In recent papers we (1996, 1997) have proposed a new path planning approach for mobile robots: statistical motion planning with respect to typical obstacle behavior in order to improve pre-planning in dynamic environments. In this paper, we present our experimental system: in a real environment, cameras observe the workspace in order to detect obstacle motions and to derive statistical data. We have developed new techniques based on stochastic trajectories to model obstacle behavior. Collision probabilities are calculated for polygonal objects moving on piecewise linear trajectories. The statistical data can be applied directly, thus the entire chain from raw sensor data to a stochastic assessment of robot trajectories is closed. Finally, some new work regarding different applications of statistical motion planning is outlined, including road-map approaches for pre-planning, expected time to reach the goal, and reactive behaviors.

As recent research is directed towards flexible and autonomous robot behaviour, a priori knowledge is increasingly replaced by data obtained automatically by external sensors of the robot. Much effort already has been made regarding the integration of different types of sensor data into reliable world representations. Yet, the aspect of guiding robot perception in a systematic manner often has been neglected. Closing this gap, we present a framework addressing the efficient exploration of an unknown three-dimensional environment. A natural approach to this problem is a repeated "planning-sensing-updating"-cycle. In this paper special attention is paid on planning: Based on the current, incomplete world model and on constraints respecting the robot's motions, rating functions in configuration space are defined. When planning sensing actions, configurations with high ratings are chosen. Thus, a fast and efficient exploration of the workspace is guaranteed. Our approach works with miscellaneous robot architectures, including mobile systems as well as manipulators with many degrees of freedom and redundancies. The inverse kinematic solution of the robot has not to be known. The sensors to be used have to provide range measurements, like laser scanners, stereo or sonar systems.

To plan collision-free trajectories for mobile robots among moving obstacles most approaches presume the obstacles' trajectories to be known. This is contradictory to practice where motions generally are known-at best-as average behaviours. Thus, we proposed to use statistical information for path planning. This paper focuses on the derivation of formulae to estimate collision probabilities. By generalizing elementary collision patterns we develop a method to calculate a collision probability field. For any given goal position, it estimates the collision probabilities for paths starting at arbitrary points in the workspace. This provides a powerful tool for path planning in dynamic environments. Using an algorithm similar to path planning based on potential fields, we can calculate paths with (approximately) minimum collision probability.

This paper proposes a new path planning approach based on statistical information, like the occupancy probability of partly or completely dynamic obstacles and/or the statistical flow of dynamic obstacles. With this new approach it is possible to integrate the expected behavior of moving obstacles into the preplanning of paths without knowing the individual movement of these dynamic obstacles. As the preplanned paths coincide with the average movement pattern in the environment, the runtime planning is simplified. The authors propose an appropriate world model to represent the necessary statistical data, which can be obtained automatically. In addition, a path planning approach, suited for point-shaped and arbitrary-shaped vehicles, is sketched. These paths integrate geometrical detours to fit the planned movements into the statistical movement pattern of the dynamic obstacles.>

After a short overview of a monitoring and navigation system for mobile vehicles (called MONAMOVE) consisting of multiple carriers of different size and an environment monitoring system, the navigation principle using one global navigator for gross motion planning and several local ones for fine motion planning is described. The coordination of multiple vehicles with their individual trajectories computed by the local navigators is managed by the global navigator using automatically computed crossings lying on a regular grid. This fine motion planning approach fulfills constraints like right orientation and continuous curvature along the paths; it bases on the computation of right oriented points which lie inside a driving channel (DC). These points are connected to the best right oriented line sequence without being a stupid following of the right border of the DC. Various extensions of this work are proposed.

A hierarchical navigation system which delivers paths with continuous curvature for rectangularly-modeled vehicles is proposed. After a brief overview of the transport system consisting of omni wheel drive carriers, and on-carrier and off-carrier sensor components, the navigation module of this system is described. The multilevel navigation is based on a new polygonal partitioning of the Cartesian free-space, which allows a fast local modification of the partitions if obstacles are added or deleted. An A*-algorithm whose weight function is automatically adjusted by information on lower system levels delivers a coarse route through the environment (driving channel). Subsequently, a sequence of straight lines is generated, which are oriented along the right border of this channel. On the next level of path planning, curves are fitted into the corners of the line segment sequences to generate paths with continuous curvature. The generated, right-oriented paths are collision-free for rectangular modeled vehicles.>

The integration of a three-dimensional sensor into a robot workcell is described. The sensor is based on the well-known coded light approach (CLA). The CCD camera of the CLA is mounted at the robot's hand and one or more projectors are fixed outside the robot's work space. The principle of the CLA and the calibration of th robot-sensor-system are explained. An application in which the tool of the robot follows an unknown path on an arbitrary surface is presented. For evaluation reasons the deviation from the surface coordinates are simultaneously measured by a gage, allowing a quantitative error analysis.>