Sooyong Lee

On-line optical flow feedback for mobile robot localization/ navigation

Open-loop estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity of an estimate. However, these methods can sometimes lead to inaccurate or unreliable positional estimates. Using one or more optical flow sensors, a method has been developed which can accurately track position in both ideal kinematic conditions and otherwise. Using optical flow techniques and available sensors, reliable positional estimates are made. Location of the sensors has also been investigated in order to minimize errors caused by inaccurate sensor readings. Finally, the method is implemented and tested using a potential filed based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where kinematic violations (such as wheel slip) occurred.

Localization based on visibility sectors using range sensors

Presents a rapid localization method for mobile robots. Localization, i.e., absolute position measurement, is an important issue since odometer errors render it impossible for any robot to precisely follow a specified trajectory, resulting in a growing difference between the actual configuration and the calculated configuration as the robot travels. Periodic localization is required to correct these errors. We propose a localization method using range sensor data which is based on simple geometric properties of the environment. In many common situations, information regarding the environment is provided a priori for path planning. During processing, the method proposed here utilizes this information to partition the workspace into sectors using simple visibility computations, and a small identifying label is computed for each sector. The localizer analyzes range sensor readings (distances) and extracts characteristic points, which are compared with the pre-computed sector labels to localize the robot, first to a sector, and then to a particular configuration within that sector. Advantages of this two step process are that it is computationally very simple, and that it allows precise localization without any landmarks from any configuration in the environment. This localization method also provides opportunities for the global navigation procedure to analyze and select trajectories in terms of their tolerance to localization errors.

An integrated mobile robot path (re)planner and localizer for personal robots

We describe a method for navigation in a known indoor environment, such as a home or office, that requires only inexpensive range sensors. Our framework includes a high-level planner which integrates and coordinates path planning and localization modules with the aid of a module for computing regions which are expected, with high probability, to contain the robot at any given time. The localization method is based on simple geometric properties of the environment which are computed during a preprocessing stage. The roadmap-based path planner enables one to select routes, and subgoals along those routes, that will facilitate localization and other optimization criteria. In addition, our framework enables one to quickly plan new routes, dynamically, based on the current position as computed by intermediate localization operations. We present simulation and hardware experimental results that illustrate the practicality and potential of our approach.

Consecutive scanning based obstacle detection and probabilistic navigation of a mobile robot

This paper presents the obstacle detection algorithm based on the consecutive range sensor scanning scheme and the probabilistic navigation for an mobile robot. For a known environment, a mobile robot scans the environment using the range sensor which can rotate 360/spl deg/. The environment is rebuilt using nodes of two adjacent walls, and an obstacle is detected by comparing characteristic points of both the known environment and the scanned data set. It is very useful for detecting the moving obstacle. By comparing two data sets, the movement of an obstacle is extracted. Furthermore, the consecutive scanning data set provides obstacle information in unknown environment. Geometric comparison between the two consecutive data sets is used to detect the obstacles and the algorithm is presented with both simulation and experimental results. After the obstacle information is extracted from the consecutive scanning, the path is rebuilt by checking the collision probability.

Sensor based localization for multiple mobile robots using virtual link

An approach for multiple mobile robot localization using inexpensive infrared sensors and CMOS cameras is presented. A multiple robot system is treated as a virtual linked robot. Using the virtual link length and the relative heading information in conjunction with sensor scan data for each robot in the system, the entire system can effectively be localized without investing heavily in sensors. In order to facilitate collaboration and exploit geometry, a team must be able to extract salient information from the collective sensing of all its members. By collecting and fusing sensor information from multiple, distinct positions, the effective resolution of the space can be improved beyond that of the individual measurements. The proposed localization method is verified via both simulation and experiment.

The global path re-planner for a mobile manipulator

This paper is a summary of an effort to develop a powerful motion planning algorithm for the mobile manipulator. The mobile manipulator is expected to work in partially defined or unstructured environments. In our global/local approach to path planning, joint trajectories are generated for a desired Cartesian space path, designed by the global path planner. For local path planner, inverse kinematics for a redundant system is used. Obstacle avoidance and joint displacement limits for the manipulator links are considered in the motion planner. In an event of failure to obtain feasible trajectories, the task can not be accomplished. In the case of the joint constraint violation, use of Jacobian matrix element as gradient is proposed. At the point of failure, a derivation in the Cartesian space path is obtained and the re-planner gives a new path that would achieve the goal position. To calculate the deviation, a non-linear optimization problem is formulated and solved by standard sequential quadratic programming (SQP) method.

Obstacle avoidance with perturbation/correlation method

We propose a novel technique for acquiring effective information for obstacle avoidance in mobile robot navigation. Instead of simply receiving data at a single point, we actively give perturbations to the system and measure the response from the system. Correlating the input and the output, we can formulate the correlation function and useful information to guide the mobile robot such as gradient can be obtained from this function. This algorithm is applied to the obstacle avoidance in mobile robot navigation and the experimental results are provided.

Vision Based Path Planning for Mobile Robot Using Extrapolated Artificial Potential Field and Probabilistic Obstacle Avoidance

Mobile robots are increasingly being used to do tasks in unknown environment. The potential of robots to undertake such tasks lies on their ability to intelligently and efficiently locate and interact with objects in their environment. This paper describes a novel method to plan paths for mobile robots in a partially known environment observed by an overhead camera. The environment consists of dynamic obstacles and targets. A new methodology, Extrapolated Artificial Potential Field is proposed for real time robot path planning. The proposed Extrapolated Artificial Potential Field is capable of navigating robots situated among moving obstacles and target. An algorithm for probabilistic collision detection is introduced. The paper summarizes this approach, and discusses the results of path planning experiments using an Amigobot. The result shows that our method is effective.Copyright

Consecutive Scanning Scheme: Application to Localization and Dynamic Obstacle Detection for a Mobile Robot

This paper presents the mobile robot localization and obstacle detection algorithm using the consecutive range sensor scanning scheme. For known environment, a mobile robot scans the environment using which can rotate 360°. The environment is rebuild using characteristic points which means nodes of two adjacent walls, and obstacle is detected by comparing characteristic points of both original environment and scanned data set. If scanning is done densely for a certain position, inaccuracy of sensor can be overcome to some extent. It is very useful algorithm in detecting the moving obstacle. By comparing two data sets, the movement of an obstacle can be picked out. Furthermore, consecutive scanning data set provides additional localization information so that we can get the robot configuration more precisely.Copyright

Various Robotic Applications of Perturbation/Correlation Method

We propose a novel technique for acquiring effective information for obstacle avoidance in mobile robot navigation and for object detection in vision image. Instead of simply receiving data at a single point, we actively give perturbations to the system and measure the response from the system. Correlating the input and the output, we can formulate the correlation function and, useful information such as gradient can be obtained from this function. This algorithm is applied to the obstacle avoidance in mobile robot navigation and the object detection in vision image processing.Copyright