The instantaneous center of rotation (ICOR) is essential engineering information, providing wheel slip data for fully autonomous skid-steering mobile robots driven by deep learning-based dead-reckoning algorithms. Conventionally, cost-effective and compact inertial measurement unit (IMU) sensors based on microelectromechanical systems (MEMSs) have been employed for ICOR measurement. However, these sensors inherently rely on the differential value of gyroscope data, leading to significant estimation errors due to the amplified noise at low sampling rates. To address this challenge, we developed an acceleration sensor array based on a fiber Bragg grating, inspired by existing MEMS acceleration sensor arrays, which allows ICOR estimation solely from the acceleration data provided by two sensors at the beginning of rotation. The estimation accuracies in the clockwise and counter-clockwise directions were measured to be 3.1 ± 5.1 mm and 3.7 ± 9.9 mm, respectively. Furthermore, a peak detection algorithm was proposed for extracting the peak acceleration under different ICOR conditions using the variance and Bollinger bands. The precision level was comparable to those of optical cameras and MEMS IMU sensors that use computationally intensive algorithms, such as Kalman filters, even without differentiation. This breakthrough in measurement capability opens up the possibility for real-time ICOR estimation, allowing the complete automation of skid-steering-based robots and benefiting fields constrained by the structural limitations of conventional MEMS IMU sensors.

Energy-efficient gait planning and control is established for biped robots, which utilizes the allowable zero moment point (ZMP) region. Based on 3-D linear inverted pendulum mode (LIPM), we construct a practical gait planning algorithm for a given travel distance minimizing the energy consumed by the actuators of humanoid joints with 1) an online gait synthesis (GSYN) algorithm to generate a complete walking cycle (a starting step, several cyclic steps, and a stopping step) compromising waking stability and energy efficiency at the fully utilizing allowable ZMP region and with 2) effective gait parameter optimization to maximize the energy efficiency of the gaits generated by GSYN, finding two optimal parameters-number of steps and average walking speed-satisfying geometrical constraints, friction force limit, and yawing moment limit to guarantee feasible motions. The proposed algorithm was verified through simulations, and the gait control system was implemented on a DARwIn-OP humanoid robot.

The minimum-energy trajectory planning and control algorithm on a straight line with rotation is proposed for battery-powered three-wheeled omni-directional mobile robots (TOMRs). In order to lengthen the operation time of a mobile robot with carried batteries, we have chosen a practical cost function as the energy from the batteries to actuation motors. The optimal control theory using a Hamiltonian function is used to solve the minimum-energy trajectory, which gives the rotational velocity profile in analytic form and the translational velocity profile by numerical method. We also establish search algorithm to find the minimum-energy trajectory. These minimum-energy velocity trajectories are compared to the conventional trapezoidal velocity profiles via simulation to analyze actuation energy consumption, which reveal energy savings up to 4.76 %. Also a trajectory following control system is implemented to validate the actual performance.

The minimum-time trajectory planning problem for three-wheeled omnidirectional mobile robots (TOMRs) is solved based on the combined dynamic model of a mobile robot and dc motor actuators, under the constraint of bounded control inputs due to the battery voltage. We constrain that the bounded-curvature path based on a smooth road (which is described as a clothoid) be given for the translational motion of the TOMR and that the reference profile with respect to the path-length parameter be predetermined for the heading motion of the TOMR. The dynamics of the TOMR is transformed into normal and tangent spaces for motion analysis on the bounded-curvature path. We find out the time-optimality condition of the TOMR, which imposes that the input voltage vector of three motors should have at least one extreme component. Based on the optimality condition, we present a systematic way to construct the optimal control input vector. Finally, several examples are analyzed by the use of the proposed method.

We examine time-optimal straight-line trajectory generation for three-wheeled omni-directional mobile robots. Our studies are based on the mobile robot dynamics with battery voltage bounds. We formulate a multi-objective problem that has both translational and rotational costs for time-optimality. Using the switching functions by maximum principle, we present a new approach to determine extreme control components used as motor input. Through the proposed method, we find three combinations of optimal candidates to be the optimal solutions of our problem.

The Dynamic Voltage Scaling (DVS) technique has been widely studied for energy-constrained real-time systems; however, its application to control systems has not been studied in a variety of aspects. This article presents a novel method to simultaneously schedule processor voltage and control-task periods online, considering energy-efficiency of control systems as a whole. A new performance index is proposed, which contains both control performance and processor energy terms. Then, an online scheduler assigning processor voltage and control-task periods that maximizes the performance index is proposed. The performance of the proposed scheduler under varying control workload is verified using MATLAB simulations and experiments on an actual DVS hardware platform.

The minimum-energy translational trajectory planning algorithm is proposed for battery-powered three-wheeled omni-directional mobile robots (TOMRs). We have chosen a practical cost function as the total energy drawn from the batteries, in order to lengthen the operational time of a mobile robot with given batteries. After establishing the dynamic equations of TOMRs, the optimal control theory is used to solve the minimum-energy trajectory, which gives the velocity profile in analytic form. Various simulations are performed and the consumed energy is compared to other velocity trajectories. Simulation results reveal that the energy saving is achieved of up to 2.4% compared with loss-minimization control, and up to 4.3% compared with conventional trapezoidal velocity profile.

Energy of Wheeled Mobile Robot (WMR) is usually supplied by batteries with finite energy. In order to extend run-time of battery-powered WMR, it is necessary to minimize the energy consumption. The energy is dissipated mostly in the motors, which strongly depends on the velocity profile. This paper investigates energy efficiency for typical 3-step velocity control methods to minimize a new energy object function which considers practical energy consumption dissipated in motors related to motor dynamics, velocity profile, and motor control input. We performed analyses on energy consumption for the four typical 3-step velocity profiles: S-C-S, T-C-T, S-C-T, and T-C-S (S is an acceleration/deceleration section by step control input, T is an acceleration/deceleration section by acceleration/deceleration phase of trapezoidal velocity profile, and C is a cruise section with a constant velocity). Also we suggested an efficient iterative search algorithm with binary search which can find the numerical solution quickly. We performed various computer simulations to show the performance of energy-efficient 3-step velocity control in comparison with a conventional 3-step trapezoidal profile with a reasonable constant acceleration rate as a benchmark. Simulation results reveal that the S-C-T is the most energy efficient 3-step velocity control profile, which enables WMR to extend working time up to 30%.

We present a robust localization algorithm for mobile robots with laser range finders, which takes feature based approach for reliable matching process as well as point based approach for accurate and stable pose calculation. An efficient sequential segmentation algorithm is suggested, which also performs least square fitting processes simultaneously. Hence robust line segment features can be obtained with much less computation time. A novel cost function for pose calculation is suggested and formulated, which makes it possible to compute the location and orientation accurately and reliably. We examined the validity of the proposed algorithm with various simulations and experiments, and revealed its robustness and accuracy compared to other typical localization algorithms. The results show that the localization error of the proposed algorithm is 40% to 80% less than conventional localization algorithms.

For real-time computer-controlled systems, control performances of tasks as well as energy consumption of overall system must be optimized. A control task does not have a fixed period but a range of periods in which control performance varies. Hence when more than one control tasks are scheduled on a single processor, an optimization problem appears. Furthermore, when an energy saving technique such as dynamic voltage scaling is used, its properties affect the control performance. Using a performance index that involves control performance and energy consumption, a static solution is proposed to obtain optimal processor speed and a set of periods for given control tasks in O(k). Also, a dynamic solution is proposed to utilize system services of real-time operating systems to overcome unavoidable deficiencies of the static solution and to further reduce the energy consumption of the overall system. The performances of proposed solutions are revealed via simulation studies.

We analyze inherent time-delay of commercial off-the-shelf (COTS) vision system in measuring postures of moving objects, and present the minimum sensing time-delay obtainable. Slow shutter speed in COTS vision systems causes motion-blur in moving objects, which invites vagueness in time-delay as well as in measured position. We can overcome the vagueness by introducing exposure time-delay. Considering the exposure time-delay and image transfer time of overlay boards, we can find out the minimum sensing time-delay (MSTD) in COTS vision systems. Also, by utilizing the field-interleaved scanning and slow readout speed of COTS cameras, we propose a posture determination algorithm that guarantees MSTD, which uses both field-based processing and processing during overlaying methods. To verify validity of the proposed MSTD and the algorithm, the overall time-delay of our COTS vision system is measured directly via pendulum experiments.

Real-time computer systems are often used in harsh environments, such as aerospace, and in industry. Such systems are subject to many transient faults while in operation. Checkpointing enables a reduction in the recovery time from a transient fault by saving intermediate states of a task in a reliable storage facility, and then, on detection of a fault, restoring from a previously stored state. The interval between checkpoints affects the execution time of the task. Whereas inserting more checkpoints and reducing the interval between them reduces the reprocessing time after faults, checkpoints have associated execution costs, and inserting extra checkpoints increases the overall task execution time. Thus, a trade-off between the reprocessing time and the checkpointing overhead leads to an optimal checkpoint placement strategy that optimizes certain performance measures. Real-time control systems are characterized by a timely, and correct, execution of iterative tasks within deadlines. The reliability is the probability that a system functions according to its specification over a period of time. This paper reports on the reliability of a checkpointed real-time control system, where any errors are detected at the checkpointing time. The reliability is used as a performance measure to find the optimal checkpointing strategy. For a single-task control system, the reliability equation over a mission time is derived using the Markov model. Detecting errors at the checkpointing time makes reliability jitter with the number of checkpoints. This forces the need to apply other search algorithms to find the optimal number of checkpoints. By considering the properties of the reliability jittering, a simple algorithm is provided to find the optimal checkpoints effectively. Finally, the reliability model is extended to include multiple tasks by a task allocation algorithm.

We derive a near-time-optimal trajectory for wheeled mobile robots (WMRs) satisfying the following: 1) initial and final postures/velocities; and 2) battery voltage and armature current constraints, under assumptions of simplified dynamics and constant translational/rotational velocity sections. We use a simplified dynamic model for WMRs, neglecting inductances of motor armatures and divide our trajectory generation algorithm for cornering motion into three sections. We specify a path-deviation requirement for obstacle avoidance. Transforming dynamics into uncorrelated form with regard to translational and rotational velocities, we make extreme control possible. By separating the rotational section with translational sections and determining the velocity scale factor a near-time-optimal trajectory can be obtained. Simulation results along with inverse control of path-following are given to validate the generated trajectory.

We present a practical and systematic strategy for measuring machine intelligence quotient (MIQ) of human-machine cooperative systems. So far, much research related to intelligent control has been established, but the issues about definition and measurement of machine intelligence are not yet clearly formulated. We propose the intelligence task graph (ITG) as a modeling and analysis tool. By using the ITG, the machine contribution of human-machine cooperative systems can be explicitly separated from the human contribution and directly formulated by equations. Thus, the ITG is a very useful modeling tool for estimating the MIQ that was introduced as the index of machine intelligence. We propose a new human-oriented method for estimating the MIQ. This research will help engineers assess intelligent machines that are designed to perform human-like qualities with a high degree of autonomy.

Real-time computers are often used in embedded, life-critical applications where high reliability is important. A common approach to make such systems dependable is to vote on redundant processors executing multiple copies of the same task. The most popular redundant structure is triple modular redundancy (TMR). The processors that make up such systems are subject not only to independently occurring permanent and transient faults, but to correlated transient faults, such as electromagnetic interference (EMI) caused by the operating environment. This paper proposes two new scheduling strategies for TMR computer-controllers. Both strategies can tolerate correlated faults as well as independent faults. These strategies, TMR-R (TMR with rotated task group) and TMR-Q (TMR with quintuple computation), are developed using task grouping and assignment. To evaluate the reliability of these strategies, a discrete-time Markov model for control systems is devised. Reliability equations for the TMR-R and TMR-Q are derived from state transitions of sampling intervals based on the Markov model. The reliability of these TMR is proved by comparing them with a conventional TMR, using numerical analysis. These proposed strategies are anticipated to be useful for control systems operating in harsh environments, such as controllers of airplanes or nuclear power plants.

In existing fuzzy logic controllers (FLCs), input variables are mostly the error and the change-of-error regardless of complexity of controlled plants. Either control input u or the change of control input Deltau is commonly used as its output variable. A rule table is then constructed on a two-dimensional (2-D) space. This scheme naturally inherits from conventional proportional-derivative (PD) or proportional-integral (PI) controller. Observing that 1) rule tables of most FLCs have skew-symmetric property and 2) the absolute magnitude of the control input |u| or |Deltau| is proportional to the distance from its main diagonal line in the normalized input space, we derive a new variable called the signed distance, which is used as a sole fuzzy input variable in our simple FLC called single-input FLC (SFLC). The SFLC has many advantages: The total number of rules is greatly reduced compared to existing FLCs, and hence, generation and tuning of control rules are much easier. The proposed SFLC is proven to be absolutely stable using Popov criterion. Furthermore, the control performance is nearly the same as that of existing FLCs, which is revealed via computer simulations using two nonlinear plants.

In this paper, we describe the development of our BEST nano-robot soccer team composed of 5 robots, a vision system, and communication modules. Each nano-robot is designed with an all-in-one type microcontroller and two DC motors fixed in parallel due to its small size, 4(cm)/spl times/4(cm)/spl times/4.5(cm). A vision system capable of 30 recognitions/sec provides each robot's position feedback via RF communication. A three level hierarchical software control scheme is adopted: role allocation, target generation, and visual servoing. Dynamic role allocation is done in the highest layer (the role allocation layer) regarding the context and role importance. In the target generation layer, target positions of robots are generated based on 6 roles, 2-2-1 formation, and libero system. In the visual servoing layer, path planning is achieved using a newly proposed line-circle algorithm with linear and circular paths as our basic strategy. Normally, 2-2-1 formation is adopted. When the both attackers are blocked, one of defenders is assigned to continue the attack as libero (temporary 3-1-1 formation).

We present a measure called collidability measure for obstacle avoidance control of redundant manipulators. Considering moving directions of manipulator links, the collidability measure is defined as the inverse of sum of predicted collision distances between links and obstacles. This measure is suitable for obstacle avoidance control since directions of moving links are as important as distances to obstacles. For dynamic redundancy resolution, null space control is utilized to avoid obstacles by minimizing the collidability measure. Also, by clarifying decomposition in the joint acceleration level, we present a simple dynamic control law with bounded joint torques which guarantees tracking of a given end-effector trajectory and improves a kinematic cost function such as collidability measure. Simulation results are presented to illustrate the effectiveness of the proposed algorithm.

We present a practical and systematic strategy for measuring machine (robot) intelligence. A lot of research related to intelligent control has been carried out, but the subjects of definition and measurement of machine intelligence are not clearly formulated yet. We propose a human-oriented definition of machine intelligence and an intelligence task graph (ITG) as a modeling and analysis tool. By using an ITG, the machine contribution of human-machine cooperative systems is easily separated from the human contribution and directly described as numerical equations. Therefore we conclude that the ITG is very useful for estimating the machine intelligence quotient. This research will help engineers design intelligent robots which support human-friendly interfaces and perform environment controls with high performance.

A fast, scalable hardware earliest deadline first (EDF) link scheduler for ATM switching network is developed. This EDF scheduler is a fast hardware solution suitable for real time scheduler on nodes in ATM switching networks up to 2.5 Gbps switching speed (scheduling within 0.17 /spl mu/s), capable of performing simultaneous input and output operations within two clock cycles (mostly in one clock cycle). The designed hardware is efficient since the architecture employs the minimum size EDF priority queue, combined with variable size FIFO queues for channels implemented with a two port memory buffer. Early traffic can be simply checked and delayed. Also, it is scalable with respect to the number of channels C and the total number of buffers N. Moreover, deadline folding technique eliminates the need to extend the deadline resolution. Simulation studies and layout design demonstrate the efficiency and utility of the proposed architecture.

A new tri-aural ultrasonic sensor system is suggested to build more accurate world maps for mobile robots with less scanning. In ordinary single sensor systems, the inherent beam-width of sonar transmitter causes ambiguity in sensing direction. Dual sensors may be used to discriminate plane and corner with several scans. However, the proposed method uses triple sensors, and achieves more accuracy with less scanning. Simulation studies and experimental results reveal the performance of the proposed method.

Despite its close relationship to robot arm dynamics, conventional path planning does not take it into account, leading to the possible underutilization of the robot's capabilities. The authors have developed a minimum-time path-planning method in joint space taking into consideration robot arm dynamics as well as other realistic constraints. The main differences between this method and others are: (1) an absolute tolerance in the path deviation at each corner point can be specified; (2) local upper bounds on joint accelerations are derived from the arm dynamics so as to nearly fully utilize robot's capabilities; and (3) a set of local optimization problems, one at every local corner point, replaces the global minimum-time problem, thus making the minimum-time path-planning problem simpler and easier to solve. The method is applied to the path planning of the first three joints of the Unimation PUMA 600 series robot arm, using its simulator on a DEC VAX-11/780. The results show significant improvements in the total traveling time in addition to the ease and simplicity obtained from the decomposition of the global problem into a set of local optimization problems.