Daniel O. Sales

Mobile Robots Navigation in Indoor Environments Using Kinect Sensor

This paper presents the development of a perception system for indoor environments to allow autonomous navigation for surveillance mobile robots. The system is composed by two parts. The first part is a reactive navigation system in which a mobile robot moves avoiding obstacles in environment, using the distance sensor Kinect. The second part of this system uses a artificial neural network (ANN) to recognize different configurations of the environment, for example, path ahead, left path, right path and intersections. The ANN is trained using data captured by the Kinect sensor in indoor environments. This way, the robot becomes able to perform a topological navigation combining internal reactive behavior to avoid obstacles and the ANN to locate the robot in the environment, in a deliberative behavior. The topological map is represented by a graph which represents the configuration of the environment, where the hallways (path ahead) are the edges and locations (left path and intersection, for example) are the vertices. The system also works in the dark, which is a great advantage for surveillance systems. The experiments were performed with a Pioneer P3-AT robot equipped with a Kinect sensor in order to validate and evaluate this approach. The proposed method demonstrated to be a promising approach to autonomous mobile robots navigation.

CaRINA Intelligent Robotic Car: Architectural design and applications

Abstract This paper presents the development of two outdoor intelligent vehicles platforms named CaRINA I and CaRINA II, their system architecture, simulation tools, and control modules. It also describes the development of the intelligent control system modules allowing the mobile robots and vehicles to navigate autonomously in controlled urban environments. Research work has been carried out on tele-operation, driver assistance systems, and autonomous navigation using the vehicles as platforms to experiments and validation. Our robotic platforms include mechanical adaptations and the development of an embedded software architecture. This paper addresses the design, sensing, decision making, and acting infrastructure and several experimental tests that have been carried out to evaluate both platforms and proposed algorithms. The main contributions of this work is the proposed architecture, that is modular and flexible, allowing it to be instantiated into different robotic platforms and applications. The communication and security aspects are also investigated.

3D Vision-Based Autonomous Navigation System Using ANN and Kinect Sensor

In this paper, we present an autonomous navigation system based on a finite state machine (FSM) learned by an artificial neural network (ANN) in an indoor navigation task. This system uses a kinect as the only sensor. In the first step, the ANN is trained to recognize the different specific environment configurations, identifying the different environment situations (states) based on the kinect detections. Then, a specific sequence of states and actions is generated for any route defined by the user, configuring a path in a topological like map. So, the robot becomes able to autonomously navigate through this environment, reaching the destination after going through a sequence of specific environment places, each place being identified by its local properties, as for example, straight path, path turning to left, path turning to right, bifurcations and path intersections. The experiments were performed with a Pioneer P3-AT robot equipped with a kinect sensor in order to validate and evaluate this approach. The proposed method demonstrated to be a promising approach to autonomous mobile robots navigation.

Adaptive finite state machine based visual autonomous navigation system

In this paper we present an original approach applied to autonomous mobile robots navigation integrating localization and navigation using a topological map based on the proposed AFSM (adaptive finite state machine) technique. In this approach, the environment is mapped as a graph, and each possible path is represented by a sequence of states controlled by a FSM-finite state machine. An ANN (artificial neural network) is trained to recognize patterns on input data, where each pattern is associated to specific environment features or properties, consequently representing the present context/state of the FSM. When a new input pattern is recognized by the ANN (changing the current context), this allows the FSM to change to the next state and its associated action/behavior. The input features are related to specific local properties of the environment (obtained from sensors data), as for example, straight path, right and left turns, and intersections. This way, the FSM is integrated to a previously trained ANN, which acts as a key component recognizing and indicating the present state and the state changes, allowing the AFSM to select the current/correct action (local reactive behaviors) for each situation. The AFSM allows the mobile robot to autonomously follow a sequence of states/behaviors in order to reach a destination, first choosing an adequate local reactive behavior for each current state, and second detecting the changes in the current context/state, following a sequence of states/actions that codes the topological (global) path into the FSM (sequence of states/actions). The ANN is also a very important component of this system, since it can be trained/adapted to recognize a complex set of situations and state changes. In order to demonstrate the robustness of the proposed approach to different situations and sensors configurations, we evaluated the proposed approach for both indoor and outdoor environments, using a Pioneer P3-AT robot equipped with Kinect sensor for indoor environments, and an automated vehicle equipped with a standard RGB camera for urban roads environments. The proposed method was tested in different situations with success and demonstrated to be a promising approach to autonomous mobile robots control and navigation.

Template-based autonomous navigation in urban environments

Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper focus in the road identification problem, which is an important capability to autonomous vehicle drive. Our approach is based on image processing, template matching classification, and finite state machines processing. The proposed system allows to train an image segmentation algorithm in order to identify navigable and non-navigable regions (inside/outside roads), generating as output the steering control for an Electric Autonomous Vehicle, that should stay following the road. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.

Template-based autonomous navigation and obstacle avoidance in urban environments

Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper presents a vehicle control system capable of learning behaviors based on examples from human driver and analyzing different levels of memory of the templates, which are an important capability to autonomous vehicle drive. Our approach is based on image processing, template matching classification, finite state machine, and template memory. The proposed system allows training an image segmentation algorithm and a neural network to work with levels of memory of the templates in order to identify navigable and non-navigable regions. As an output, it generates the steering control and speed for the Intelligent Robotic Car for Autonomous Navigation (CaRINA). Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.

Vision-Based Autonomous Navigation System Using ANN and FSM Control

Autonomous mobile robot navigation is a very relevant problem in robotics research. This paper proposes a vision-based autonomous navigation system using artificial neural networks (ANN) and finite state machines (FSM). In the first step, ANNs are used to process the image frames taken from the robot´s camera, classifying the space, resulting in navigable or non-navigable areas (image road segmentation). Then, the ANN output is processed and used by a FSM, which identifies the robot´s current state, and define which action the robot should take according to the processed image frame. Different experiments were performed in order to validate and evaluate this approach, using a small mobile robot with integrated camera, in a structured indoor environment. The integration of ANN vision-based algorithms and robot´s action control based on a FSM, as proposed in this paper, demonstrated to be a promising approach to autonomous mobile robot navigation.

Multi-agent Autonomous Patrolling System Using ANN and FSM Control

The multi-agent patrolling problem has recently received growing attention from the community due to the wide range of potential applications. This work presents an autonomous patrolling system composed by 4 intelligent robots that can freely move through an indoor environment and detect intruders. The robots use a localization/navigation system composed of an artificial neural network (ANN) trained to detect key features of the environment. These features are used to identify context changes, being used as input of a finite state machine (FSM), allowing a topological map localization and navigation of the robot in the environment. When an intruder is detected, a broadcast message with its position is sent, making all other robots execute a multi-agent version of a coordinated A* algorithm in order to determine the best path to reach that position and to surround the target. Then, the robots autonomously navigate through this defined path until reach the goal. Experiments were performed in the player/stage environment in order to evaluate the multi-agent system. The localization/navigation system with intruder detection was evaluated in the real world with a Pioneer P3-AT mobile robot.

A hybrid GA-ANN approach for autonomous robots topological navigation

This paper proposes a hybrid approach using genetic algorithm and artificial neural networks for autonomous path planning and motion control for mobile robots. A topological navigation approach is adopted, using the environment mapped as a graph. A genetic algorithm is used to generate and evolve a set of feasible actions, aiming to lead the robot to the goal considering the shortest path. Each action is a different reactive behavior designed for a specific environment feature such as corridors, turns or intersections. Then, an artificial neural network is trained to recognize the different environment features, and the next behavior is activated every time the ANN detects a transition. Experiments were performed in Player/Stage robotics simulator and obtained results showed this approach as a promising way to plan and execute a path.

Swarm intelligence and the quest to solve a garbage and recycling collection problem

This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem.