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This paper presents a novel methodology to localise Unmanned Ground Vehicles (UGVs) using Unmanned Aerial Vehicles (UAVs). The UGVs are assumed to be operating in a Global Navigation Satellite System (GNSS)-denied environment. The localisation of the ground vehicles is achieved using UAVs that have full access to the GNSS. The UAVs use range sensors to localise the UGV. One of the major requirements is to use the minimum number of UAVs, which is two UAVs in this paper. Using only two UAVs leads to a significant complication that results an estimation unobservability under certain circumstances. As a solution to the unobservability problem, the main contribution of this paper is to present a methodology to treat the unobservability problem. A Constrained Extended Kalman Filter (CEKF)-based solution, which uses novel kinematics and heuristics-based constraints, is presented. The proposed methodology has been assessed based on the stochastic observability using the Posterior Cramér–Rao Bound (PCRB), and the results demonstrate the successful operation of the proposed localisation method.

As people further develop space with advanced technology, space robots have played a significant role in on-orbit servicing missions. Space robots can carry out more risky and complicated missions with less cost than astronauts. Dual-arm space robots can perform complex on-orbit space missions more effectively than single-arm space robots. Since the coupled dynamics between the free-floating base and the arms exist in space robots, accurate coordinate control of the base and the arms is essential. Spacecraft release missions have been proposed to berth/deberth a spacecraft to a space station. Based on the existing release missions, a tangent release strategy is introduced in this paper, which can release a space object in the tangent direction of the final link of a space manipulator. This strategy can control a dual-arm space robot to deploy cargo/spacecraft in variable directions in 3D space without thrusters and the associated fuel consumption. For instance, this tangent release operation can transport cargo or modules of large-scale spacecraft needing on-orbit assembly. Considering model uncertainties, robust controllers again model uncertainties that are used to control the dual-arm space robot with high accuracy. Hence, a robust sliding mode controller (SMC) is utilized to accurately control the space robot to carry out the proposed tangent release strategy. For comparison, we select a conventional computed torque control (CTC) implemented by a PD-type controller. In the simulations, the SMC performs better in tracking accuracy and robustness against the model uncertainties than the PD controller. Numerical simulations indicate the feasibility and effectiveness of the tangent release manipulation of a space object by a dual-arm space robot.

Stiffness-adjustable snake-like robots have been proposed for various applications, including minimally invasive surgery. Based on a variable neutral-line mechanism, previous works proposed a class of snake-like robots that can adjust their stiffness by changing the driving cables’ tensions. A constant curvature hypothesis was used to formulate such robots’ kinematics and was further verified by our previous work via rigorous force analysis and ADAMS simulations. However, all these models and analyses have ignored the effect of the robot links’ gravity, resulting in significant errors in real systems. In this paper, a static model considering gravity compensation is proposed for the stiffness-adjustable snake-like robots. The proposed model adopts a nonlinear Gauss–Seidel iteration scheme and consists of two parts: gravity update and pose estimation. In each iteration, the former updates the payload of each link caused by gravity, and the latter estimates the pose of the robot by refreshing the angle and position values. This iteration stops when the change in the tip position is less than a pre-set error ϵ. During the above process, the only dependent information is each cable’s tension. Simulations and experiments are carried out to verify the effectiveness of the proposed model. The impact of gravity is found to increase with growing material densities in the simulations. The experimental results further indicate that compared with a model without gravity compensation, our model reduces the tip estimation error by 91.5% on average.

Komatsu Ltd. is reinforcing the research and development of automation technology for our bulldozers. Based on this trend, a methodology for optimized distribution of materials is proposed and developed for dumping area operations using bulldozers in this article. The aim of this work is to develop a robust methodology to contribute to the development of an autonomous system as possible so that it can be applied to our commercial machines immediately. The identified problem is that no formulation has yet been produced which can be applied to achieve such objectives. To develop the methodology, firstly, problem formulation of a specific scenario at the dumping area is presented. Secondly, integer linear programming is applied to solve the distribution problem. A hybrid method combining heuristics techniques and local branching is utilized to make this methodology capable of providing fast and reasonable solutions. Lastly, simulation results with specific different distribution requirements are presented to show that the developed methodology can derive optimized solutions for different requirements with a stable computational performance.

This article proposes a novel approach for the two-wheel-steer four-wheel-drive vehicle path tracking based on force control. Both the kinematic and dynamic models are used to calculate the virtual inputs based on a model predictive control algorithm. By using these virtual inputs as the constraints of a real-time sequential quadratic programming optimization, the optimal real inputs such as the drive forces and the steering angles are obtained. Note that the system is discretized when designing the control algorithm. The novelty is that all the forces are taken into account in the dynamic model, and the controller uses optimal steering angles and drive forces to obtain the minimum path offset. The integrated algorithms are then used in tracking a predefined path. Simulation results are provided to demonstrate the effectiveness and applicability of the proposed approach.

This paper presents the implementation of an external axis control system on a conventional CNC machine so that the machine can be actively controlled in response to sensors such as vision and force. The controller that runs on an external computer has direct access to the CNC controller for machine position sensing. The control signals to the machine are sent through purpose built circuitry via the machine's manual pulse generator (MPG) inputs. To demonstrate the accuracy and performance of the control system, it was used to visually track the profile of a mandrel used for shear spinning. The implemented system eliminates the parallax error and the need to use an accurate pixel resolution. The raw tracking data is processed by a curvature detection algorithm that detects linear and circular segments and segment transitions. The results show that the visual tracking system provides accurate tracking results that are well within the tolerances used in the industry.