A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

Robotic Surveillance Based on the Meeting Time of Random Walks

This paper analyzes the meeting time between a pair of pursuer and evader performing random walks on digraphs. The existing bounds on the meeting time usually work only for certain classes of walks and cannot be used to formulate optimization problems and design robotic strategies. First, by analyzing multiple random walks on a common graph as a single random walk on the Kronecker product graph, we provide the first closed-form expression for the expected meeting time in terms of the transition matrices of the moving agents. This novel expression leads to necessary and sufficient conditions for the meeting time to be finite and to insightful graph-theoretic interpretations. Second, based on the closed-form expression, we setup and study the minimization problem for the expected capture time for a pursuer/evader pair. We report theoretical and numerical results on basic case studies to show the effectiveness of the design.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0884

Advances in humanoid control and perception

One day there will be humanoid robots among us doing our boring, time-consuming, or dangerous tasks. They might cook a delicious meal for us or do the groceries. For this to become reality, many advances need to be made to the artificial intelligence of humanoid robots. The ever-increasing available computational processing power opens new doors for such advances. In this thesis we develop novel algorithms for humanoid control and vision that harness this power. We apply these methods on an iCub humanoid upper-body with 41 degrees of freedom. For control, we develop Natural Gradient Inverse Kinematics (NGIK), a sampling-based optimiser that applies natural evolution strategies to perform inverse kinematics. The resulting algorithm makes very few assumptions and gives much more freedom in definable constraints than its Jacobian-based counterparts. A special graph-building procedure is introduced to build Task-Relevant Roadmaps (TRM) by iteratively applying NGIK and storing the results. TRMs form searchable graphs of kinematic configurations on which a wide range of task-relevant humanoid movements can be planned. Through coordinating several instances of NGIK, a fast parallelised version of the TRM building algorithm is developed. To contrast the offline TRM algorithms, we also develop Natural Gradient Control which directly uses the optimisation pass in NGIK as an online control signal. For vision, we develop dynamic vision algorithms that form cyclic information flows that affect their own processing. Deep Attention Selective Networks (dasNet) implement feedback in convolutional neural networks through a gating mechanism that is steered by a policy. Through this feedback, dasNet can focus on different features in the image in light of previously gathered information and improve classification, with state-of-the- art results at the time of publication. Then, we develop PyraMiD-LSTM, which processes 3D volumetric data by employing a novel convolutional Long Short-Term Memory network (C-LSTM) to compute pyramidal contexts for every voxel, and combine them to perform segmentation. This resulted in state-of-the-art performance on a segmentation benchmark. The work on control and vision is integrated into an application on the iCub robot. A Fast-Weight PyraMiD-LSTM is developed that dynamically generates weights for a C-LSTM layer given actions of the robot. An explorative policy using NGC generates a stream of data, which the Fast-Weight PyraMiD-LSTM has to predict. The resulting integrated system learns to model the effects of head and hand movements and their effects on future visual input. To our knowledge, this is the first effective visual prediction system on an iCub

Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots

This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them

Model Simplification for Efficient Collision Detection in Robotics

Motion planning for industrial robots is a computationally intensive task due to the massive number of potential motions between any two configurations. Calculating all possibilities is generally not feasible. Instead, many motion planners sample a sub-set of the available space until a viable solution is found. Simplifying models to improve collision detection performance, a significant component of motion planning, results in faster and more capable motion planners. Several approaches for simplifying models to improve collision detection performance have been presented in the literature. However, many of them are sub-optimal for an industrial robotics application due to input model limitations, accuracy sacrifices, or the probability of increasing false negatives during collision queries. This thesis focuses on the development of model simplification approaches optimised for industrial robotics applications. Firstly, a new simplification approach, the Bounding Sphere Simplification (BSS), is presented that converts triangle-mesh inputs to a collection of spheres for efficient collision and distance queries. Additionally, BSS removes small features and generates an output model less prone to false negatives

Autonomous Vehicle Battery State-of-Charge Prognostics Enhanced Mission Planning

Most mission planning algorithms are designed for healthy systems. When faults occur in a system, it is advantageous to optimize the mission plan by taking the system health condition into consideration. In this paper, a mission planning scheme is proposed to integrate real-time prognostics in a receding horizon path planning framework to accommodate the system fault. In this scheme, the state-of-charge of a battery is monitored and predicted by a particle-filtering based prognostic algorithm. The predicted state-of-charge and remaining useful life of the battery are used in the mission planning to minimize mission failure risk. A series of experiments are presented on a robotic platform, which is powered by a Lithium-ion battery, to demonstrate and verify the proposed scheme

Enhanced Path Planning Method for Improving Safety and Productivity of Excavation Operations

Improving safety and productivity of earthwork operations is of paramount importance, especially in congested sites where collisions are more probable. Real-time Location Systems and Automated Machine Guidance and Control technologies are expected to improve both safety and productivity of earthwork operations by providing excavator operators a higher level of support regarding the path planning of excavators based on site conditions. However, in spite of the large number of studies related to automated path planning of excavators using well established algorithms from robotics, such as Rapidly-exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), these studies do not fully consider the engineering constrains of the equipment and do not result in smooth and optimal paths that can be applied in practice. This research aims to improve the path planning of excavators in a congested site where the speed of the algorithm and the quality of the path significantly influence the overall performance of the earthwork operations. The proposed method is implemented and tested in Unity 3D game engine environment for visualization and verification purposes. The efficiency of the proposed method in generating a collision-free path, which can ensure improved productivity, is verified both quantitatively and visually. The comparative results with other recent and modified versions of the RRT algorithm show that the proposed algorithm is able to find a higher quality path in a shorter time

A Theory of Collective Cell Migration and the Design of Stochastic Surveillance Strategies

In nature, complex emergent behavior arises in groups of biological entities often as a result of simple local interactions between neighbors in space or on a network. In such cases, scientific inquiry is typically aimed at inferring these local rules. Conversely, in teams of robots, the goal is to create decentralized control laws which results in efficient global behavior. These behaviors are designed for tasks such as maintaining formation control, performing effective coverage control or persistently monitoring an environment. With this in mind, we consider the following: 1> the emergence of collective cell migration from local contact and mechanical feedback and 2> the design of unpredictable surveillance strategies for teams of robots.Collective cell migration is an essential part of tissue and organ morphogenesis during embryonic development, as well as of various disease processes, such as cancer. The vast majority of theoretical descriptions of collective cell behavior focus on large numbers of cells, but fail to accurately capture the dynamics of small groups of cells. Here we introduce a low-dimensional theoretical description that successfully describes single cell migration, cell collisions, collective dynamics in small groups of cells, and force propagation during sheet expansion, all within a common theoretical framework. We also explain the counter-intuitive observation that pairs of cells repel each other upon collision while they coordinate their motion in larger clusters.Conventional monitoring strategies used by teams of robots are deterministic in nature making it possible for intelligent intruders who study the motion of the patrolling agent to compromise the patrol route. This problem can be solved by designing random walkers on graphs which naturally incorporate unpredictability. Within this framework, we study and provide the first analytic expression for the first meeting time of multiple random walkers, in terms of their transition matrices. We also study two problems related to maximizing unpredictability: given graph and visit frequency constraints, 1> maximize the entropy rate generated by a Markov chain, and 2> maximize the return time entropy associated with the Markov chain, where the return time entropy is the weighted average over all graph nodes of the entropy of the first return times of the Markov chain