In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle" - for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept, which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillationfree motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios.

The Lane-Curvature Method (LCM) presented in this paper is a new local obstacle avoidance method for indoor mobile robots. The method combines the Curvature-Velocity Method (CVM) with a new directional method called the Lane Method. The lane method divides the environment into lanes, and then chooses the best lane to follow to optimize travel along a desired heading. A local heading is then calculated for entering and following the best lane, and CVM uses this heading to determine the optimal translational and rotational velocities, considering the heading direction, physical limitations, and environmental constraints. By combining both directional and velocity space methods, LCM yields safe collision-free motion as well as smooth motion taking the dynamics of the robot into account. Introduction A local obstacle avoidance method for indoor mobile robots in unknown or partially known environments is investigated. The method should guide a robot through a collision free space along a g...

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Over the past few years, localization based on landmarks has become increasingly popular. Virtually all existing approaches to landmark-based navigation, however, rely on the human designer to decide what constitutes appropriate landmarks. This paper presents an approach that enables mobile robots to select their landmarks by themselves. Landmarks are chosen based on their utility for localization. This is done by training neural network landmark detectors so as to minimize the a posteriori localization error that the robot is expected to make after querying its sensors. An empirical study illustrates that self-selected landmarks are superior to landmarks carefully selected by a human. The Bayesian approach is also applied to control the direction of the robot's camera, and empirical data demonstrates the appropriateness of this approach for active perception. The author is also affiliate...

The control of mobile robots acting autonomously in the real world is one of the long-term goals of the field of artificial intelligence. So far the field lacks methods bridging the gap between the sophisticated symbolic techniques to represent and reason about action and more and more reliable low-level robot control and navigation systems. In this paper we present GOLEX, an execution and monitoring system for the logic-based action language GOLOG and the complex and distributed RHINO control software which operates on RWI B21 and B14 mobile robots. GOLEX provides the following features: it maps abstract primitive actions into low-level commands of the robot control system, thus allowing the user to concentrate on the application rather than the inner workings of the robot; it monitors the execution of the primitive GOLOG actions, making it possible to detect simple execution failures and timeouts; and it includes means to deal with sensing and user input and to continue the operati...

We consider the problem of autonomous navigation in an unstructured outdoor environment. The goal is for a small outdoor robot to come into a new area, learn about and map its environment, and move to a given goal at modest speeds (1 m/s). This problem is especially difficult in outdoor, off-road environments, where tall grass,

Abstract — In this paper, we present an algorithm for generating complex dynamically-feasible maneuvers for autonomous vehicles traveling at high speeds over large distances. Our approach is based on performing anytime incremental search on a multiresolution, dynamically-feasible lattice state space. The resulting planner provides real-time performance and guarantees on and control of the suboptimality of its solution. We provide theoretical properties and experimental results from an implementation on an autonomous passenger vehicle that competed in, and won, the Urban Challenge competition. I.

To be truly useful, mobile robots need to be fairly autonomous and easy to control. This is especially true in situations where multiple robots are used, due to the increase in sensory information and the fact that the robots can interfere with one another. This paper describes a system that integrates autonomous navigation, a task executive, task planning, and an intuitive graphical user interface to control multiple, heterogeneous robots. We have demonstrated a prototype system that plans and coordinates the deployment of teams of robots. Testing has shown the effectiveness and robustness of the system, and of the coordination strategies in particular. 1

We propose a motion planning algorithm for performing policy search in the full pose and velocity space of a mobile robot. By comparison, existing techniques optimize high-level plans, but fail to optimize the low-level motion controls. We use policy search in a high dimensional control space to find plans that lead to measurably better motion planning. Our experimental results suggest that our approach leads to superior robot motion than many existing techniques.

Office delivery robots have to perform many tasks such as picking up and delivering mail or faxes, returning library books, and getting coffee. They have to determine the order in which to visit locations, plan paths to those locations, follow paths reliably, and avoid static and dynamic obstacles in the process. Reliability and efficiency are key issues in the design of such autonomous robot systems. They must deal reliably with noisy sensors and actuators and with incomplete knowledge of the environment. They must also act efficiently, in real time, to deal with dynamic situations. To achieve these objectives, we have developed a robot architecture that is composed of four layers: obstacle avoidance, navigation, path planning, and task planning. The layers are independent, communicating processes that are always active, processing sensory data and status information to update their decisions and actions. A version of our robot architecture has been in nearly daily use in our building...

Abslracl-This paper relates first experiences using a stateof-the.art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the Sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor featum B frame rate of up 1 30 fames per second. Ils capabilities in terms of basic obstacle avoidance and local path-planning am eralualed and compared to the performance of a shndard laser sEmner. I.