We present, for the first time, an analytical relation between the navigation errors during Zero-Velocity-Update (ZUPT) aided pedestrian inertial navigation and Inertial Measurement Unit (IMU) performances. Angle Random Walk of gyroscopes and Velocity Random Walk of accelerometers in IMU, velocity measurement uncertainty during the stance phase, and the sampling frequency of IMU are studied and their effects on the overall navigation accuracy are analyzed. Numerical simulation of the ZUPT-aided navigation algorithm is conducted based on a generated pedestrian trajectory to verify the analytical results with the discrepancy less than 30%, showing fidelity of the results. In this study, a foot motion model was developed according to the ambulatory gait analysis as a basis of the numerical simulation. This study may serve as a benchmark for analysis of errors in pedestrian inertial navigation.

Obstacle detection is a critical task in mobile robot navigation. To allow for rapid reactions to changes in the environment, it is required to complete its operation in short time intervals. That can be difficult if computing and/or sensing robot resources are limited. Therefore, it is important to design precisely what sensorial information would be needed to guarantee detection, and how it would be gathered to guarantee throughput. In this paper we present analysis and experiments related to the design of an obstacle detection system for a mobile robot with limited sensing capabilities. Objectives are to preserve safety while maintaining good motion performance. The design is made in two steps. Firstly, to find what is the minimum information needed to assure safety as a function of the sensors at hand, the robot capabilities and the intended motion control policy. Second, to manage the available sensors in order to provide the required information when needed. For the example studied, a justified solution has been defined which guarantees detection, robot safety, and good motion performance in real time. The solution has been tested with experiments that illustrate the effectivity achieved.

In experimental robotics it is common to complement the motion planning algorithm with a local obstacle avoidance module. We are concerned here with the specifications of a range sensor specifically design for the task. Such sensor has special requirements: it has to guarantee detection and response, and it has to guarantee throughput, providing information ail a rate in proportion to robot velocity. An analysis is presented which allows the selection of the sensor requirements for collision avoidance tasks in mobile robots. The design takes into account robot dynamics, it is compatible with fast motion-only the indispensable environment zones are explored-and avoids unnecessary velocity reductions. Experiments show the possibilities and limitations of the approach.

This work addresses the problem of finding the shortest path for a vehicle (say, a mobile robot or a car) moving in a limited workspace. The proposed approach makes use of a tool dubbed the reflective unfolding operator which has a clear geometric interpretation and provides an interesting means for solving other trajectory design problems. The authors' previously reported result (1997) which relates to the special case of a car maneuvering within a disc-shaped area, is extended here to the general case of an arbitrarily shaped area. The approach is illustrated by computer simulations.

The effect of robot dynamics on autonomous navigation is a key issue in sensor-based motion planning. In most of existing works, the solution is attempted by separating the planning and control into two sequential stages; as a minus, this may, for example, adversely effect the algorithm convergence. The strategy proposed in this paper solves the problem by combining motion planning and control within a single-stage procedure. The procedure exhibits good dynamic behavior, while providing safety (collision avoidance) and fast response. Results of testing the approach on a commercial Nomad-200 mobile robot are presented. Also discussed is the effect of model parameters on motion performance.

In this paper we pose the following two questions: Given two points, start and target, within a relatively small closed planar area W/spl sub/R/sup 2/, each with a prescribed direction of motion (orientation) in it, and assuming a possibility of reversals of motion: (i) What is the shortest path of bounded curvature that connects the points and lies completely in W? (ii) What is the minimum number of motion reversals (path cusps) one needs to arrive at the target point with the prescribed orientation? Such questions appear in various applications with nonholonomic motion constraints, as for example in motion planning for driverless cars. The proposed approach solves both problems. It makes use of a tool dubbed the reflective unfolding operator which has a clear geometric interpretation and provides an interesting means for solving other trajectory design problems.

We pose the following questions: Given two points within a closed area W/spl sub/R/sup 2/, each with a prescribed direction of motion in it, (i) what is the shortest path of bounded curvature that joins them and lies completely in W? (ii) what is the minimum number of cusps one needs to design a path in W? These kind of questions appear in various applications, such as robot motion planning. The proposed approach makes use of a tool dubbed the reflective unfolding operator which has a clear geometric interpretation and provides an interesting means for solving other trajectory design problems. In this text, the approach is applied to the following problem: for a car moving with bounded curvature and possible reversals, given the starting and target directions of motion at the center of a disc D of some radius R, design the shortest possible path fully lying in D. The path produced by the algorithm turns out to also be of the lowest complexity (the minimum number of cusps).

Most of today's approaches to sensor-based motion planning focus on kinematic and geometric issues and ignore the system dynamics. Those few that address dynamics do so in a two-stage fashion by considering one issue at the time. This work attempts to incorporate control of body dynamics into the sensor-based motion planning process. A point mobile robot is assumed to operate in a planar environment with unknown arbitrary stationary obstacles. Based on its current velocity and sensory data about the surrounding obstacles, the robot plans its motion to locally maximize the turning angle toward the current intermediate target. An optimal braking procedure takes care of sudden potential collisions by guaranteeing a safe emergency stopping path. Given the constraints on robot's dynamics, sensing, and control means, conditions are formulated for generating trajectories that guarantee convergence and the robot's safety at all times. The approach calls for continuous computation and is fast enough for real-time implementation. Simulated examples demonstrate its performance.

Given two points in the plane, each with the prescribed direction of motion, the question being asked is to find the shortest smooth path of bounded curvature that joins them. The classical result by Dubins (1957) that is commonly used gives a sufficient set of paths which is guaranteed to contain the shortest path; the latter is then found by explicitly calculating every path in the set. In this paper we show that in the case when the distance between the two points is above some minimum, the solution sought can be found via a simple classification scheme. Besides computational savings (essential, for example, in real-time motion planning), this result sheds light on the nature of factors affecting the length of paths in the Dubins's problem.

The existing approaches to sensor-based motion planning tend to deal solely with kinematic and geometric issues, and ignore the system dynamics. This work attempts to incorporate body dynamics into the paradigm of sensor-based motion planning. The authors consider the case of a mass point robot operating in a planar environment with unknown arbitrary stationary obstacles. Given the constraints on the robot's dynamics, sensing, and control means, conditions are formulated for generating trajectories which guarantee convergence and the robot's safety at all times. The approach calls for continuous computation and is fast enough for real time implementation. The robot plans its motion based on its velocity, control means, and sensing information about the surrounding obstacles, and such that in case of a sudden potential collision it can always resort to a safe emergency stopping path. Simulated examples demonstrate the algorithm's performance.

The existing approaches to sensor-based motion planning tend to deal solely with kinematic and geometric issues, and ignore the system dynamics. This work attempts to incorporate body dynamics into the paradigm of sensor-based motion planning. The authors consider the case of a point mass mobile robot operating in a planar environment with unknown stationary obstacles of arbitrary shape. Given the constraints on the robot's dynamics, sensing, and control means, conditions are formulated for generating collision-free trajectories with guaranteed convergence. The approach calls for continuous computation and is fast enough for real time implementation. Based on its velocity and sensing data, the robot continuously plans its motion based on the canonical solution which presents the time-optimal path within the robot's current sensing range. For a special case of a sudden potential collision an option of a safe emergency stopping path is always maintained. Simulated examples demonstrate the algorithm's performance.