This paper presents a comparative performance evaluation of three different teleoperation interfaces for very low weight (<3 kg) anthropomorphic dual arms intended to conduct complex manipulation tasks involving a certain level of dexterity, accuracy and agility, either in ground service or in aerial manipulation applications. A visual human pose estimation system is developed to obtain the Cartesian and joint values of the user, which are mapped to the corresponding pose of the dual arm manipulator exploiting the equivalent human-robot kinematics. A leader-follower scheme is also presented, using a reduced scale dual arm that can replicate directly the joint positions of the leader arms to the follower arms. A 6-DOF (degrees of freedom) joystick is proposed to generate linear motions more accurately. A total of 60 ground tests were conducted involving 10 participants to determine the accuracy and time performance in two benchmarks (box edges and S contour tracking). Finally, the visual and leader-follower interfaces were evaluated with the dual arm aerial manipulator on flight tests, reporting several findings derived from the system evaluation.

This paper deals with a problem of precise jumping for legged robots: what are the control inputs required to perform a jump that results in a desired landing point? We propose a novel precise jump planning method, formulated as a bilevel optimization problem. The presented formulation exploits certain insights into the jump dynamics, leading to a low-dimensional optimization problem, and allowing fast computation. During the Flight phase of a jump there are no external forces other than gravity acting on the robot, so its centroidal angular momentum (CAM) is conserved, and its center of mass (COM) follows a ballistic trajectory. This trajectory depends solely on COM position and velocity at Liftoff. We define a bilevel optimization problem consisting of a nonlinear upper-level problem, and a lower-level quadratic programming (QP) problem. The upper-level problem selects COM position and velocity at Liftoff that result in a desired landing point, while minimizing CAM at Liftoff. The lower-level problem selects ground reaction forces during Push-Off that achieve desired COM position and velocity at Liftoff. The results are presented on a simulated one-legged robot, but the proposed approach can be extended to bipeds and multi-legged robots.

Since the diagnosis of autism spectrum disorder (ASD) relies heavily on behavioral observations by experienced clinician, we seek to investigate whether parts of this job can be autonomously performed by a humanoid robot using only sensors available on-board. To that end, we developed a robot-assisted ASD diagnostic protocol. In this work we propose the Partially observable Markov decision process (POMDP) framework for such protocol which enables the robot to infer information about the state of the child based on observations of child's behavior. We extend our previous work by developing a protocol POMDP model which uses tasks of the protocol as actions. We devise a method to interface protocol and task models by using belief at the end of a task to generate observations for the protocol POMDP, resulting in a hierarchical POMDP framework. We evaluate our approach through an exploratory study with fifteen children (seven typically developing and eight with ASD).

This paper explores the improvement of non-iterative co-simulation master. A simple hybrid system is depicted and analyzed. Based on this analysis guidelines for calculating the calling sequence are introduced. Guidelines allow the implementation of a new constraint programming algorithm. This algorithm allows better calling sequence selection based solely on information about connecting the co-simulation network. The algorithm is confirmed by the example of co-simulation of a hybrid electric vehicle. In this example, the constraint programming algorithm found a subjectively good calling sequence without any involvement of the model developer.

This paper presents a functional imitation task aimed at facilitating Autism Spectrum Disorder (ASD) diagnostics in children. Imitation plays a key role in the development of social skills at a young age, and studies have shown that the ability to imitate is impaired in children with ASD. Therefore, we expect imitation-based tasks to have diagnostic value. In this paper, we introduce two novel elements of human-robot interaction in the context of autism diagnostics. Instead of pure motoric imitation, we propose imitation tasks involving real objects in the environment. The introduction of physical objects strongly emphasizes joint attention skills, another area that is typically impaired in children with ASD. Furthermore, we present simple object detection, manipulation, tracking and gesture recognition algorithms, suitable for real-time, onboard execution on the small-scale humanoid robot NAO. The proposed system paves the way for fully autonomous execution of diagnostic tasks, which would simplify the deployment of robotic assistants in clinical settings. The source code for all described functionalities has been made publicly available as open-source software. We present a preliminary evaluation of the proposed system with a control group of typically developing preschool children and a group of seven children diagnosed with ASD.

In this paper, we present an algorithm for decentralized control of multiple automated guided vehicles performing transportation tasks within industrial and warehousing environments. By running on each vehicle in the system, the algorithm provides vehicles with capabilities for autonomous path planning and motion co-ordination. The path planning part of the algorithm implements a free-ranging motion scheme by determining the shortest feasible paths considering nonholonomic vehicle constraints. The motion co-ordination part of the algorithm ensures safe vehicle motions by reliable detection and resolution of different conflict situations with other vehicles in the shared workspace. Conflict resolution is based on a vehicle priority scheme and results in temporary stopping or removal of the lower priority vehicles taking part in the conflict. Removal action is always performed within the vehicle's private zone, i.e., the pre-allocated local region of the workspace surrounding the vehicle. By encoding information on the vehicle size and its kinematic constraints, the introduced private zone mechanism provides the necessary physical space required for successful execution of every removal action. We also analyze the stability of the presented algorithm and discuss its deadlock-free and livelock-free properties. Algorithm performance has been validated by simulation using ten vehicles and experimentally on two different setups-a laboratory setup comprising five Pioneer 3DX vehicles and by two state-of-the-art autonomous forklifts in industrial-like operating conditions.

In this paper we propose an algorithm for decentralized control of Automated Guided Vehicles (AGVs) operating in automated warehouse environments. The motion planning part of the algorithm provides vehicles with capabilities for autonomous motion planning considering nonholonomic vehicle constraints and collision-free path execution. The decision making part of the algorithm ensures safe vehicle motions and reliable conflict situation resolution. The proposed control algorithm also prevents occurrence of deadlock and livelock situations. Stability of the algorithm has been proven by its analysis based on automata theory, while its performance has been validated by simulation on a system comprising twenty vehicles as well as experimentally on a laboratory setup comprising five Pioneer 3DX vehicles.

A micro immune optimization algorithm presented in this paper was considered for solving inverse kinematics of a redundant robotic arm. For this purpose, a multiobjective criterion was defined to compare performances of micro immune and micro genetic algorithms. Multiobjective optimization was used to determine a set of best algorithms, and the micro immune algorithm proved superior in solving a selected optimization problem. The assessment of time needed for algorithms to solve the inverse kinematics problem on-line was made based on the tests carried out on a low-cost micro controller.

This paper presents a dynamic routing method for supervisory control of multiple automated guided vehicles (AGVs) that are traveling within a layout of a given warehouse. In dynamic routing a calculated path particularly depends on the number of currently active AGVs' missions and their priorities. In order to solve the shortest path problem dynamically, the proposed routing method uses time windows in a vector form. For each mission requested by the supervisor, predefined candidate paths are checked if they are feasible. The feasibility of a particular path is evaluated by insertion of appropriate time windows and by performing the windows overlapping tests. The use of time windows makes the algorithm apt for other scheduling and routing problems. Presented simulation results demonstrate efficiency of the proposed dynamic routing. The proposed method has been successfully implemented in the industrial environment in a form of a multiple AGV control system.

The paper deals with an application of fuzzy control in joint servo control of a five-axis electric driven articulated robot arm. The same fuzzy control algorithm is used in all joint servo loops, requiring only multiplexing and proper scaling of the fuzzy controller inputs and outputs. The nonlinear character of the proposed fuzzy controller has provided for highly effective trajectory tracking. Trajectory tracking appears satisfactory also in the presence of external disturbances.>