Yue Xie’s research while affiliated with University of Cambridge and other places

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this paper, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multi-objective mixed-integer program. To solve the optimization problem, we develop a hybrid heuristic approach based on Non-Dominated Sorting Genetic Algorithm II and branch-and-bound algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors.

Embodied intelligence reflects the ability of an agent to interact with its environment through its body to gain intelligence and autonomy. Such a concept has become popular in recent years for the design of robotic systems. This paper generalises the embodied intelligence concept to Additive Manufacturing (AM) to provide a new understanding of the technology with inspirations from biological production processes including organism growth and animal construction. Morphology-based embodied intelligence is analysed on the design and manufacturing aspects for delivering intelligent products (including robots) while embodied artificial intelligence (AI) (foundation models) is discussed for the printer itself. The paper opens a new perspective for AM research.

Soft robotics has emerged as the standard solution for grasping deformable objects, and has proven invaluable for mobile robotic exploration in extreme environments. However, despite this growth, there are no widely adopted computational design tools that produce quality, manufacturable designs. To advance beyond the diminishing returns of heuristic bio-inspiration, the field needs efficient tools to explore the complex, non-linear design spaces present in soft robotics, and find novel high-performing designs. In this work, we investigate a hierarchical design optimization methodology which combines the strengths of topology optimization and quality diversity optimization to generate diverse and high-performance soft robots by evolving the design domain. The method embeds variably sized void regions within the design domain and evolves their size and position, to facilitating a richer exploration of the design space and find a diverse set of high-performing soft robots. We demonstrate its efficacy on both benchmark topology optimization problems and soft robotic design problems, and show the method enhances grasp performance when applied to soft grippers. Our method provides a new framework to design parts in complex design domains, both soft and rigid.

Topology optimization is a powerful design tool in structural engineering and other engineering problems. The design domain is discretized into elements, and a finite element method model is iteratively solved to find the element that maximizes the structure's performance. Although gradient-based solvers have been used to solve topology optimization problems, they may be susceptible to suboptimal solutions or difficulty obtaining feasible solutions, particularly in non-convex optimization problems. The presence of non-convexities can hinder convergence, leading to challenges in achieving the global optimum. With this in mind, we discuss in this paper the application of the quality diversity approach to topological optimization problems. Quality diversity (QD) algorithms have shown promise in the research field of optimization and have many applications in engineering design, robotics, and games. MAP-Elites is a popular QD algorithm used in robotics. In soft robotics, the MAP-Elites algorithm has been used to optimize the shape and control of soft robots, leading to the discovery of new and efficient motion strategies. This paper introduces an approach based on MAP-Elites to provide diverse designs for structural optimization problems. Three fundamental topology optimization problems are used for experimental testing, and the results demonstrate the ability of the proposed algorithm to generate diverse, high-performance designs for those problems. Furthermore, the proposed algorithm can be a valuable engineering design tool capable of creating novel and efficient designs.

Computational design can excite the full potential of soft robotics, but it has the drawback of being highly nonlinear in terms of material, structure, and contact. To date, enthusiastic research interests have been demonstrated for individual soft fingers, but the frame design space (how each soft finger is assembled) remains largely unexplored. Computational design remains challenging for the finger-based soft gripper to grip across multiple geometrically distinct object types successfully. Including the design space for the gripper frame can bring huge difficulties for conventional optimization algorithms and fitness calculation methods due to the exponential growth of design space. This work proposes an automated computational design optimization framework that generates gripper diversity to individually grasp geometrically distinct object types based on a quality-diversity approach. This work first discusses a significantly large design space (28 design parameters) for a finger-based soft gripper, including the rarely-explored design space of finger arrangement. Then, a contact-based Finite Element Modelling (FEM) is proposed in SOFA to output high-fidelity grasping data for fitness evaluation and feature measurements. Finally, diverse gripper designs are obtained from the framework while considering features such as the volume and workspace of grippers. This work bridges the gap of computationally exploring the vast design space of finger-based soft grippers while grasping large geometrically distinct object types with a simple control scheme.

Computational design is a critical tool to realize the full potential of Soft Robotics, maximizing their inherent benefits of high performance, flexibility, robustness, and safe interaction. Practically, computational design entails a rapid iterative search process over a parameterized design space, with assessment using (frequently) computational modeling and (more rarely) physical experimentation. Bayesian approaches work well for these expensive-to-analyze systems and can lead to efficient exploration of design space than comparative algorithms. However, such computational design typically entails weaknesses related to a lack of fidelity in assessment, a lack of sufficient iterations, and/or optimizing to a singular objective function. Our work directly addresses these shortcomings. First, we harness a sophisticated nonlinear Finite Element Modeling suite that explicitly considers geometry, material, and contact nonlinearity to perform rapid accurate characterization. We validate this through extensive physical testing using an automated test rig and printed robotic fingers, providing far more experimental data than that reported in the literature. Second, we explore a significantly larger design space than comparative approaches, with more free variables and more opportunity to discover novel, high performance designs. Finally, we use a multiobjective Bayesian optimizer that allows for the identification of promising trade-offs between two critical objectives, compliance and contact force. We test our framework on optimizing Fin Ray grippers, which are ubiquitous throughout research and industry due to their passive compliance and durability. Results demonstrate the benefits of our approach, allowing for the optimization and identification of promising gripper designs within an extensive design space, which are then 3D printed and usable in reality.

Soft grippers are ideal for grasping delicate, deformable objects with complex geometries. Universal soft grippers have proven effective for grasping common objects, however complex objects or environments require bespoke gripper designs. Multi‐material printing presents a vast design‐space which, when coupled with an expressive computational design algorithm, can produce numerous, novel, high‐performance soft grippers. Finding high‐performing designs in challenging design spaces requires tools that combine rapid iteration, simulation accuracy, and fine‐grained optimization across a range of gripper designs to maximize performance, no current tools meet all these criteria. Herein, a diversity‐based soft gripper design framework combining generative design and topology optimization (TO) are presented. Compositional pattern‐producing networks (CPPNs) seed a diverse set of initial material distributions for the fine‐grained TO. Focusing on vacuum‐driven multi‐material soft grippers, several grasping modes (e.g. pinching, scooping) emerging without explicit prompting are demonstrated. Extensive automated experimentation with printed multi‐material grippers confirms optimized candidates exceed the grasp strength of comparable commercial designs. Grip strength, durability, and robustness is evaluated across 15,170 grasps. The combination of fine‐grained generative design, diversity‐based design processes, high‐fidelity simulation, and automated experimental evaluation represents a new paradigm for bespoke soft gripper design which is generalizable across numerous design domains, tasks, and environments.