Robots are increasingly being used in situations such as search and rescue that require robust navigation capabilities, potentially in areas with little or no GPS or other high-quality localization information. As more robots are used in these scenarios, it becomes viable to collaborate between heterogeneous types of robots to leverage their individual strengths while minimizing their weaknesses. More specifically, in a scenario involving unmanned ground and aerial vehicles (UGV, UAV), the ground robot can contribute its high payload capacity to provide computational resources and high accuracy sensors while the aerial robot can bring its high mobility and capability to traverse obstacles to the team. However, in order for the team to benefit from these capabilities, it must be capable of generating a plan for both robots that allows them to collaboratively localize when necessary. Our approach to this problem is to combine a recently developed state lattice planner using controller-based motion primitives (SLC) with planning using adaptive dimensionality (PAD). The SLC planner allows for robust navigation using a wide variety of sensors including in areas with no or limited high-quality localization information while the PAD planner allows us to expand beyond a single robot and generate plans for a team of robots operating in a high dimensional space. We present our results to this combined approach for a UGV/UAV team operating indoors in areas with limited visual features.

This paper describes a localization algorithm based on hybrid sensor system with application to an active shopping cart. For a given experimental environment, the probability localization method is applied to confirm the global coordinate of the target customer. And a hybrid sensor system combining the Zigbee and odometry is used to improve the localization performance of the active shopping cart. The shopping cart is equipped with motors for mobility and sensors for tracking person. Through experimental work, we corroborate the feasibility of the proposed localization algorithm.

This paper presents tracking algorithms of a mobile robot that tracks a human in front. The mobile robot, which is a differential-driven wheel type, is equipped with a laser range finder to perform human tracking in front. Initially, we recommend the torso part for the robust tracking of the human body in outdoor environment by a laser range finder. To track a human in front, we define a virtual target determined by the velocity of the target human. For a more efficient movement of the mobile robot, a desired heading direction of the robot considering motion vectors of the robot and the human is proposed.

This paper presents a new control scheme of JAIST Active Robotic Walker (JARoW) developed to provide elderly people with sufficient ambulatory capability. Toward its practical use, our focus is placed on how to allow easier and reliable maneuverability by creating a natural user interface. Specifically, our challenge lies in providing a well-functioning controller by detecting what the user wants to do or their intentions. A Kalman filter based tracking scheme is realized to estimate and predict the locations of the user's legs and body in real time. The feedback control can then adjust the motions of JARoW corresponding to the actual user's walking behaviors. Our experiments confirm that JARoW can autonomously adjust its motion direction and velocity without requiring any additional control inputs.

This paper presents tracking algorithms of a mobile robot that follows a human body moving at high speed in unstructured outdoor environment. The mobile robot, which is a differential-driven wheel type, is equipped with one laser range finder to perform high-speed human tracking and obstacle avoidance. Initially, the waist part of the human body is found the best part of the human body for robust tracking by a laser range finder. Next an obstacle avoidance algorithm considering the relative velocity between the robot and obstacles is proposed. It is shown that the proposed human tracking algorithms based on the Kalman filter performs well even in the case of the target occlusions and temporary losses. These tracking algorithms have been applied to a marathoner following robot that carries the marathoner's personal belongings in many different outdoor environments.

This work presents the kinematic modeling and motion planning algorithm for an omni-directional mobile robot with kinematic redundancy. This robot consists of three wheel mechanisms each of which has one redundant joint as compared to the operational degrees. Initially, the kinematic modeling of this robot is conducted. Next, using such a kinematic redundancy of each chain, several motion planning algorithms are suggested. A localization algorithm of the mobile robot based on odometry is presented and specifically, two-leveled obstacle avoidance scheme, which simultaneously considers both large and small obstacles, is presented. The usefulness of the proposed algorithms is verified through simulation.

This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.

This paper deals with design and motion planning of a reconfigurable robot that can be used for inspection of 80-100 mm pipelines. This robot consists of two connecting modules and each module consists of three pairs of caterpillar, which is operated by micro DC motor. The robot is foldable by using an embedded four-bar mechanism and compression of a spring connected to the four-bar allows the robot to maintain contact with the wall of pipelines. Controlling the speed of each caterpillar independently provides a steering capability to go through elbow and T-branch. The robot system has been developed and the validity of this mechanism was proved by experimentation.

As the need of well-being life becomes popular, one of important issues is clean drinking water. Delivery of clean water from the main water fountain to the end user is very crucial. However, typical pipelines are being contaminated by rust and several chemicals. And thus the water being delivered to house is not trustful as the drinking water. Thus, cleaning and inspection of the in-house pipeline becomes an important issue. However, it is hard to find a small-sized motor adequate to the robot for the inspection of the in-house pipeline with diameter of 15 mm. Thus, this paper introduces a new semi-automatic pipeline inspection robotic system for small-sized pipelines. Three different types of inspection robot system were developed, which have different combination of sensors to realize SLAM. The robot system is carried by an extension cable, which is in advance being penetrated into the pipeline by a compressed air. The feasibility of the proposed SLAM using this semi-automatic pipeline inspection robotic system was verified through experimentation.

This work deals with a motion planning algorithm of an omni-directional mobile robot with active caster wheels. A typical problem occurred in the motion control, which has been identified through experimental experiences, is skidding of the mobile wheel. It sometimes results in uncertain rotation of the steering wheel. This is due to mismatching between the command and the mechanical hardware. To cope with this problem, a motion planning algorithm pursuing the natural configuration of the mobile wheel was mainly investigated, which resolves the skidding problem and uncertain motions of the steering wheel. Through simulations and experimentation, the feasibility of this algorithm was verified.