Robot Navigation In Dynamic Environments Research Articles

Sonar sensor data processing based on optical flow in robot navigation

The application of optical flow techniques on artificial image sequences in computing relative motions of obstacles is studied as a data processing algorithm for ultrasonic sensors. These sensors in robot systems are widely used in calculating the distance of obstacles. However, owing to the angular uncertainty of sensor characteristics, limitations exist on the positioning of an object's location, making it difficult to obtain accurate velocity information. Collision avoidance in dynamic environments requires information on relative motion, such as distance and velocity. In this paper, a technique for image processing on a range of ultrasonic sensor data is suggested in order to obtain the relative motion of front objects. The proposed technique is conducted using a two-wheel differential drive robot for problem cases related to indoor navigation. Digital filtering is also conducted to smooth the fluctuations, after which relative velocity is calculated using the optical flow technique. Experimental results demonstrate the possible application of the proposed algorithm for robot navigation in dynamic environments. This technique will increase the intelligence of robot systems, allowing for effective sensing in various environmental conditions.

Reinforcement based mobile robot navigation in dynamic environment

In this paper, a new approach is developed for solving the problem of mobile robot path planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms have been used widely for solving real world problems, especially in robotics since it has been proved to give reliable and efficient solutions due to its simple and well developed theory. However, most of the researchers who tried to use Q-learning for solving the mobile robot navigation problem dealt with static environments; they avoided using it for dynamic environments because it is a more complex problem that has infinite number of states. This great number of states makes the training for the intelligent agent very difficult. In this paper, the Q-learning algorithm was applied for solving the mobile robot navigation in dynamic environment problem by limiting the number of states based on a new definition for the states space. This has the effect of reducing the size of the Q-table and hence, increasing the speed of the navigation algorithm. The conducted experimental simulation scenarios indicate the strength of the new proposed approach for mobile robot navigation in dynamic environment. The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.

Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction

This paper considers the problem of autonomous robot navigation in dynamic and congested environments. The predictive navigation paradigm is proposed where probabilistic planning is integrated with obstacle avoidance along with future motion prediction of humans and/or other obstacles. Predictive navigation is performed in a global manner with the use of a hierarchical Partially Observable Markov Decision Process (POMDP) that can be solved on-line at each time step and provides the actual actions the robot performs. Obstacle avoidance is performed within the predictive navigation model with a novel approach by deciding paths to the goal position that are not obstructed by other moving objects movement with the use of future motion prediction and by enabling the robot to increase or decrease its speed of movement or by performing detours. The robot is able to decide which obstacle avoidance behavior is optimal in each case within the unified navigation model employed.

Real-time hierarchical POMDPs for autonomous robot navigation

This paper proposes a new hierarchical formulation of POMDPs for autonomous robot navigation that can be solved in real-time, and is memory efficient. It will be referred to in this paper as the Robot Navigation–Hierarchical POMDP (RN-HPOMDP). The RN-HPOMDP is utilized as a unified framework for autonomous robot navigation in dynamic environments. As such, it is used for localization, planning and local obstacle avoidance. Hence, the RN-HPOMDP decides at each time step the actions the robot should execute, without the intervention of any other external module for obstacle avoidance or localization. Our approach employs state space and action space hierarchy, and can effectively model large environments at a fine resolution. Finally, the notion of the reference POMDP is introduced. The latter holds all the information regarding motion and sensor uncertainty, which makes the proposed hierarchical structure memory efficient and enables fast learning. The RN-HPOMDP has been experimentally validated in real dynamic environments.

An Enhanced Classifier System for Autonomous Robot Navigation in Dynamic Environments

ABSTRACTFor solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.

Real-Time Motion Control for Safe Navigation

The paper addresses autonomous mobile robot navigation in dynamic environments. Assuming a preplanned trajectory for the robot, the focus is on real time speed control to modify the speed component of the plan in the presence of unexpected moving obstacles encountered during operation. The problem, approached in a comprehensive way, involves the interpretation of range measurements from a laser scanner to discriminate moving objects, the estimation of their speed and orientation, the issue of speed recommendations for the robot by a fuzzy controller, and the fusion of those with the preplanned speed profile. The paper illustrates the general procedure with an implementation on the RAM-2 mobile robot.

A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation

Payton and Rosenblatt (1990) have proposed a command fusion method for combining outputs of multiple behaviors in a mobile robot navigation system such that information loss due to command fusion can be reduced. Using linguistic fuzzy rules to explicitly capture heuristics implicit in the Payton-Rosenblatt approach, we have extended their approach to a fuzzy logic architecture for mobile robot navigation in dynamic environments, which is simpler and easier to understand and modify. We have also developed and empirically tested a new defuzzification technique for alleviating difficulties in applying existing defuzzification methods to mobile robot navigation control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>