Tetsushi Ikeda

Social force model with explicit collision prediction

We introduce a new specification of the social force model in which pedestrians explicitly predict the place and time of the next collision in order to avoid it. This and other specifications of the social force model are calibrated, using genetic algorithms, on a set of pedestrian trajectories, obtained tracking with laser range finders the movement of pedestrians in controlled experiments, and their performance is compared. The results show that the proposed method has a better performance in describing the trajectory set.

Person Tracking in Large Public Spaces Using 3-D Range Sensors

A method for tracking the position, orientation, and height of persons in large public environments is presented. Such a piece of information is known to be useful both for understanding their actions, as well as for applications such as human-robot interaction. We use multiple 3-D range sensors, which are mounted above human height to have less occlusion between persons. A computationally simple-tracking method is proposed that works on single sensor data and combines multiple sensors so that large areas can be covered with a minimum number of sensors. Moreover, it can work with different sensor types and is robust to the imperfect sensor measurements; therefore, it is possible to combine currently available 3-D range sensor solutions to achieve tracking in wide public spaces. The method was implemented in a shopping center environment, and it was shown that good tracking performance can be achieved.

Human tracking using floor sensors based on the Markov chain Monte Carlo method

The aim of this paper is to develop a human tracking system that is resistant to environmental changes and covers wide area. Simply structured floor sensors are low-cost and can track people in a wide area. However, the sensor reading is discrete and missing; therefore, footsteps do not represent the precise location of a person. A Markov chain Monte Carlo method (MCMC) is a promising tracking algorithm for these kinds of signals. We applied two prediction models to the MCMC: a linear Gaussian model and a highly nonlinear bipedal model. The Gaussian model was efficient in terms of computational cost while the bipedal model discriminated people more accurate than the Gaussian model. The Gaussian model can be used to track a number of people, and the bipedal model can be used in situations where more accurate tracking is required.

A Microscopic “Social Norm” Model to Obtain Realistic Macroscopic Velocity and Density Pedestrian Distributions

We propose a way to introduce in microscopic pedestrian models a “social norm” in collision avoiding and overtaking, i.e. the tendency, shared by pedestrians belonging to the same culture, to avoid collisions and perform overtaking in a preferred direction. The “social norm” is implemented, regardless of the specific collision avoiding model, as a rotation in the perceived velocity vector of the opponent at the moment of computation of the collision avoiding strategy, and justified as an expectation that the opponent will follow the same “social norm” (for example a tendency to avoid on the left and overtake on the right, as proposed in this work for Japanese pedestrians). By comparing with real world data, we show that the introduction of this norm allows for a better reproduction of macroscopic pedestrian density and velocity patterns.

A waypoint-based framework in brain-controlled smart home environments: Brain interfaces, domotics, and robotics integration

The noninvasive brain-machine interface (BMI) is anticipated to be an effective tool of communication not only in laboratory settings but also in our daily livings. The direct communication channel created by BMI can assist aging societies and the handicapped and improve human welfare. In this paper we propose and experiment a BMI framework that combines BMI with a robotic house and autonomous robotic wheelchair. Autonomous navigation is achieved by placing waypoints within the house and, from the user side, the user performs BMI to give commands to the house and wheelchair. The waypoint framework can offer essential services to the user with an effectively improved information-transfer rate and is an excellent examples of the fusion of data measured by sensors in the house, which can offer insight into further studies.

Deciphering the crowd: modeling and identification of pedestrian group motion.

Associating attributes to pedestrians in a crowd is relevant for various areas like surveillance, customer profiling and service providing. The attributes of interest greatly depend on the application domain and might involve such social relations as friends or family as well as the hierarchy of the group including the leader or subordinates. Nevertheless, the complex social setting inherently complicates this task. We attack this problem by exploiting the small group structures in the crowd. The relations among individuals and their peers within a social group are reliable indicators of social attributes. To that end, this paper identifies social groups based on explicit motion models integrated through a hypothesis testing scheme. We develop two models relating positional and directional relations. A pair of pedestrians is identified as belonging to the same group or not by utilizing the two models in parallel, which defines a compound hypothesis testing scheme. By testing the proposed approach on three datasets with different environmental properties and group characteristics, it is demonstrated that we achieve an identification accuracy of 87% to 99%. The contribution of this study lies in its definition of positional and directional relation models, its description of compound evaluations, and the resolution of ambiguities with our proposed uncertainty measure based on the local and global indicators of group relation.

Including human factors for planning comfortable paths

This work proposes a Human-Comfortable Path Planner (HCoPP) system for autonomous passenger vehicles. The aim is to create a path planner that improves the feeling of comfort of the passenger, this topic is different from collision free planning and it has not received much attention. For this purpose, in addition to the shortest distance constraint conventionally used in path planning, constraints related to relevant environmental features are introduced. For straight segments, the constraint is based on the lane-circulation pattern preferred by humans. In curved segments and intersections, the constraint takes into account the visibility. A multi-layered cost map is proposed to integrate these additional constraints. To compute the human-comfortable path, a graph search algorithm was implemented. The evaluation of the proposed approach was conducted by having 30 participants riding an autonomous robotic wheelchair. The paths computed by the proposed path planner were compared towards a state of the art shortest-distance path planner implemented in the navigation stack of ROS. Experimental results show that the paths computed by the proposed approach are perceived as more comfortable.

Communicating robotic navigational intentions

This paper presents a study on intention communication in a navigational context using a robotic wheelchair. The robotic wheelchair uses light projection to communicate its motion intentions. The novelty of the work is threefold: the communication of robot intentions to the passenger, the consideration of passenger and robot as a group (“in-group”) [1] who share motion intentions and the communication of the in-group intentions to other pedestrians (the “out-group”). A comparison in an autonomous navigation task where the robotic wheelchair autonomously navigates the environment with and without intention communication was performed showing that passengers and walking people found intention communication intuitive and helpful for passing by actions. Evaluation results significantly show human participant preference for having navigational intention communication for the wheelchair passenger and the person passing by it. Quantitative results show the motion of the person passing by the wheelchair with intention communication was significantly smoother compared to without intention communication.

Framework of distributed audition

We propose distributed audition for natural man-machine interface, for robots that act in the environment, and for continuous personal identification. The distributed audition system, consisting of a network of microphones and speakers, monitors the environment, maintains the environment models, and provide information to agents in the environment. The distributed audition system can calibrate locations and parameters in self-organizing manner by producing sounds and observing them. Concepts and fundamental problems of distributed audition are discussed. This work also provides a prototype system and an experimental result of auto calibration.

Cooperative customer navigation between robots outside and inside a retail shop—an implementation on the ubiquitous market platform

Applying the technologies of a network robot system, recommendation methods used in e-commerce are incorporated in a retail shop in the real world. We constructed a platform for ubiquitous networked robots that focuses on a shop environment where communication robots perform customer navigation. The platform observes customers’ purchasing behavior by networked sensors, including a laser range finder-based human position tracking system, and then controls visible-type communication robots in the environment to perform customer navigation. Two types of navigation scenarios are implemented and investigated in experiments using 80 participants. The results indicate that the participants in the cooperative navigation scenario, who interacted with communication robots located both outside and inside the shop, felt friendliness toward the robots and found it easy to understand what the robots said.