Yoichi Morales

How do people walk side-by-side?: using a computational model of human behavior for a social robot

This paper presents a computational model for side-by-side walking for human-robot interaction (HRI). In this work we address the importance of future motion utility (motion anticipation) of the two walking partners. Previous studies only considered a robot moving alongside a person without collisions with simple velocity-based predictions. In contrast, our proposed model includes two major considerations. First, it considers the current goal, modeling side-by-side walking, as a process of moving towards a goal while maintaining a relative position with the partner. Second, it takes the partners utility into consideration; it models side-by-side walking as a phenomenon where two agents maximize mutual utilities rather than only considering a single agent utility. The model is constructed and validated with a set of trajectories from pairs of people recorded in side-by-side walking. Finally, our proposed model was tested in an autonomous robot walking side-by-side with participants and demonstrated to be effective.

Vehicle localization in outdoor woodland environments with sensor fault detection

This paper describes a 2D localization method for a differential drive mobile vehicle on real forested paths. The mobile vehicle is equipped with two rotary encoders, Crossbows NAV420CA inertial measurement unit (IMU) and a NAVCOM SF-2050M GPS receiver (used in StarFire-DGPS dual mode). Loosely-coupled multisensor fusion and sensor fault detection issues are discussed as well. An extended Kalman filter (EKF) is used for sensor fusion estimation where a GPS noise pre-filter is used to avoid introducing biased GPS data (affected by multi-path). Normalized innovation squared (NIS) tests are performed when a GPS measurement is incorporated to reject GPS data outliers and keep the consistency of the filter. Finally, experimental results show the performance of the localization system compared to a previously measured ground truth.

Human-comfortable navigation for an autonomous robotic wheelchair

Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

DGPS, RTK-GPS and StarFire DGPS Performance Under Tree Shading Environments

In this paper we present our experimental measurement results using GPS in outdoor tall obstacle free and under tree shading environments. We tested and compared three different GPS receivers using different principles such as Differential GPS, Real Time Kinematic GPS and an innovative system called StarFire DGPS. Coordinate conversion issue is discussed and stationary and moving tests were performed. In the stationary tests, off-line post-processed data was used to compare the difference between receivers measuring the same point at different times. Finally three GPS receivers were mounted on a mobile platform to perform moving experiments simultaneously at open field and under tree foliage where experimental measurements show the performance of each GPS receiver according to its measurement principle in harsh conditions.

1Km autonomous robot navigation on outdoor pedestrian paths “running the Tsukuba challenge 2007”

This paper presents and describes the approach for achieving long distance autonomous navigation with a mobile robot on outdoor cluttered pedestrian paths. The task was to finish an event launched by the City of Tsukuba in Japan, called ldquoReal World Robot Challengerdquo, of navigating 1km autonomously in a real environment with real pedestrians and bicycles. The hardware, software and strategy for navigating in cluttered environments is explained. Moreover, the complementary functionality of the overall system where map-based and sensor-based navigation seamlessly change, is presented. The robustness of the system is validated with experimental results.

GPS moving performance on open sky and forested paths

In this paper we present a systematic study of the performance of seven different configurations of GPS on a moving vehicle using three different GPS receivers. The seven different configurations are 1) single frequency code differential DGPS, 2) double frequency code differential DGPS, 3) RTK-GPS receiving RTCM correction from a mobile phone, 4) RTK-GPS receiving RTCM correction information via wireless module from base antenna, 5) StarFire WADGPS, 6) StarFire-DGPS dual mode and 7) StarFire-RTK GPS dual mode. As GPS is mostly used in loosely coupled configurations where receiver output is fused with other sensors, the contribution of this paper is the statistical comparison in two dimensions of different GPS configurations using as performance index availability, precision (using standard deviation parameters of each measurement) and reliability. Two types of study environments were tested, open sky and under tree shading. Time for re-acquiring fix solution for RTK-GPS configurations and re-acquiring dual frequency solution by dual frequency configurations are discussed as well. Finally, experimental measurement results show how under tree shading, biased position data with small covariance can be rejected thresholding measurements with small HDOP values and large number of satellites used for solution.

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.

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.

Walking together: side-by-side walking model for an interacting robot

This paper presents a computational model for side-by-side walking within human-robot interaction (HRI). In this work we address the importance of future motion utility (motion anticipation) of two walking partners. Previous studies only considered a robot moving alongside a person without collisions and with simple velocity-based predictions. In contrast, our proposed model includes two major considerations. First, it considers the current goal, modeling side-by-side walking as a process of moving toward a goal while maintaining a relative position with the partner. Second, it takes the partners utility into consideration; it models side-by-side walking as a phenomenon where two agents maximize mutual utilities rather than only considering a single agent utility. The model is constructed based in a set of trajectories from pairs of people recorded in side-by-side walking; then, parameters of the model were calibrated for a mobile robot and tested in an autonomous robot walking side-by-side with participants. Finally, two evaluations were performed. The first evaluation shows that the proposed model considering mutual utilities performs better than a single utility method and a method that keeps distance from the walking partner. In the second evaluation the proposed method was used for a robot deployed in a shopping mall environment where it demonstrated to be effective.

Probabilistic approach for building auditory maps with a mobile microphone array

This paper presents a multi-modal sensor approach for mapping sound sources using an omni-directional microphone array on an autonomous mobile robot. A fusion of audio data (from the microphone array), odometry information and the laser range scan data (from the robot) was used to precisely localize and map the audio sources in an environment. An audio map is created while the robot is autonomously navigating through the environment by continuously generating audio scans with a steered response power (SRP) algorithm. Using the poses of the robot, rays are cast in the map in all directions given by the SRP. Then each occupied cell in the geometric map hit by a ray is assigned a likelihood of containing a sound source. This likelihood is derived from the SRP at that particular instant. Since the localization of the robot is probabilistic, the uncertainty in the pose of the robot in the geometric map is propagated to the occupied cells hit during the ray casting. This process is repeated while the robot is in motion and the map is updated after every audio scan. The generated sound maps were reused and the changes in the audio environment were updated by the robot as it identifies these changes.