This paper describes a mobile robot navigation method in dynamic environments. The method uses a real-time omnidirectional stereo, which can obtain panoramic range information of 360 degrees. From this panoramic range information, the robot estimates its ego-motion by comparing the current and the previous observations in order to integrate observations obtained at different positions. The uncertainty in the estimation is also calculated. Next, the robot recognizes and tracks moving obstacles. Finally, the robot plans a collision free path by a heuristic planner in space-time considering the velocity uncertainty of observed obstacles. Experimental results show the effectiveness of our method.

/pdf/mobile-robot-navigation-in-dynamic-environments-using-xzzm4o5qki.pdf

Mobile robot navigation in dynamic environments using omnidirectional stereo

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.

Robot colonies

Describes a method of modeling the motion uncertainty of moving obstacles and its application to mobile robot motion planning. The method explicitly considers three sources of motion uncertainty: path ambiguity, velocity uncertainty, and observation uncertainty. The model is represented by a probabilistic distribution over possible position an a path of a moving obstacle. Using this model, the best robot motion is selected which minimizes the expected time of reaching the destination. By considering not the range but the distribution of the uncertainty, more efficient behaviors of the robot are realized.

/pdf/modeling-motion-uncertainty-of-moving-obstacles-for-robot-mehtkf457p.pdf

Modeling motion uncertainty of moving obstacles for robot motion planning

This paper handles the problem of collision avoidance in a multi-robot environment. To solve this problem, the motion processes of the mobile robots are modelled in space-time. Since the robots are autonomous and communication is non-deterministic, there is temporal uncertainty in addition to spatial uncertainty. The paper presents a method to model both uncertainty components in a homogeneous way. It is shown, that it is not sufficient to guarantee a spatial security distance between the robots. Distances in space-time and space-time vectors must be considered. The main result of this paper is a straightforward and efficient solution to the problem of collision avoidance between up to three mobile robots by applying a space-time displacement vector. The solution is based on space-time, which is a helpful view onto our world in relativity theory and quantum physics. Space-time methods are also very valuable in Robotics, especially for problems in dynamic environments and for motion coordination of mobile robots. Practical experiments with up to two robots, and simulations of up to three robots have been performed and are reported.

Collision avoidance by using space-time representations of motion processes

This paper describes a method of modeling the motion uncertainty of moving obstacles and its application to mobile robot motion planning. The method explicitly considers three sources of uncertainty: path ambiguity, velocity uncertainty, and observation uncertainty. The uncertainty model represents the position of an obstacle at a certain time point by a probabilistic distribution over possible positions on each possible path of the moving obstacle. Using this model, the best robot motion is selected in a decisiontheoretic way. By considering not the range but the distribution of the uncertainty, more efficient behaviors of the robot are realized.

/pdf/probabilistic-uncertainty-modeling-of-obstacle-motion-for-1cps5ns0af.pdf

Probabilistic Uncertainty Modeling of Obstacle Motion for Robot Motion Planning

We address the problem of planning a safe path for a mobile robot, in the presence of moving obstacles, based on local information. We consider constraints on the speed and the acceleration of the robot. Moreover, we take into account the uncertainty in estimating the velocity of each obstacle. The concept of safety is used to design a planning strategy. A path which maximizes the product of safety based on local information, attraction towards the goal, and time-to-collision, is chosen. The safety function depends on the acceleration bounds. The attraction towards goal depends on the distance from the goal. Time-to-collision depends on how far the robot is from a moving obstacle. Simulation results of this approach are presented. >

Local path planning in dynamic environments with uncertainty

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Local path planning in dynamic environments with uncertainty