In this paper, the sensors of a fuzzy inference system that is based on a navigation technique for an unmanned ground vehicle are formulated in a cluttered dynamic environment. This fuzzy inference system consists of two controllers. The first controller uses three sensors based on the distances from the front, the right and the left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to choose the optimal route based on the dynamic environment and reach the desired destination with minimum running power and time. Experimental tests have been carried out in three different case studies to investigate the validation and effectiveness of the introduced controllers of the fuzzy inference system. The reported simulation results are conducted using MATLAB software package. The results show that the controllers of the fuzzy inference system consistently perform the maneuvering task and route planning efficiently even in a complex environment with populated dynamic obstacles

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Autonomous vehicles operate in real time traffic scenarios and aim to reach their destination from their source in the most efficient manner possible. Research in mobile robotics provides a variety of sophisticated means with which to plan the path of these vehicles. Conversely professional human drivers usually drive using instinctive means, which enables them to reach their goal almost optimally whilst still obeying all traffic laws. In this paper we propose the use of fuzzy logic for novel motion planning. The planner is generated using an evolutionary algorithm which resembles the learning stage of professional drivers. Whether to overtake or not, is a decision which affects one’s driving and the decision is made using some deliberation. We further extend the approach to perform decision making regarding overtaking for all vehicles. Further we coordinate the motion of the vehicles at a traffic crossing to avoid any potential jam or collision. Experimental results prove that by using this approach we have been able to make the vehicles move in an optimal manner in a variety of scenarios

A Multiagent Approach to Qualitative Navigation in Robotics

Navigation in unknown unstructured environments is still a difficult open problem in the field of robotics. In this PhD thesis we present a novel approach for robot navigation based on the combination of landmark-based navigation, fuzzy distances and angles representation and multiagent coordination based on a bidding mechanism. The objective has been to have a robust navigation system with orientation sense for unstructured environments using visual information. To achieve such objective we have focused our efforts on two main threads: navigation and mapping methods, and control architectures for autonomous robots. Regarding the navigation and mapping task, we have extended the work presented by Prescott, so that it can be used with fuzzy information about the locations of landmarks in the environment. Together with this extension, we have also developed methods to compute diverting targets, needed by the robot when it gets blocked. Regarding the control architecture, we have proposed a general architecture that uses a bidding mechanism to coordinate a group of systems that control the robot. This mechanism can be used at different levels of the control architecture. In our case, we have used it to coordinate the three systems of the robot (Navigation, Pilot and Vision systems) and also to coordinate the agents that compose the Navigation system itself. Using this bidding mechanism the action actually being executed by the robot is the most valued one at each point in time, so, given that the agents bid rationally, the dynamics of the biddings would lead the robot to execute the necessary actions in order to reach a given target. The advantage of using such mechanism is that there is no need to create a hierarchy, such in the subsumption architecture, but it is dynamically changing depending on the specific situation of the robot and the characteristics of the environment. We have obtained successful results, both on simulation and on real experimentation, showing that the mapping system is capable of building a map of an unknown environment and use this information to move the robot from a starting point to a given target. The experimentation also showed that the bidding mechanism we designed for controlling the robot produces the overall behavior of executing the proper action at each moment in order to reach the target

Cooperative Object Transport in Multi-robot Systems:A Review of the State-of-the-Art

In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increase the visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectivelypublishersversionPeer reviewe

Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201