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Three-Dimensional Mountain Complex Terrain and Heterogeneous Multi-UAV Cooperative Combat Mission Planning

IEEE ACCESS 2020年 8卷 197407-197419页

作者： Liu, Haishi Chen, Qiyong Pan, Nan Sun, Yuxuan Yang, Yaluo Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Mat Sci & Engn Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Yunnan Police Officer Acad Fac Publ Secur Kunming 650223 Yunnan Peoples R China

Most research regarding multi-target, multi-base, and multi-unmanned aerial vehicle (UAV) coordinated combat mission planning faces the problems of ignoring heterogeneous UAVs, as well as poor task allocation and trajectory planning coupling. To solve these problems, based on air maneuver combat mission backgrounds, the present paper provided a heterogeneous multi-UAV cooperative mission planning method in the complex three-dimensional (3D) mountain environment. In the present paper, based on the Life-cycle Swarm Optimization (LSO) algorithm, varying the number of individuals in the population was utilized to improve the algorithm and further combined with the rapidly exploring random tree (RRT) algorithm to obtain an optimized path. Then, an improved algorithm was utilized for task allocation and trajectory optimization, and the number and speed of drones dispatched by each base were determined regarding time coordination. Finally, a simulation experiment was conducted. Numerical simulation results showed that the following algorithm was compared with the Particle Swarm Optimization (PSO) algorithm and the Whale Optimization Algorithm (WOA) when considering radar threats and solid obstacle areas. This has good approximation and high convergence accuracy, and it was effectively utilized in the planning of UAV collaborative missions in 3D complex terrain environments.

关键词： Planning Task analysis Particle swarm optimization Trajectory planning Optimization Drones Collaborative work particle swarm optimization artificial intelligence heterogeneous multi-UAV life-cycle swarm optimization algorithm rapidly exploring random tree

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Increasing Diversity of Solutions in Sampling-based Path Planning 18

Increasing Diversity of Solutions in Sampling-based Path Pla...

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4th International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Vonasek, Vojtech Saska, Martin Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic

ISBN: (纸本)9781450365840

Sampling-based path planning algorithms like rapidly-exploring random trees (RRT) are widely used to solve path planning problems for robots with many Degrees Of Freedom (DOF). Although the sampling-based planners are stochastic and provide different solutions in every trial, many resulting trajectories can be quite similar, e.g., because they are close to each other. Computation of di verse paths, i.e., paths leading through different parts of the configuration space, is important for online navigation of mobile robots or for finding solutions leading through different narrow passages of the configuration space. In this paper, we propose an extension of the RRT algorithm to find diverse paths in the configuration space iteratively. The idea of the proposed method is to prohibit the exploration of selected regions of the configuration space. The prohibited regions are defined using the waypoints of paths computed in the previous iterations. We show that the proposed method finds more diverse trajectories than can be achieved by repeated computations of a single sampling-based planner.

关键词： Path planning rapidly exploring random tree

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tree-based search of the next best view/state for three-dimensional object reconstruction

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第1期15卷

作者： Irving Vasquez-Gomez, J. Enrique Sucar, L. Murrieta-Cid, Rafael Herrera-Lozada, Juan-Carlos Inst Politecn Nacl Consejo Nacl Ciencia & Tecnol Ctr Innovac & Desarrollo Tecnol Comp Mexico City DF Mexico Inst Nacl Astrofis Opt & Electr Puebla Mexico Ctr Invest Matemat Guanajuato Mexico Inst Politecn Nacl Ctr Innovac & Desarrollo Tecnol Comp Mexico City DF Mexico

Three-dimensional models from real objects have many applications in robotics. To automatically build a three-dimensional model from an object, it is essential to determine where to place the range sensor in order to completely observe the object. However, the view (position and orientation) of the sensor is not sufficient, given that its corresponding robot state needs to be calculated. Additionally, a collision-free trajectory to reach that state is required. In this article, we directly find the state of the robot whose corresponding sensor view observes the object. This method does not require to calculate the inverse kinematics of the robot. Unlike previous approaches, the proposed method guides the search with a tree structure based on a rapidly exploring random tree overcoming previous sampling techniques. In addition, we propose an information metric that improves the reconstruction performance of previous information metrics.

关键词： Next best view path planning 3-D reconstruction rapidly exploring random tree 3-D mapping

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randomized path planning for tasks requiring the release and regrasp of objects

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ADVANCED ROBOTICS 2016年第4期30卷 270-283页

作者： Kim, Jinkyu Ko, Inyoung Park, Frank C. Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 151742 South Korea Samsung Elect Co Ltd Global Technol Ctr Suwon 443742 South Korea

This paper presents a randomized planning algorithm for manipulation tasks that require the robot to release and regrasp an object in different robot postures. Such problems arise, for example, in robotic suturing and knot tying, and in assembly tasks where parts must be guided through complex environments. Formulating the problem as one of planning on a foliated manifold, we present a randomized planning algorithm that, unlike existing methods, involves sampling and tree propagation primarily in the task space manifold;such an approach significantly improves computational efficiency by reducing the number of projections to the constraint manifold, without incurring any significant increases in the number of release-regrasp sequences. We also propose a post-processing topological exploration algorithm and path refinement procedure for reducing the number of release-regrasp sequences in a solution path, independent of the algorithm used to generate the path. Experiments involving spatial open chains with up to 10 degrees of freedom, operating in complex obstacle-filled environments, show that our algorithm considerably outperforms existing algorithms in terms of computation time, path length, and the number of release-regrasp operations.

关键词： Motion planning sampling-based planning rapidly exploring random tree release-regrasp foliation

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Anytime Dynamic exploring Rapid random tree Approach in Higher Dimension Search Space for Non-Holonomic Robotics

Anytime Dynamic Exploring Rapid Random Tree Approach in High...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Luo, Ren C. Huang, Charly Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781509051021

With the breakthrough of exploring Rapid random tree and several other improvement efforts, the sampling-based motion planning method has been gaining ground in 2D planning and gradually being accepted by many systems as their global planning algorithm. There are many recent approaches on integrating the incremental nature and algorithmic simplicity of sampling-based approach with the agile replanning strategy studied on Incremental Heuristic based motion planning algorithms. Therefore, we develop an anytime dynamic RRT* for non-holonomic systems. We also implement Lyapunov function, which better represents the true-cost-to-go, in order to generate ideal and smoother trajectories. Our method proved to be more robust than some state-of-the-art planning algorithms, with lower cost and smoother path. We evaluate our algorithm with 3 major benchmarks in simulated as well as real-environment. We compared our algorithm with other major planning approaches and proved the cost yields between 8.5%similar to 16.7% less cost and as may reach as low as 58.17% and 95% less than RRT and RRT* respectively.

关键词： Motion Planning optimal path planning sampling-based algorithms rapidly exploring random tree Anytime Dynamic Approach

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FPGA based Combinatorial Architecture for Parallelizing RRT

FPGA based Combinatorial Architecture for Parallelizing RRT

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European Conference on Mobile Robots

作者： Malik, Gurshaant Singh Gupta, Krishna Krishna, K. Madhava Chowdhury, Shubhajit Roy

ISBN: (纸本)9781467391634

Complex tasks are often handled through software implementation in combination with high performance processors. Taking advantage of hardware parallelism, FPGA is breaking the paradigm by accomplishing more per clock cycle with closely matched application requirements. With the aim to minimise computation delay with increase in map's size and geometric constraints, we present the FPGA based combinatorial architecture that allows multiple RRTs to work together to achieve accelerated, uniform exploration of the map. We also analyse our architecture against hardware implementation of other scalable RRT methods for motion planning. We observe notable furtherance of acceleration capabilities with the proposed architecture delivering a minimum 3X gain over the other implementations while maintaining uniformity in exploration.

关键词： parallel processing path planning random processes trees (mathematics) FPGA combinatorial architecture geometric constraints hardware parallelism high performance processors motion planning parallelizing RRT rapidly exploring random tree software implementation Acceleration Collision avoidance Computer architecture Field programmable gate arrays Hardware Robots Space exploration Field programmable gate arrays software implementation geometric constraint motion planning Space Exploration Computer Architecture Computer hardware Collision avoidance Stochastic Processes Path planning PARALLEL PROCESSING (COMPUTERS) Robot acceleration HIGH-PERFORMANCE Building construction

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Guided Motion Planning for Modular Robots 1

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1st International Workshop on Modelling and Simulation for Autonomous Systems (MESAS)

作者： Vonasek, Vojtech Penc, Ondrej Preucil, Libor Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic Fac Nucl Sci & Phys Eng Brehova 7 Prague 11519 Czech Republic

ISBN: (纸本)9783319138237;9783319138220

Modular robots consist of many modules that can be connected into various structures. This allows modular robots to adapt their shape according to a given task and environment. To visit a desired place in an environment, motion planning is required. To generate feasible plans for these robots with many degrees of freedom and many kinematic and dynamic constraints, a physical simulation is required to precisely model motion of the robots. As the physical simulation can be time consuming, motion planners need to be extended to decrease the number of simulation queries. In this paper, we propose a novel motion planning for modular robots based on guided sampling of configuration space. The guided sampling utilizes a precomputed simple path in the environment, along which the configuration space of the robot is sampled with higher probability. This approach significantly decreases the number of calls of physical simulations which increases the speed of the planning process.

关键词： motion planning physical simulation modular robots rapidly exploring random tree

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Interactive Motion Planning for Steerable Needles in 3D Environments with Obstacles

Interactive Motion Planning for Steerable Needles in 3D Envi...

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3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

作者： Patil, Sachin Alterovitz, Ron Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA

ISBN: (纸本)9781424477098

Bevel-tip steerable needles for minimally invasive medical procedures can be used to reach clinical targets that are behind sensitive or impenetrable areas and are inaccessible to straight, rigid needles. We present a fast algorithm that can compute motion plans for steerable needles to reach targets in complex, 3D environments with obstacles at interactive rates. The fast computation makes this method suitable for online control of the steerable needle based on 3D imaging feedback and allows physicians to interactively edit the planning environment in real-time by adding obstacle definitions as they are discovered or become relevant. We achieve this fast performance by using a rapidly exploring random tree (RRT) combined with a reachability-guided sampling heuristic to alleviate the sensitivity of the RRT planner to the choice of the distance metric. We also relax the constraint of constant-curvature needle trajectories by relying on duty-cycling to realize bounded-curvature needle trajectories. These characteristics enable us to achieve orders of magnitude speed-up compared to previous approaches;we compute steerable needle motion plans in under 1 second for challenging environments containing complex, polyhedral obstacles and narrow passages.

关键词： 3D environments 3D imaging feedback bevel-tip steerable needles complex polyhedral obstacles constant-curvature needle trajectories interactive motion planning medical control systems motion control needles online control position control rapidly exploring random tree reachability-guided sampling heuristics

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