Linkage fabrics are gaining in popularity and finding applications in architecture, aerospace, healthcare, and fashion because they can deliver materials with bespoke flexibility and strength through the geometric design of linkage nodes. In this article, we provide a perspective on linkage fabrics as a new class of programmable materials. We describe the theory and design principles of these linkage fabrics and show how they can be designed and simulated using digital tools, and fabricated using 3D printing. This digital approach overcomes a major obstacle to the adoption of these materials, namely their complexity. We show how simulation methods can be verified and calibrated through experimental testing. This perspective article also discusses design-led research challenges for linkage fabrics such as the development of wearable assistive devices for those with physical disabilities.

As no internationally agreed-upon method for determining safe speed values currently exists, collecting vast amounts of information on conventional ship behaviour could be used to train autonomous ship intelligence in determining safe speeds in different conditions. This requires speed data collected from conventional ships to resemble what can be described as safe speeds. To test this, the Automatic Identification System (AIS) and environmental data – namely visibility, mean wind speed and significant wave height – were collected and merged for two study areas in Norway in the period between 27 March 2014 and 1 January 2021. Regression analyses based on 47,490 unique vessel transits were conducted and supplemented by two graphical methods for revealing relationships between variables. Contrary to the contemporary understanding of safe speed, reduced visibility did not lead to significantly reduced transit speeds. Wind and waves caused a reduction in speed in the open ocean, but not in coastal waters. Transit speeds were lower in coastal waters than in the open ocean.

Several studies have been conducted on collision avoidance (CA) and path planning for maritime autonomous surface ships (MASS) based on artificial potential field (APF) and electronic navigation chart (ENC) data. However, to date, accurate, highly efficient, and automatic modelling of complicated geometry environment potential fields (EPFs) has not been realised. In this study, an accurate EPF model is established using ENC data to describe different types of obstacles, navigable areas, and non-navigable areas. The implicit equations of complex polygons are constructed based on the R-function theory, and the discrete-convex hull method is introduced to realise the automatic modelling of EPF. Moreover, collaborative CA and obstacle avoidance (OA) experiments are designed and conducted in a simulated environment and based on the ENC data. The results show that the proposed EPF modelling method is accurate, reliable, and time-efficient even with numerous ENC data and complex shapes owing to the R-function representation for geometric objects and discrete-convex hull method. The combination of improved APF and EPF models is proven to be effective for CA and OA. This paper presents a practical EPF modelling approach for APF-based ship path planning.

Collision avoidance (COLAV) is a prerequisite for the navigation safety of unmanned surface vehicles (USVs). Since USVs have to avoid obstacles clearly and timely, i.e. the COLAV should be agile, the COLAV algorithm should have low computation complexity and make efficient COLAV decisions. However, balancing between the computation complexity and the COLAV decision optimality is still intractable at present. This paper innovatively proposes a COLAV algorithm for USVs by combining the velocity obstacle method with the predictive model method, named the collision shielded model predictive control (CS-MPC) algorithm, such that the agility of USVs COLAV is improved. The runtime of the proposed COLAV algorithm is shortened by shielding the dangerous parts of the search space of the COLAV decisions, and the COLAV decision is efficient with the aid of the accurately predicted motion trajectory by the motion mathematical model of USVs. As such, the USV can safely navigate in complex water areas where multiple vessels and obstacles exist. A series of simulations on a yacht in different kinds of encounter situations were carried out to verify the effectiveness and the agility of the proposed CS-MPC COLAV algorithm.

Autonomous driving will not just change vehicles themselves, but also the entire concept of mobility. New business models and the expansion of individual mobility to new groups of society are merely examples of possible impact. In order to create optimal vehicles for new technologies right from the start, vehicle concept optimization helps to find suitable solutions from numerous possible variations. The package as part of a vehicle concept is currently focused on passenger cars with steering wheels and pedals. Therefore, a new method is needed to plan the package of driverless and autonomous vehicles. In this paper, we present a possible method that separates the vehicle into the interior and the front and rear wagon. This way, different seating layouts can be considered and evaluated in terms of package efficiency. In the results, we check the plausibility by rebuilding a current battery electric vehicle (BEV) and, by way of example, show the variation of the gear angle and different seating layouts, and the resulting package efficiency.

Agricultural weeds remain an important production constraint, with labor shortages and a lack of new herbicide options in recent decades making the problem even more acute. Robotic weeding machines are a possible solution to these increasingly intractable weed problems. Franklin Robotics’ Tertill™ is an autonomous weeding robot designed for home gardeners that relies on a minimalistic design to be cost-effective. The objectives of this study were to investigate the ability of the Tertill to control broadleaf and grass weeds, and based on early observations, experiments were conducted with and without its string-trimmer–like weeding implement. Tertill demonstrated high weed-control efficacy, supporting its utility as a tool for home gardeners. Weeds were best controlled by the combined effect of soil disturbance caused by the action of the robot’s wheels and the actuation of the string trimmer. Despite the regrowth potential of an annual grass due to its meristem location, Tertill maintained low densities of millet in an experimental arena. The simple and effective design of the Tertill may offer insights to inform future development of farm-scale weeding robots. Weed density, emergence periodicity, robot working rate, and robotic weeding mechanisms are important design criteria regardless of the technology used for plant detection.

This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

This article tackles the tricky legal issues associated with autonomy and automation in attack. Having clarified the meanings of these notions, the implications of the rules of weapons law for such technologies are assessed. More challenging issues seem, however, to be raised by the law of targeting, and in particular by the evaluative assessments that are required of attackers, for example in relation to the precautions in attack prescribed by Additional Protocol I. How these rules can sensibly be applied when machines are undertaking such decision-making is therefore addressed. Human Rights Watch has called for a comprehensive ban on autonomous attack technologies and the appropriateness of such a proposal at the present stage of technological development is therefore assessed. The article then seeks to draw conclusions.

The increasing complexity of weapon systems requires an interdisciplinary approach to the conduct of weapon reviews. Developers need to be aware of international humanitarian law principles that apply to the employment of weapons. Lawyers need to be aware of how a weapon will be operationally employed and use this knowledge to help formulate meaningful operational guidelines in light of any technological issues identified in relation to international humanitarian law. As the details of a weapon's capability are often highly classified and compartmentalized, lawyers, engineers, and operators need to work cooperatively and imaginatively to overcome security classification and compartmental access limitations.

Topics relevant to modeling and control of mobile wheeled robots with a differential drive are discussed by assuming a motion that is planar and free from lateral and longitudinal slippages, and by taking into account dynamic and kinematic properties of the vehicle. Based on the concept of geometric path-tracking, a controller is designed that is a memoryless function of the lateral, heading, and velocity path-tracking offsets. If these offsets are kept small and the assigned tracking velocity is constant, then this controller may be given a linear, time-invariant and decoupled PID (Proportional + integral + derivative) structure.

In future real-time systems such as those required for intelligent autonomous vehicle control, we need flexibility in choosing the set of services to support under varying environmental conditions and system states. It is not feasible to make an optimal choice of services at run-time, so we propose a method of ranking the services pre-run-time, based on the ‘utility' of each service. This paper focuses on the problem of calculating a ‘value' for the utility of each service alternative. We show how to derive values systematically and rationally, using Measurement Theory and Decision Analysis. The approach relies on engineering judgement and data input by a domain expert. In the context of autonomous vehicles, we believe that such knowledge would be available, making ‘value-based scheduling' a feasible approach.