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"motion planning"
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Architectures of revolt : the cinematic city circa 1968
\"Architectures of Revolt explores the intertwined stories of cinema and the city in 1968, a year which witnessed political revolutions and a revolutionary cinematic engagement, both of which relied upon interacting with and using the city in new ways. Includes case studies from cities around the world\"-- Provided by publisher.
Book
dRRT: Scalable and informed asymptotically-optimal multi-robot motion planning
Many exciting robotic applications require multiple robots with many degrees of freedom, such as manipulators, to coordinate their motion in a shared workspace. Discovering high-quality paths in such scenarios can be achieved, in principle, by exploring the composite space of all robots. Sampling-based planners do so by building a roadmap or a tree data structure in the corresponding configuration space and can achieve asymptotic optimality. The hardness of motion planning, however, renders the explicit construction of such structures in the composite space of multiple robots impractical. This work proposes a scalable solution for such coupled multi-robot problems, which provides desirable path-quality guarantees and is also computationally efficient. In particular, the proposed dRRT∗ is an informed, asymptotically-optimal extension of a prior sampling-based multi-robot motion planner, dRRT. The prior approach introduced the idea of building roadmaps for each robot and implicitly searching the tensor product of these structures in the composite space. This work identifies the conditions for convergence to optimal paths in multi-robot problems, which the prior method was not achieving. Building on this analysis, dRRT is first properly adapted so as to achieve the theoretical guarantees and then further extended so as to make use of effective heuristics when searching the composite space of all robots. The case where the various robots share some degrees of freedom is also studied. Evaluation in simulation indicates that the new algorithm, dRRT∗ converges to high-quality paths quickly and scales to a higher number of robots where various alternatives fail. This work also demonstrates the planner’s capability to solve problems involving multiple real-world robotic arms.
Journal Article
The Barcelona reader : cultural readings of a city
\"Over the last twenty years there has been a growing international interest in the city of Barcelona. This has been reflected in the academic world through a series of studies, courses, seminars, and publications. The Barcelona Reader hinges together a selection of the best academic articles, written in English, about the city, and its main elements of identity and interest: art, urban planning, history and social movements. The book includes scholarly essays about Barcelona that can be of interest to the student and the general public alike. It focuses on cultural representations of the city: the arts (including literature) provide a complex yet discontinuous portrait of the city, similar to a patchwork. The authors selected create a kaleidoscope of views and voices thus presenting a diverse yet inclusive Barcelona portrait. The Barcelona Reader offers a multifaceted assessment that will be essential reading for anyone interested in this iconic city\"-- Back cover.
Book
Survey of UAV motion planning
Motion planning is a vital module for unmanned aerial vehicles (UAVs), especially in scenarios of autonomous navigation and operation. This survey delivers some recent state‐of‐the‐art UAV motion planning algorithms and related applications. The logic flow of this survey is divided as the path finding, which is the front‐end of most motion planning systems, and the trajectory optimisation, which usually serves as the back‐end. Motivation, methodology, problem formulation and derivation are given in this survey, in detail. Finally, a section about real‐world applications is given, where roles and effectiveness of most popular motion planning methods are revealed.
Journal Article
Large language models for chemistry robotics
This paper proposes an approach to automate chemistry experiments using robots by translating natural language instructions into robot-executable plans, using large language models together with task and motion planning. Adding natural language interfaces to autonomous chemistry experiment systems lowers the barrier to using complicated robotics systems and increases utility for non-expert users, but translating natural language experiment descriptions from users into low-level robotics languages is nontrivial. Furthermore, while recent advances have used large language models to generate task plans, reliably executing those plans in the real world by an embodied agent remains challenging. To enable autonomous chemistry experiments and alleviate the workload of chemists, robots must interpret natural language commands, perceive the workspace, autonomously plan multi-step actions and motions, consider safety precautions, and interact with various laboratory equipment. Our approach, CLAIRify, combines automatic iterative prompting with program verification to ensure syntactically valid programs in a data-scarce domain-specific language that incorporates environmental constraints. The generated plan is executed through solving a constrained task and motion planning problem using PDDLStream solvers to prevent spillages of liquids as well as collisions in chemistry labs. We demonstrate the effectiveness of our approach in planning chemistry experiments, with plans successfully executed on a real robot using a repertoire of robot skills and lab tools. Specifically, we showcase the utility of our framework in pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization. Further details about CLAIRify can be found at https://ac-rad.github.io/clairify/.
Journal Article
Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope
Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.
Journal Article
An Overview of Motion-Planning Algorithms for Autonomous Ground Vehicles with Various Applications
With the rapid development and the growing deployment of autonomous ground
vehicles (AGVs) worldwide, there is an increasing need to design reliable,
efficient, robust, and scalable motion-planning algorithms. These algorithms are
crucial for fulfilling the desired goals of safety, comfort, efficiency, and
accessibility. To design optimal motion-planning algorithms, it is beneficial to
explore existing techniques and make improvements by addressing the limitations
of associated techniques, utilizing hybrid algorithms, or developing novel
strategies. This article categorizes and overviews numerous motion-planning
algorithms for AGVs, shedding light on their strengths and weaknesses for a
comprehensive understanding. For various applications of AGVs, such as urban and
off-road autonomous driving, the features of driving conditions and vehicle
kinodynamics are outlined, and sample-tailored motion-planning algorithms built
upon relevant canonical techniques are briefly introduced. As a result of the
overview, future research efforts on motion-planning techniques are identified
and discussed.
Journal Article
Comparative Benchmark of Sampling-Based and DRL Motion Planning Methods for Industrial Robotic Arms
This study presents a comprehensive comparison between classical sampling-based motion planners from the Open Motion Planning Library (OMPL) and a learning-based planner based on Soft Actor–Critic (SAC) for motion planning in industrial robotic arms. Using a UR3e robot equipped with an RG2 gripper, we constructed a large-scale dataset of over 100,000 collision-free trajectories generated with MoveIt-integrated OMPL planners. These trajectories were used to train a DRL agent via curriculum learning and expert demonstrations. Both approaches were evaluated on key metrics such as planning time, success rate, and trajectory smoothness. Results show that the DRL-based planner achieves higher success rates and significantly lower planning times, producing more compact and deterministic trajectories. Time-optimal parameterization using TOPPRA ensured the dynamic feasibility of all trajectories. While classical planners retain advantages in zero-shot adaptability and environmental generality, our findings highlight the potential of DRL for real-time and high-throughput motion planning in industrial contexts. This work provides practical insights into the trade-offs between traditional and learning-based planning paradigms, paving the way for hybrid architectures that combine their strengths.
Journal Article
Benchmarking and optimization of robot motion planning with motion planning pipeline
Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through motion planning pipeline and planning request adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes.
Journal Article
Optimal motion planning for overhead cranes
Overhead cranes are widely used in industrial applications for material displacing. Many linear or non-linear control schemes have been proposed for overhead cranes and implemented on electronic systems, but energy efficiency of transportation has seldom been considered in motion planning. This study aims at finding an optimal solution of motion planning in terms of energy efficiency for overhead cranes. Using the optimal control method an optimal trajectory is obtained with less energy consumption than the compared trajectories and is also satisfying physical and practical constraints such as swing, acceleration and jerk. Besides the energy optimal model, the authors also propose two other models to optimise time efficiency and safety during transportation. The results obtained have been compared with some existing motion trajectories, and have been shown to be superior to these benchmarks in terms of energy efficiency, time efficiency and safety, respectively.
Journal Article