Extended Kalman filtering for robotic proximity sensing

The authors address the problems in using a fiber-optic proximity sensor to detect robot end-effector positioning errors in performing a contact task when uncertainties about target position exist. An extended Kalman filter approach is employed to solve the nonlinear filtering problem. Some experimental results are given.

Modular non-computational-connectionist-hybrid neural network approach to robotic systems

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Developing skin analogues for a robotic octopus

In order to fabricate a biomimetic skin for an octopus inspired robot, a new process was developed based on mechanical properties measured from real octopus skin. Various knitted nylon textiles were tested and the one of 10-denier nylon was chosen as reinforcement. A combination of Ecoflex 0030 and 0010 silicone rubbers was used as matrix of the composite to obtain the right stiffness for the skin-analogue system. The open mould fabrication process developed allows air bubble to escape easily and the artificial skin produced was thin and waterproof. Material properties of the biomimetic skin were characterised using static tensile and instrumented scissors cutting tests. The Young’s moduli of the artificial skin are 0.08 MPa and 0.13 MPa in the longitudinal and transverse directions, which are much lower than those of the octopus skin. The strength and fracture toughness of the artificial skin, on the other hand are higher than those of real octopus skins. Conically-shaped skin prototypes to be used to cover the robotic arm unit were manufactured and tested. The biomimetic skin prototype was stiff enough to maintain it conical shape when filled with water. The driving force for elongation was reduced significantly compared with previous prototypes.

Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network

The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Net- work (AERONET) routinely monitor clouds using zenith ra- diances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m−2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m−2 at the ARM Oklahoma site during 2007– 2008, our 1.5-min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5-min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22 % are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11 % with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.

Robotic multiwell planar patch-clamp for native and primary mammalian cells

Robotic multiwell planar patch-clamp has become common in drug development and safety programs because it enables efficient and systematic testing of compounds against ion channels during voltage-clamp. It has not, however, been adopted significantly in other important areas of ion channel research, where conventional patch-clamp remains the favored method. Here, we show the wider potential of the multiwell approach with the ability for efficient intracellular solution exchange, describing protocols and success rates for recording from a range of native and primary mammalian cells derived from blood vessels, arthritic joints and the immune and central nervous systems. The protocol involves preparing a suspension of single cells to be dispensed robotically into 4-8 microfluidic chambers each containing a glass chip with a small aperture. Under automated control, giga-seals and whole-cell access are achieved followed by preprogrammed routines of voltage paradigms and fast extracellular or intracellular solution exchange. Recording from 48 chambers usually takes 1-6 h depending on the experimental design and yields 16-33 cell recordings.

A feasible study of EEG-driven assistive robotic system for stroke rehabilitation

Stroke is a medical emergency and can cause a neurological damage, affecting the motor and sensory systems. Harnessing brain plasticity should make it possible to reconstruct the closed loop between the brain and the body, i.e., association of the generation of the motor command with the somatic sensory feedback might enhance motor recovery. In order to aid reconstruction of this loop with a robotic device it is necessary to assist the paretic side of the body at the right moment to achieve simultaneity between motor command and feedback signal to somatic sensory area in brain. To this end, we propose an integrated EEG-driven assistive robotic system for stroke rehabilitation. Depending on the level of motor recovery, it is important to provide adequate stimulation for upper limb motion. Thus, we propose an assist arm incorporating a Magnetic Levitation Joint that can generate a compliant motion due to its levitation and mechanical redundancy. This paper reports on a feasibility study carried out to verify the validity of the robot sensing and on EEG measurements conducted with healthy volunteers while performing a spontaneous arm flexion/extension movement. A characteristic feature was found in the temporal evolution of EEG signal in the single motion prior to executed motion which can aid in coordinating timing of the robotic arm assistance onset.

Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Planning is one of the key problems for autonomous vehicles operating in road scenarios. Present planning algorithms operate with the assumption that traffic is organised in predefined speed lanes, which makes it impossible to allow autonomous vehicles in countries with unorganised traffic. Unorganised traffic is though capable of higher traffic bandwidths when constituting vehicles vary in their speed capabilities and sizes. Diverse vehicles in an unorganised exhibit unique driving behaviours which are analysed in this paper by a simulation study. The aim of the work reported here is to create a planning algorithm for mixed traffic consisting of both autonomous and non-autonomous vehicles without any inter-vehicle communication. The awareness (e.g. vision) of every vehicle is restricted to nearby vehicles only and a straight infinite road is assumed for decision making regarding navigation in the presence of multiple vehicles. Exhibited behaviours include obstacle avoidance, overtaking, giving way for vehicles to overtake from behind, vehicle following, adjusting the lateral lane position and so on. A conflict of plans is a major issue which will almost certainly arise in the absence of inter-vehicle communication. Hence each vehicle needs to continuously track other vehicles and rectify plans whenever a collision seems likely. Further it is observed here that driver aggression plays a vital role in overall traffic dynamics, hence this has also been factored in accordingly. This work is hence a step forward towards achieving autonomous vehicles in unorganised traffic, while similar effort would be required for planning problems such as intersections, mergers, diversions and other modules like localisation.

Planning autonomous vehicles in the absence of speed lanes using lateral potentials

Chaotic traffic, prevalent in many countries, is marked by a large number of vehicles driving with different speeds without following any predefined speed lanes. Such traffic rules out using any planning algorithm for these vehicles which is based upon the maintenance of speed lanes and lane changes. The absence of speed lanes may imply more bandwidth and easier overtaking in cases where vehicles vary considerably in both their size and speed. Inspired by the performance of artificial potential fields in the planning of mobile robots, we propose here lateral potentials as measures to enable vehicles to decide about their lateral positions on the road. Each vehicle is subjected to a potential from obstacles and vehicles in front, road boundaries, obstacles and vehicles to the side and higher speed vehicles to the rear. All these potentials are lateral and only govern steering the vehicle. A speed control mechanism is also used for longitudinal control of vehicle. The proposed system is shown to perform well for obstacle avoidance, vehicle following and overtaking behaviors.

Planning autonomous vehicles in the absence of speed lanes using an elastic strip

Planning of autonomous vehicles in the absence of speed lanes is a less-researched problem. However, it is an important step toward extending the possibility of autonomous vehicles to countries where speed lanes are not followed. The advantages of having nonlane-oriented traffic include larger traffic bandwidth and more overtaking, which are features that are highlighted when vehicles vary in terms of speed and size. In the most general case, the road would be filled with a complex grid of static obstacles and vehicles of varying speeds. The optimal travel plan consists of a set of maneuvers that enables a vehicle to avoid obstacles and to overtake vehicles in an optimal manner and, in turn, enable other vehicles to overtake. The desired characteristics of this planning scenario include near completeness and near optimality in real time with an unstructured environment, with vehicles essentially displaying a high degree of cooperation and enabling every possible(safe) overtaking procedure to be completed as soon as possible. Challenges addressed in this paper include a (fast) method for initial path generation using an elastic strip, (re-)defining the notion of completeness specific to the problem, and inducing the notion of cooperation in the elastic strip. Using this approach, vehicular behaviors of overtaking, cooperation, vehicle following,obstacle avoidance, etc., are demonstrated.

Optimal steady motions for oriented vehicles

Motivated by the motion planning problem for oriented vehicles travelling in a 3-Dimensional space; Euclidean space E3, the sphere S3 and Hyperboloid H3. For such problems the orientation of the vehicle is naturally represented by an orthonormal frame over a point in the underlying manifold. The orthonormal frame bundles of the space forms R3,S3 and H3 correspond with their isometry groups and are the Euclidean group of motion SE(3), the rotation group SO(4) and the Lorentzian group SO(1; 3) respectively. Orthonormal frame bundles of space forms coincide with their isometry groups and therefore the focus shifts to left-invariant control systems defined on Lie groups. In this paper a method for integrating these systems is given where the controls are time-independent. For constant twist motions or helical motions, the corresponding curves g(t) 2 SE(3) are given in closed form by using the well known Rodrigues’ formula. However, this formula is only applicable to the Euclidean case. This paper gives a method for computing the non-Euclidean screw/helical motions in closed form. This involves decoupling the system into two lower dimensional systems using the double cover properties of Lie groups, then the lower dimensional systems are solved explicitly in closed form.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard CCD camera and a ToF camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time of light information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localization. Further development of these methods will make it possible to identify objects and their position in the real world, and to use this information to prevent possible collisions between the robot and such objects.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard charge-coupled device (CCD) camera and a time-of-flight (ToF) camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time-of-flight information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time, this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localisation. Further development of these methods will make it possible to identify objects and their position in the real world and to use this information to prevent possible collisions between the robot and such objects.

Heuristic based evolution for the coordination of autonomous vehicles in the absence of speed lanes

The current state of the art in the planning and coordination of autonomous vehicles is based upon the presence of speed lanes. In a traffic scenario where there is a large diversity between vehicles the removal of speed lanes can generate a significantly higher traffic bandwidth. Vehicle navigation in such unorganized traffic is considered. An evolutionary based trajectory planning technique has the advantages of making driving efficient and safe, however it also has to surpass the hurdle of computational cost. In this paper, we propose a real time genetic algorithm with Bezier curves for trajectory planning. The main contribution is the integration of vehicle following and overtaking behaviour for general traffic as heuristics for the coordination between vehicles. The resultant coordination strategy is fast and near-optimal. As the vehicles move, uncertainties may arise which are constantly adapted to, and may even lead to either the cancellation of an overtaking procedure or the initiation of one. Higher level planning is performed by Dijkstra's algorithm which indicates the route to be followed by the vehicle in a road network. Re-planning is carried out when a road blockage or obstacle is detected. Experimental results confirm the success of the algorithm subject to optimal high and low-level planning, re-planning and overtaking.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

Proposal of a floating mechanism based on magnetic levitation method toward EEG-driven robotic system

We discussed a floating mechanism based on quasi-magnetic levitation method that can be attached at the endpoint of a robot arm in order to construct a novel redundant robot arm for producing compliant motions. The floating mechanism can be composed of magnets and a constraint mechanism such that the repelling force of the magnets floats the endpoint part of the mechanism stable for the guided motions. The analytical and experimental results show that the proposed floating mechanism can produce stable floating motions with small inertia and viscosity. The results also show that the proposed mechanism can detect small force applied to the endpoint part because the friction force of the mechanism is very small.