Bi-objective Motion Planning Approach for Safe Motions: Application to a Collaborative Robot

International audienceAccepted version freely available here: [ http://bit.ly/2qlyjJ6 ] Online version via SpringerLink: [ http://link.springer.com/article/10.1007/s10846-019-01110-1 ] Abstract: This paper presents a new bi-objective safety-oriented path planning strategy for robotic manipulators. Integrated into a sampling-based algorithm, our approach can successfully enhance the task safety by guiding the expansion of the path towards the safest configurations. Our safety notion consists of avoiding dangerous situations, e.g. being very close to the obstacles, human awareness, e.g. being as much as possible in the human vision field, as well as ensuring human safety by being as far as possible from human with hierarchical priority between human body parts. Experimental validations are conducted in simulation and on the real Baxter research robot. They revealed the efficiency of the proposed method, mainly in the case of a collaborative robot sharing the workspace with humans

Autonomous Planning and Mapping for the Characterization of Gamma Contaminated Environments

The past $100$ years of research and development in the fields of nuclear power, weapons, and industrial radiation applications have imbibed regions across the world with facilities and terrain which is contaminated with radioactive material. Such locations can pose significant hazards to human health, thus requiring vigilant monitoring and mitigation efforts. The use of autonomous robots is well suited to this task. Motivated by this fact, this work contributes a holistic perspective on the deployment, design, and use of autonomous robots for the characterization of radioactively contaminated environments. The set of developments presented in this dissertation incorporate principles of gamma radiation detection and measurement, techniques for mapping and localizing a variety of radioactive sources, path planning strategies tailored to both ground and aerial platforms, as well as prototype systems implementing methods for perception and navigation in dirty, dangerous, and degraded conditions. Specifically, Chapter \ref{chap:intro} presents the motivation behind this work, including its practical application, as well as a brief description of the approach utilized to accomplish environmental radiation characterization. Chapter \ref{chap:contrib} presents a detailed overview of the presented radiation mapping contributions and associated publications in addition to a brief note on other synergistic contributions made towards enabling autonomy in the perceptually degraded environments associated in particular with waste decommissioning facilities. Subsequently the core contributions of this thesis are presented in detail. Chapter \ref{chap:single_source} presents a method for autonomous single source localization using an aerial robot, alongside details regarding principles of radiation measurement and detection. Chapter \ref{chap:radbot} describes a technique developed to map distributed radiation fields in 2D using a ground platform, while Chapter \ref{chap:radmf} extends the work to perform the mapping task in 3D using a collision tolerant micro aerial vehicle. Subsequently, Chapter \ref{chap:auro} presents autonomous distributed 3D radiation mapping coupled with an intelligent path planning algorithm tailored to source seeking behaviors in confined environments. Finally, conclusions and an outlook for future research are discussed in Chapter \ref{chap:conclusions}.Overall, this dissertation contributes a body of work enabling autonomous radiological surveying in challenging conditions, demonstrating robust functionality through a series of field experiments using real radiation sources. Each of the presented methods is associated with a tested and reliable robotic system purpose-built for its designated task. This combination of performance robotic hardware demonstrating novel autonomous functionality in realistic use-case scenarios showcases the applicability and dependability of the presented systems and methods

Path planning algorithms for autonomous navigation of a non-holonomic robot in unstructured environments

openPath planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms.Path planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms

Path planning for robotic truss assembly

A new Potential Fields approach to the robotic path planning problem is proposed and implemented. Our approach, which is based on one originally proposed by Munger, computes an incremental joint vector based upon attraction to a goal and repulsion from obstacles. By repetitively adding and computing these 'steps', it is hoped (but not guaranteed) that the robot will reach its goal. An attractive force exerted by the goal is found by solving for the the minimum norm solution to the linear Jacobian equation. A repulsive force between obstacles and the robot's links is used to avoid collisions. Its magnitude is inversely proportional to the distance. Together, these forces make the goal the global minimum potential point, but local minima can stop the robot from ever reaching that point. Our approach improves on a basic, potential field paradigm developed by Munger by using an active, adaptive field - what we will call a 'flexible' potential field. Active fields are stronger when objects move towards one another and weaker when they move apart. An adaptive field's strength is individually tailored to be just strong enough to avoid any collision. In addition to the local planner, a global planning algorithm helps the planner to avoid local field minima by providing subgoals. These subgoals are based on the obstacles which caused the local planner to fail. A best-first search algorithm A* is used for graph search

Hybrid PSO-PWL-Dijkstra approach for path planning of non holonomic platforms in dense contexts

Planning is an essential capability for autonomous robots. Many applications impose a diversity of constraints and traversing costs in addition to the usually considered requirement of obstacle avoidance. In applications such as route planning, the use of dense properties is convenient as these describe the terrain and other aspects of the context of operation more rigorously and are usually the result of a concurrent mapping and learning process. Unfortunately, planning for a platform with more than three degrees of freedom can be computationally expensive, particularly if the application requires the platform to optimally deal with a thorough description of the terrain. The objective of this thesis is to develop and demonstrate an efficient path planning algorithm based on dynamic programming. The goal is to compute paths for ground vehicles with and without trailers, that minimise a specified cost-to-go while taking into account dynamic constraints of the vehicle and dense properties of the environment. The proposed approach utilises a Quadtree Piece-Wise Linear (QT-PWL) approximation to describe the environment in a low dimensional subspace and later uses a particle approach to introduce the dynamic constraints of the vehicle and to smooth the path in the full dimensional configuration space. This implies that the optimisation process can exploit the QT-PWL partition. Many usual contexts of operation of autonomous platforms have cluttered spaces and large regions where the dense properties are smooth; therefore, the QT-PWL partition is able to represent the context in a fraction of cells that would be needed by a homogeneous grid. The proposed methodology includes adaptations to both algorithms to achieve higher efficiency of the computational cost and optimality of the planned path. In order to demonstrate the capabilities of the algorithm, an idealized test case is presented and discussed. The case for a car and a tractor with multiple trailers is presented. A real path planning example is presented in addition to the synthetic experiments. Finally, the experiments and results are analysed and conclusions and directions for possible future work are presented

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Efficient Mission Planning for Robot Networks in Communication Constrained Environments

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations