A common limitation of autonomous tissue manipulation in robotic minimally invasive surgery (MIS) is the absence of force sensing and control at the tool level. Recently, our team has developed miniature force-sensing forceps that can simultaneously measure the grasping and pulling forces during tissue manipulation. Based on this design, here we further present a method to automate tissue traction that comprises grasping and pulling stages. During this process, the grasping and pulling forces can be controlled either separately or simultaneously through force decoupling. The force controller is built upon a static model of tissue manipulation, considering the interaction between the force-sensing forceps and soft tissue. The efficacy of this force control approach is validated through a series of experiments comparing targeted, estimated, and actual reference forces. To verify the feasibility of the proposed method in surgical applications, various tissue resections are conducted on ex vivo tissues employing a dual-arm robotic setup. Finally, we discuss the benefits of multiforce control in tissue traction, evidenced through comparative analyses of various ex vivo tissue resections with and without the proposed method, and the potential generalization with traction on different tissues. The results affirm the feasibility of implementing automatic tissue traction using miniature forceps with multiforce control, suggesting its potential to promote autonomous MIS.

UAVs or Unmanned Aerial Vehicles are an upcoming technology which has eased human lifestyles in many ways. Due to this trend future skies have a risk of getting congested. In such a situation time optimal collision avoidance would be extremely vital to travel in a shortest possible time by avoiding collisions. The paper proposes a novel method for time optimal collision avoidance for UAVs. The proposed algorithm is constructed as a three-stage approach based on the Collision Cone method with slight modifications. A sliding mode controller is used as the control law for the navigation. Mathematical proofs are included to verify the time optimality of the proposed method. The efficiency and the applicability of the work carried out is confirmed by both simulation and experimental results. An automated Matrice 600 Pro hexacopter has been used for the experiments.

This paper presents the impact dynamics and post-impact motion control of both the spacecraft base and the robot with flexible manipulator. First, the dynamic equations of the system are reformulated in the task space using Lagrangian equation based on assumed mode method. Then, the impact dynamic equations between end-effector of robotic manipulator and the contact object are derived based on momentum conservation principle. To control the unstable motion of the system due to the impact effect caused by an external object, a combination of Sliding Mode Control (SMC) and Linear Quadratic Regulator (LQR) control algorithm is derived. The performances of the proposed control method are validated by numerical simulations. The simulation results show the satisfactory performance of the system.

This paper presents a methodology to optimize the drive forces and steering angles for achieving accurate path tracking by a four wheel steer and four wheel drive (4WS4WD) vehicle. The 7-order Bézier curves are applied in path planning and online tracking, and used to obtain the kinematic and dynamic profiles for the guidance of vehicle. The 8-input dynamic model is developed and applied in an objective function, which is optimized by PSO algorithm to obtain the optimal driving and steering motions for tracking the planned path precisely. The slip angles and lateral forces are also considered in the dynamic model, and the vehicle motion optimization can be implemented in real time. Note that this paper does not address the implementation of the force and steering controllers at the wheels. Simulation results are provided to validate the proposed method.

This paper presents a dynamic model representing the rigid body motion of a four wheel steer and four wheel drive (4WS4WD) vehicle which also takes into account the rotational dynamics of the individual wheels as well as the tyre terrain interactions. It also presents a modified pursuit algorithm taking into account the kinematic constraints as well as a decentralized wheel propulsion control system. Simulation results are presented to show the model validation and the successful implementation of the path tracking algorithm.

This paper presents the autonomous platoon formations that could be used to form a platoon of buses on dedicated roadways. A non-linear dynamic model and a path-following and car-following controller are presented and their performance evaluated. Using the dynamic model a platoon of three vehicles is simulated along a multi-segment reference path, demonstrating the adequate lateral and longitudinal performance required for simulations of scale, meaning the operation of multiple vehicles in a convoy or the coordinated operation of multiple convoys. The models and controller presented here will form the basis for future research involving simulations of autonomous vehicles when maneuvering through a series of complex intersections and roadways.

Autonomous guidance of agricultural vehicles is vital as mechanized farming production becomes more prevalent. It is crucial that tractor-trailers are guided with accuracy in both lateral and longitudinal directions, whilst being affected by large disturbance forces, or slips, owing to uncertain and undulating terrain. Previous research has been concentrated on trajectory control that provides longitudinal and lateral accuracy if the vehicle moves without sliding and/or the trailer is passive. In this paper, we extend current results by addressing the problem of robust trajectory tracking along straight and circular paths of a tractors with steerable trailers. We develop a controller that is a robust combination of a backstepping and nonlinear PI control. For vehicles subjected to slip, the proposed controller makes the lateral deviations and the orientation errors of the tractor and trailer converge to a neighborhood near the origin. Simulation results are presented to illustrate that the suggested controller ensures precise trajectory tracking in the presence of slip.

In this paper, a vectored thrust aerial vehicle( VTAV) that has three ducted fans is considered. Since ducted fans are powerful and effective in providing lift, they are suitable for thrusters of UAVs, but modeling their aerodynamic effects such as ram drag is very difficult. The VTAV has one ducted fan fixed to its body and two ducted fans that can be tilted in order to make rotational moments, which makes the system dynamics even more complicated. This paper focuses on giving a precise dynamical model that includes aerodynamic effects of ducted fans. Then it presents a hovering controller based on the dynamical model developed. Since the horizontal dynamics are under actuated, a switching control approach is introduced to realize a stabilizing controller. Numerical simulations prove the validity of the approach.

This paper describes a novel approach to surface fitting for the creation of a 3D surface map for use by a small articulated wall-climbing robot. Both a laser range finder and a low-resolution camera are used to acquire data in a sparse manner. By scanning at large intervals, such as every 5–10°, and then fusing the data, it is shown that it is possible to fit planar surfaces at an accuracy comparable to dense range scanning. Infinite planes are fit to lines extracted from the range scans and then the image corners and lines are used to provide polygon boundaries on these planes. This method is faster and more flexible, both in acquiring data and in computing the planar features and less memory is required. This method also works well in feature poor environments where stereo vision can struggle and does not need to process the feature correspondences in the typical fashion which also saves time. This surface fitting approach is demonstrated using a real data set and results show promise in providing quick yet accurate 3D planar surfaces which could be integrated into SLAM and motion planning frameworks.

Ground plane detection plays an important role in stereo vision based obstacle detection methods. Recently, V-disparity image has been widely used for ground plane detection. The existing approach based on V-disparity image can detect flat ground successfully but have difficulty in detecting non-flat ground. In this paper, we discuss the representation of non-flat ground in V-disparity image, based on which we propose a method to detect non-flat ground using V-disparity image.

A new method for calculating the collision free areas of a manipulator's configuration space (C-space) is demonstrated in this paper. Rather than mapping a point obstacle to an n-dimensional surface in the C-space, the dual problem is considered. That is, regions of the C-space that are guaranteed to be collision free are determined. Whilst some configurations that are collision free are not captured by this method, its speed and low memory usage make it ideal for motion planning in the C-space. It is especially useful for mobile manipulators such as wall climbing robots, where the surrounding terrain changes with each step the robot takes. Being able to compute the mapping of Cartesian to C-space representations of objects in real time is essential, as the workspace for these robots is not static and therefore cannot be precalculated. Once the free areas of C-space have been determined, more conventional path planning techniques which have been developed for point robots moving around forbidden areas can be used for the generation of a motion plan. This means that motion planning can be guaranteed to have an exhaustive search through all possible paths and return the lowest cost path - something that has previously been difficult to achieve with other manipulator motion planning techniques. The technique is demonstrated for a 2-degree of freedom planar manipulator, and will be extended in the future to apply to higher dimensional C-spaces. Further developments are suggested for further optimisation of the technique, most notably the idea of partitioning the C-space.

This paper presents the estimation of the position of the ground plane for navigation of on-road or off-road vehicles, in particular for obstacle detection using stereo vision. Ground plane estimation plays an important role in stereo vision based obstacle detection tasks. V-disparity image is widely used for ground plane estimation. However, it heavily relies on distinct road features which may not exist. In here, we introduce a global correlation method to extract the position of the ground plane in V-disparity image even without distinct road features.

This paper describes a new approach to align the laser scans in order to build a consistent map of the environment of a mobile robot. There are no physical landmarks and the environment is completely unknown. Our method finds the nearest matching lines in the scans and resolves the data correspondence problem. The global consistency and final map is achieved by the formulation of geometrical constraints and solving them using conjugate gradient method. The method can handle the multiple loop closing problem. This is easy to implement and fully integrated with a complete navigational architecture. The proposed algorithm is implemented using C++ programming libraries and tested in a real-time robotic simulation environment.

This work demonstrates how to carry out contact operations in a very stiff contact situation. The experimental set up used is a CNC machine that carries out metals spinning. The controlled forces are the metal forming forces. The designed controller operates in a dynamic environment. The complete control system operates in a manner similar to reaction compensation. The force controller used can be viewed as an integrating on-off controller which has tightly controlled release of control efforts to the controllers. It brings in artificial damping that ensures the stability of the control system. The designed force controller operates within limited tool path disturbances. The controller requires the feed forward of a nominal tool path. The accuracy of the nominal tool path brings the force deviations to within a manageable level. A secondary contribution of this work is the design of a generic external control loop for commercial CNC machines that enables the sensor integration for visual servoing or active force control

A number of methods have been developed for finding collision free paths for a manipulator moving from one configuration to another. In each of these cases the desired goal configuration must be chosen beforehand. However, in most applications there may be more than one configuration that satisfies the desired position and orientation requirements of the end effector. One of these configurations must be chosen for the path planner; generally we want to select the configuration that results in the lowest cost movement from the manipulator's current position. Until the motion plan is calculated, we cannot know which goal configuration to choose. This paper demonstrates a method for selecting the lowest cost path without needing to calculate a motion plan from the start position to all of the possible goal configurations. Generating motion plans is computationally expensive and it is desirable to limit these computations where ever possible. This is especially true in the case of mobile manipulators where the surrounding terrain for the motion planning calculation changes at each step, meaning that computational savings of reusing calculations from previous runs cannot be realised. The method demonstrated in this paper is shown to be much more suited to these types of applications than more traditional motion planning methods

We present a new approach for reducing the computational complexity of grid-based localization methods by suggesting the development of a parametric model of the environment of a mobile robot. This model is based on the sensor perception and obtained by applying the nonparametric bootstrapping technique. Particle filter based localization methods assume that map is available but are computationally very expensive mainly due to the measurement models. This method reduces the computational complexity of these methods by extracting a parametric model from the given map to be used for measurement update. We assign multinomial probabilities to the grid cells for future inferences. Having the cell probabilities, we can update the particles obtained from action model. The likelihood calculations become straight-forward and hence complex measurement models can be simplified. Our method can exploit any feature extraction algorithm to get the multinomial approximation of the environment. The offline extraction of few vital geometrical feature is carried out at some known locations on the map. This is then followed by the statistical technique to get the model parameters and essential cell statistics. The extracted model is used for measurement updates using a simulated pioneer robot and results show a significant increase in the update rate of the particle filter

Conventional correlation based stereo vision algorithms have poor performance at object borders due to occlusion. This paper analyzes image characteristics at depth discontinuities to improve the performance. It uses a new window scheme, especially to handle occlusion. The results show dramatically improved performance at depth discontinuities

The key issue of stereo vision algorithm using dynamic programming approach is to establish the cost function. Normally the cost of a match is the matching difference or matching error. This is almost the same for different algorithms in this approach. The only difference is in the cost of occlusion. Actually the cost of a known occluded point should be zero, because it is a good match. The difficulty is how to find the occluded region. In this paper, we present a new algorithm in which we first find the occluded region in the image using correlation approach. Then we establish a cost function in which the cost of the occluded region is set to zero. Then we use dynamic programming approach to minimize the energy of this cost function. Experimental results show that this algorithm can achieve excellent results with acceptable time efficiency

This paper presents the hardware and software architectures of a compact agricultural tractor, that is currently being developed as a generic mobile platform for agricultural tasks. The specific task that is being addressed is fruit picking. Such systems require precision outdoor maneuvering and coordination with the implement attached to the vehicle. The system described also includes a loader attached to the front of the tractor which carries the fruit picking robot. First the hardware associated with the safety subsystem, steering, traction and loader control systems are described. Then the real-time software that coordinates the control of all subsystems while maintaining a functional radio link to a remote station is described.

As a prelude to using stereo vision to accurately locate apples in an orchard, this paper presents a vision based algorithm to locate apples in a single image. On-tree situations of contrasting red and green apples as well as green apples in the orchard with poor contrast have been considered. The study found out that the redness in both cases of red and green apples can be used to differentiate apples from the rest of the orchard. Texture based edge detection has been combined with redness measures, and area thresholding followed by circle fitting, to determine the location of apples in the image plane. In the case of severely cluttered environments, Laplacian filters have been used to further clutter the foliage arrays by edge enhancement so that texture differences between the foliage and the apples increased thereby facilitating the separation of apples from the foliage. Results are presented that show the recognition of red and green apples in a number of situations as well as apples that are clustered together and/or occluded.

This paper presents a curvature based hand shape recognition method using images of a hand. The curvatures extracted from hand shape contours are converted to a non-dimensional quantity. Combining the contour geometry with the non-dimensional quantities, signatures are generated for template hand shapes. These template signatures are used to recognize hand shapes embedded in newly acquired image contours. The method provides a means of identifying a hand shape in a position and orientation independent manner. Nevertheless, the position and orientation of the hand gesture can still be obtained. The paper then presents a way to use hand shapes in an efficient virtual interface for a wheelchair. The presented system uses only four ergonomically chosen hand shapes to effect command inputs. The wheelchair operates in two different modes: the manual mode and the autonomous mode. The hand shapes used are arranged in a hierarchy taking into account the safety, ergonomics and reliability of operation. Results are presented to show the behavior of the wheelchair in both modes.

This paper presents a user interface to assist the occupant of a wheelchair. It uses vision to recognize the gestures formed against a back-lit surface. Gesture recognition algorithm uses a combination of gesture contour geometry and a non-dimensional quantity derived using the contour curvatures. The gestures are identified in a position and orientation independent manner. The developed gesture recognition system is implemented on a wheelchair. Two different modes of operations, namely, the manual mode and the autonomous mode are provided for the user. In manual mode the user continuously interacts with the wheelchair. In autonomous mode the user action is limited to choosing a desired destination by pointing to a zone of a known map displayed on the back-lit surface. Results are presented to show the performance of the wheelchair in both modes.

Addresses the problem of human robot interaction with application to the design of assistive devices. We describe the design and development of a prototype of a smart wheelchair that can be commanded by a rider. Specifically, we focus on (a) the vision-based human interaction interface; (b) the suite of sensors on the chair, and (c) the software architecture and the control algorithms used to control the chair.

This paper presents a general approach for the identification of contact and grasping uncertainties, and the monitoring of contact situation changes in force controlled assembly operations. The identification problem is solved using virtual contact manipulators and Kalman filter techniques. Monitoring is solved by carrying out a statistical test on the sum of normalized and squared innovations of the Kalman filter, within a moving window, identification and monitoring are verified by experimental results. The paper explains how the error covariance matrix of the Kalman filter is interpreted to analyse the observability of the Kalman filter's states. Preliminary simulation results are presented for an ad hoc active sensing strategy to achieve complete state observability.