31,788 research outputs found
Lightweight Neural Path Planning
Learning-based path planning is becoming a promising robot navigation
methodology due to its adaptability to various environments. However, the
expensive computing and storage associated with networks impose significant
challenges for their deployment on low-cost robots. Motivated by this practical
challenge, we develop a lightweight neural path planning architecture with a
dual input network and a hybrid sampler for resource-constrained robotic
systems. Our architecture is designed with efficient task feature extraction
and fusion modules to translate the given planning instance into a guidance
map. The hybrid sampler is then applied to restrict the planning within the
prospective regions indicated by the guide map. To enable the network training,
we further construct a publicly available dataset with various successful
planning instances. Numerical simulations and physical experiments demonstrate
that, compared with baseline approaches, our approach has nearly an order of
magnitude fewer model size and five times lower computational while achieving
promising performance. Besides, our approach can also accelerate the planning
convergence process with fewer planning iterations compared to sample-based
methods.Comment: 8 page
A hybrid control architecture for autonomous mobile robot navigation in unknown dynamic environment
This paper introduces a new hybrid control architecture for solving the navigation problem of mobile robot in an unknown dynamic environment based on an actual-virtual target switching strategy. This hybrid architecture is a combination of deliberative and reactive architectures which consists of three layers: modeling, planning and reaction. The deliberative architecture produces collision-free with shortest-distance path, while using the reactive architecture generates safe and time minimal navigation path. The proposed approach differs from previous ones in its integration architecture, the control techniques implemented in each module, and interfaces between the deliberative and reactive components. Validity and feasibility of the proposed approach are verified through simulation and real robot experiments
Analysis and Development of Computational Intelligence based Navigational Controllers for Multiple Mobile Robots
Navigational path planning problems of the mobile robots have received considerable attention over the past few decades. The navigation problem of mobile robots are consisting of following three aspects i.e. locomotion, path planning and map building. Based on these three aspects path planning algorithm for a mobile robot is formulated, which is capable of finding an optimal collision free path from the start point to the target point in a given environment. The main objective of the dissertation is to investigate the advanced methodologies for both single and multiple mobile robots navigation in highly cluttered environments using computational intelligence approach. Firstly, three different standalone computational intelligence approaches based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Cuckoo Search (CS) algorithm and Invasive Weed Optimization (IWO) are presented to address the problem of path planning in unknown environments. Next two different hybrid approaches are developed using CS-ANFIS and IWO-ANFIS to solve the mobile robot navigation problems. The performance of each intelligent navigational controller is demonstrated through simulation results using MATLAB. Experimental results are conducted in the laboratory, using real mobile robots to validate the versatility and effectiveness of the proposed navigation techniques. Comparison studies show, that there are good agreement between them. During the analysis of results, it is noticed that CS-ANFIS and IWO-ANFIS hybrid navigational controllers perform better compared to other discussed navigational controllers. The results obtained from the proposed navigation techniques are validated by comparison with the results from other intelligent techniques such as Fuzzy logic, Neural Network, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and other hybrid algorithms. By investigating the results, finally it is concluded that the proposed navigational methodologies are efficient and robust in the sense, that they can be effectively implemented to solve the path optimization problems of mobile robot in any complex environment
A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment
Formation control and cooperative motion planning are two major research areas currently being used in multi robot motion planning and coordination. The current study proposes a hybrid framework for guidance and navigation of swarm of unmanned surface vehicles (USVs) by combining the key characteristics of formation control and cooperative motion planning. In this framework, two layers of offline planning and online planning are integrated and applied on a practical marine environment. In offline planning, an optimal path is generated from a constrained A* path planning approach, which is later smoothed using a spline. This optimal trajectory is fed as an input for the online planning where virtual target (VT) based multi-agent guidance framework is used to navigate the swarm of USVs. This VT approach combined with a potential theory based swarm aggregation technique provides a robust methodology of global and local collision avoidance based on known positions of the USVs. The combined approach is evaluated with the different number of USVs to understand the effectiveness of the approach from the perspective of practicality, safety and robustness.</jats:p
Free singularity path planning of hybrid parallel robot
This paper presents a singularity-free path planning approach for a hybrid parallel robot. The hybrid robot is composed of two well-known parallel robots, a hexapod and a tripod, that are serially connected. In this paper a methodology is developed to avoid singularity configurations of the hybrid parallel robot. Nominal polynomial paths are used for motion of end effector, and the strokes of each actuator is calculated by using the developed inverse kinematic. A MATLAB program has been developed to generate the designed paths, and several poses have been tested in a CAD model of the hybrid parallel robot to validate the feasibility of the path planning approach
Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction
Navigating in search and rescue environments is challenging, since a variety
of terrains has to be considered. Hybrid driving-stepping locomotion, as
provided by our robot Momaro, is a promising approach. Similar to other
locomotion methods, it incorporates many degrees of freedom---offering high
flexibility but making planning computationally expensive for larger
environments.
We propose a navigation planning method, which unifies different levels of
representation in a single planner. In the vicinity of the robot, it provides
plans with a fine resolution and a high robot state dimensionality. With
increasing distance from the robot, plans become coarser and the robot state
dimensionality decreases. We compensate this loss of information by enriching
coarser representations with additional semantics. Experiments show that the
proposed planner provides plans for large, challenging scenarios in feasible
time.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Brisbane, Australia, May 201
PSO-Based Optimal Coverage Path Planning for Surface Defect Inspection of 3C Components with a Robotic Line Scanner
The automatic inspection of surface defects is an important task for quality
control in the computers, communications, and consumer electronics (3C)
industry. Conventional devices for defect inspection (viz. line-scan sensors)
have a limited field of view, thus, a robot-aided defect inspection system
needs to scan the object from multiple viewpoints. Optimally selecting the
robot's viewpoints and planning a path is regarded as coverage path planning
(CPP), a problem that enables inspecting the object's complete surface while
reducing the scanning time and avoiding misdetection of defects. However, the
development of CPP strategies for robotic line scanners has not been
sufficiently studied by researchers. To fill this gap in the literature, in
this paper, we present a new approach for robotic line scanners to detect
surface defects of 3C free-form objects automatically. Our proposed solution
consists of generating a local path by a new hybrid region segmentation method
and an adaptive planning algorithm to ensure the coverage of the complete
object surface. An optimization method for the global path sequence is
developed to maximize the scanning efficiency. To verify our proposed
methodology, we conduct detailed simulation-based and experimental studies on
various free-form workpieces, and compare its performance with a
state-of-the-art solution. The reported results demonstrate the feasibility and
effectiveness of our approach
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
A reconfigurable hybrid intelligent system for robot navigation
Soft computing has come of age to o er us a wide array of powerful and e cient algorithms
that independently matured and in
uenced our approach to solving problems in robotics,
search and optimisation. The steady progress of technology, however, induced a
ux of new
real-world applications that demand for more robust and adaptive computational paradigms,
tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and
to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms
and neural networks. As noted in the literature, they are signi cantly more powerful than
individual algorithms, and therefore have been the subject of research activities in the past
decades. There are problems, however, that have not succumbed to traditional hybridisation
approaches, pushing the limits of current intelligent systems design, questioning their solutions
of a guarantee of optimality, real-time execution and self-calibration. This work presents an
improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle
avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search
algorithm and the Voronoi diagram generation algorithm
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