Multi-Agent Rendezvous Algorithm with Rectilinear Decision Domain

The aim of this paper is to develop a computationally efficient decentralized rendezvous algorithm for a group of autonomous agents. The algorithm generalizes the notion of sensor domain and decision domain of agents to enable implementation of simple computational algorithms. Specifically, the algorithm proposed in this paper uses a rectilinear decision domain (RDD) as against the circular decision domain assumed in earlier work. Because of this, the computational complexity of the algorithm reduces considerably and, when compared to the standard Ando's algorithm available in the literature, the RDD algorithm shows very significant improvement in convergence time performance. Analytical results to prove convergence and supporting simulation results are presented in the paper.

Positional Consensus of Multi-Agent Systems Using Linear Programming Based Decentralized Control with Rectilinear Decision Domain

In this paper we develop a Linear Programming (LP) based decentralized algorithm for a group of multiple autonomous agents to achieve positional consensus. Each agent is capable of exchanging information about its position and orientation with other agents within their sensing region. The method is computationally feasible and easy to implement. Analytical results are presented. The effectiveness of the approach is illustrated with simulation results.

Rectilinear Path Following in 3D Space

This paper addresses the problem of determining an optimal (shortest) path in three dimensional space for a constant speed and turn-rate constrained aerial vehicle, that would enable the vehicle to converge to a rectilinear path, starting from any arbitrary initial position and orientation. Based on 3D geometry, we propose an optimal and also a suboptimal path planning approach. Unlike the existing numerical methods which are computationally intensive, this optimal geometrical method generates an optimal solution in lesser time. The suboptimal solution approach is comparatively more efficient and gives a solution that is very close to the optimal one. Due to its simplicity and low computational requirements this approach can be implemented on an aerial vehicle with constrained turn radius to reach a straight line with a prescribed orientation as required in several applications. But, if the distance between the initial point and the straight line to be followed along the vertical axis is high, then the generated path may not be flyable for an aerial vehicle with limited range of flight path angle and we resort to a numerical method for obtaining the optimal solution. The numerical method used here for simulation is based on multiple shooting and is found to be comparatively more efficient than other methods for solving such two point boundary value problem.

Frontier Based Multiple Goal Search in Unknown Environments

We present a frontier based algorithm for searching multiple goals in a fully unknown environment, with only information about the regions where the goals are most likely to be located. Our algorithm chooses an ``active goal'' from the ``active goal list'' generated by running a Traveling Salesman Problem (Tsp) routine with the given centroid locations of the goal regions. We use the concept of ``goal switching'' which helps not only in reaching more number of goals in given time, but also prevents unnecessary search around the goals that are not accessible (surrounded by walls). The simulation study shows that our algorithm outperforms Multi-Heuristic LRTA* (MELRTA*) which is a significant representative of multiple goal search approaches in an unknown environment, especially in environments with wall like obstacles.

Goal Seeking for Robots in Unknown Environments

We consider the problem of goal seeking by robots in unknown environments. We present a frontier based algorithm for finding a route to a goal in a fully unknown environment, where information about the goal region (GR), the region where the goal is most likely to be located, is available. Our algorithm efficiently chooses the best candidate frontier cell, which is on the boundary between explored space and unexplored space, having the maximum ``goal seeking index'', to reach the goal in minimal number of moves. Modification of the algorithm is also proposed to further reduce the number of moves toward the goal. The algorithm has been tested extensively in simulation runs and results demonstrate that the algorithm effectively directs the robot to the goal and completes the search task in minimal number of moves in bounded as well as unbounded environments. The algorithm is shown to perform as well as a state of the art agent centered search algorithm RTAA*, in cluttered environments if exact location of the goal is known at the beginning of the mission and is shown to perform better in uncluttered environments.

Nonlinear dynamics and chaotic motions in feedback-controlled two- and three-degree-of-freedom robots

The dynamics of a feedback-controlled rigid robot is most commonly described by a set of nonlinear ordinary differential equations. In this paper we analyze these equations, representing the feedback-controlled motion of two- and three-degrees-of-freedom rigid robots with revolute (R) and prismatic (P) joints in the absence of compliance, friction, and potential energy, for the possibility of chaotic motions. We first study the unforced or inertial motions of the robots, and show that when the Gaussian or Riemannian curvature of the configuration space of a robot is negative, the robot equations can exhibit chaos. If the curvature is zero or positive, then the robot equations cannot exhibit chaos. We show that among the two-degrees-of-freedom robots, the PP and the PR robot have zero Gaussian curvature while the RP and RR robots have negative Gaussian curvatures. For the three-degrees-of-freedom robots, we analyze the two well-known RRP and RRR configurations of the Stanford arm and the PUMA manipulator respectively, and derive the conditions for negative curvature and possible chaotic motions. The criteria of negative curvature cannot be used for the forced or feedback-controlled motions. For the forced motion, we resort to the well-known numerical techniques and compute chaos maps, Poincare maps, and bifurcation diagrams. Numerical results are presented for the two-degrees-of-freedom RP and RR robots, and we show that these robot equations can exhibit chaos for low controller gains and for large underestimated models. From the bifurcation diagrams, the route to chaos appears to be through period doubling.

A Passive Hopper With Lossless Collisions

A passive vertical hopping robot is here highly idealised as two vertically arranged masses acted on by gravity and coupled by a linear spring. The lower mass makes dead (e = 0) collisions with the rigid ground. The equations of motion can be reduced to a one dimensional map. Fixed points of the map are found in which case the robot hops incessantly. For these conservative solutions the lower mass collides with the ground with zero impact velocity. The interval of attraction for these conservative fixed points depends on system parameters.

Haptic Feedback for Injecting Biological Cells using Miniature Compliant Mechanisms

We present a real-time haptics-aided injection technique for biological cells using miniature compliant mechanisms. Our system consists of a haptic robot operated by a human hand, an XYZ stage for micro-positioning, a camera for image capture, and a polydimethylsiloxane (PDMS) miniature compliant device that serves the dual purpose of an injecting tool and a force-sensor. In contrast to existing haptics-based micromanipulation techniques where an external force sensor is used, we use visually captured displacements of the compliant mechanism to compute the applied and reaction forces. The human hand can feel the magnified manipulation force through the haptic device in real-time while the motion of the human hand is replicated on the mechanism side. The images are captured using a camera at the rate of 30 frames per second for extracting the displacement data. This is used to compute the forces at the rate of 30 Hz. The force computed in this manner is sent at the rate of 1000 Hz to ensure stable haptic interaction. The haptic cell-manipulation system was tested by injecting into a zebrafish egg cell after validating the technique at a size larger than that of the cell.

Active Vibration Suppression of One-dimensional Nonlinear Structures Using Optimal Dynamic Inversion

A flexible robot arm can be modeled as an Euler-Bernoulli beam which are infinite degrees of freedom (DOF) system. Proper control is needed to track the desired motion of a robotic arm. The infinite number of DOF of beams are reduced to finite number for controller implementation, which brings in error (due to their distributed nature). Therefore, to represent reality better distributed parameter systems (DPS) should be controlled using the systems partial differential equation (PDE) directly. In this paper, we propose to use a recently developed optimal dynamic inversion technique to design a controller to suppress nonlinear vibration of a beam. The method used in this paper determines control forces directly from the PDE model of the system. The formulation has better practical significance, because it leads to a closed form solution of the controller (hence avoids computational issues).

Modeling of wheeled mobile robots using dextrous manipulation kinematics

Abstract—This document introduces a new kinematic simulation of a wheeled mobile robot operating on uneven terrain. Our modeling method borrows concepts from dextrous manipulation. This allows for an accurate simulation of the way 3-dimensional wheels roll over a smooth ground surface. The purpose of the simulation is to validate a new concept for design of off-road wheel suspensions, called Passive Variable Camber (PVC). We show that PVC eliminates kinematic slip for an outdoor robot. Both forward and inverse kinematics are discussed and simulation results are presented.

A near-singular, flexure jointed, moment sensitive Stewart platform based fore-torque sensor

A force-torque sensor capable of accurate measurement of the three components of externally applied forces and moments is required for force control in robotic applications involving assembly operations. The goal in this paper is to design a Stewart platform based force torque sensor at a near-singular configuration sensitive to externally applied moments. In such a configuration, we show an enhanced mechanical amplification of leg forces and thereby higher sensitivity for the applied external moments. In other directions, the sensitivity will be that of a normal load sensor determined by the sensitivity of the sensing element and the associated electronic amplification, and all the six components of the force and torque can be sensed. In a sensor application, the friction, backlash and other non-linearities at the passive spherical joints of the Stewart platform will affect the measurements in unpredictable ways. In this sensor, we use flexural hinges at the leg interfaces of the base and platform of the sensor. The design dimensions of the flexure joints in the sensor have been arrived at using FEA. The sensor has been fabricated, assembled and instrumented. It has been calibrated for low level loads and is found to show linearity and marked sensitivity to moments about the three orthogonal X, Y and Z axes. This sensor is compatible for usage as a wrist sensor for a robot under development at ISRO Satellite Centre.

Transfer of dynamic learning across postures.

When learning a difficult motor task, we often decompose the task so that the control of individual body segments is practiced in isolation. But on re-composition, the combined movements can result in novel and possibly complex internal forces between the body segments that were not experienced (or did not need to be compensated for) during isolated practice. Here we investigate whether dynamics learned in isolation by one part of the body can be used by other parts of the body to immediately predict and compensate for novel forces between body segments. Subjects reached to targets while holding the handle of a robotic, force-generating manipulandum. One group of subjects was initially exposed to the novel robot dynamics while seated and was then tested in a standing position. A second group was tested in the reverse order: standing then sitting. Both groups adapted their arm dynamics to the novel environment, and this movement learning transferred between seated and standing postures and vice versa. Both groups also generated anticipatory postural adjustments when standing and exposed to the force field for several trials. In the group that had learned the dynamics while seated, the appropriate postural adjustments were observed on the very first reach on standing. These results suggest that the CNS can immediately anticipate the effect of learned movement dynamics on a novel whole-body posture. The results support the existence of separate mappings for posture and movement, which encode similar dynamics but can be adapted independently.

Microfabrication and application of reservoir pins for liquid transfer in biotechnology

Microarraying involves laying down genetic elements onto a solid substrate for DNA analysis on a massively parallel scale. Microarrays are prepared using a pin-based robotic platform to transfer liquid samples from microtitre plates to an array pattern of dots of different liquids on the surface of glass slides where they dry to form spots diameter < 200 μm. This paper presents the design, materials selection, micromachining technology and performance of reservoir pins for microarraying. A conical pin is produced by (i) conventional machining of stainless steel or wet etching of tungsten wire, followed by (ii) micromachining with a focused laser to produce a microreservoir and a capillary channel structure leading from the tip. The pin has a flat end diameter < 100 μm from which a 500 μm long capillary channel < 15 μm wide leads up the pin to a reservoir. Scanning electron micrographs of the metal surface show roughness on the scale of 10 μm, but the pins nevertheless give consistent and reproducible spotting performance. The pin capacity is 80 nanolitres of fluid containing DNA, and at least 50 spots can be printed before replenishing the reservoir. A typical robot holds can hold up to 64 pins. This paper discusses the fabrication technology, the performance and spotting uniformity for reservoir pins, the possible limits to miniaturization of pins using this approach, and the future prospects for contact and non-contact arraying technology.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 IEEE.