This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

System Identification I

Path Planning with Modified a Star Algorithm for a Mobile Robot

In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.

Mobile robot path planning using membrane evolutionary artificial potential field

This article deals with path planning of a mobile robot based on a grid map. Essential assumption for path planning is a mobile robot with functional and reliable reactive navigation and SLAM. Therefore, such issues are not addressed in this article. The main body of the article introduces several modifications (Basic Theta*, Phi*) and improvements (RSR, JPS) of A star algorithm. These modifications are focused primarily on computational time and the path optimality. Individual modifications were evaluated in several scenarios, which varied in the complexity of environment. On the basis of these evaluations, it is possible to choose path planning method suitable for individual scenario. © 2014 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of organizing committee of the Modelling of Mechanical and Mechatronic Systems MMaMS 2014. Keywords: path planning, A* algorithm, Basic Theta*, Phi*, Jump Point Search Nomenclature SLAM Simultaneous Localization and Mapping A* A star RSR Rectangular Symmetry Reduction JPS Jump Point Search

Modelling of Mechanical and Mechatronic Systems MMaMS 2014 Path planning with modified A star algorithm for a mobile robot

This paper presents a new optimal motion planning aiming to minimize the energy consumption of a wheeled mobile robot in robot applications. A model that can be used to formulate the energy consumption for kinetic energy transformation and for overcoming traction resistance is developed first. This model will provide a base for minimizing the robot energy consumption through a proper motion planning. To design the robot path, the A* algorithm is employed to generate an energy-efficient path where a new energy-related criterion is utilized in the cost function. To achieve a smooth trajectory along the generated path, the appropriate arrival time and velocity at the defined waypoints are selected for minimum energy consumption. Simulations and experiments are performed to demonstrate the energy-saving efficiency of the proposed motion planning approach.

Minimizing Energy Consumption of Wheeled Mobile Robots via Optimal Motion Planning

This paper presents a motion-planning approach for coordinating multiple mobile robots in moving along specified paths. The robots are required to fulfill formation requirements while meeting velocity/acceleration constraints and avoiding collisions. Coordination is achieved by planning robot velocities along the paths through a velocity-optimization process. An objective function for minimizing formation errors is established and solved by a linear interactive and general optimizer. Motion planning can be further adjusted online to address emergent demands such as avoiding suddenly appearing obstacles. Simulations and experiments are performed on a group of mobile robots to demonstrate the effectiveness of the proposed coordinated motion planning in multirobot formations.

Coordinated Motion Planning for Multiple Mobile Robots Along Designed Paths With Formation Requirement

Robot-assisted surgery is of growing interest in the surgical and engineering communities. The use of robots allows surgery to be performed with precision using smaller instruments and incisions, resulting in shorter healing times. However, using current technology, an operator cannot directly feel the operation because the surgeon-instrument and instrument-tissue interaction force feedbacks are lost during needle insertion. Advancements in force feedback and control not only help reduce tissue deformation and needle deflection but also provide the surgeon with better control over the surgical instruments. The goal of this review is to summarize the key components surrounding the force feedback and control during robot-assisted needle insertion. The literature search was conducted during the middle months of 2017 using mainstream academic search engines with a combination of keywords relevant to the field. In total, 166 articles with valuable contents were analyzed and grouped into five related topics. This survey systemically summarizes the state-of-the-art force control technologies for robot-assisted needle insertion, such as force modeling, measurement, the factors that influence the interaction force, parameter identification, and force control algorithms. All studies show force control is still at its initial stage. The influence factors, needle deflection or planning remain open for investigation in future.

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Force Modeling, Identification, and Feedback Control of Robot-Assisted Needle Insertion: A Survey of the Literature

Robots that work in a proper formation show several advantages compared to a single complex robot, such as a reduced cost, robustness, efficiency and improved performance. Existing researches focused on the method of keeping the formation shape during the motion, but usually neglect collision constraints or assume a simplified model of obstacles. This paper investigates the path planning of forming a target robot formation in a clutter environment containing unknown obstacles. The contribution lies in proposing an efficient path planner for the multiple mobile robots to achieve their goals through the clutter environment and developing a dynamic priority strategy for cooperation of robots in forming the target formation. A multirobot system is set up to verify the proposed method of robot path planning. Simulations and experiments results demonstrate that the proposed method can successfully address the collision avoidance problem as well as the formation forming problem.

A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

In numerous cellular applications, cells are transported to specific positions or extracted from complex cell solutions. Therefore, an efficient cell transportation path planner for these applications is important for avoiding collisions with other cells or obstacles. In this paper, a path planning approach to transporting cells using a robot-aided optical manipulation system is presented. Optical tweezers functions as a special end-effector in transporting a target cell to the desired position along the generated path. The path planner is designed based on the rapidly exploring random trees (RRT) algorithm for calculating a collision-free path for cell transportation. Both static and dynamic path planners are developed. For the dynamic path planner, an online monitoring strategy is employed to dynamically avoid collisions with randomly appeared obstacles caused by environmental influence such as the Brownian movement of microparticles. Experiments of transporting yeast cells are performed to demonstrate the effectiveness of the proposed approach. Note to Practitioners - Manipulations of cells and other microparticles represent an essential process for most cell-based bioengineering applications, such as cytopathology, cell sociology, and cytotaxonomy. Cell transportation, which is treated as a typical cell manipulation task, has recently received considerable attention because of its wide applications. This paper presents a novel approach to applying RRT-based path planner to cell transportation with a robot-aided optical manipulation system. The research outcome provides a unique solution to achieving cell transportation automatically and efficiently.

Shuang Liu

Papers

Minimizing Energy Consumption of Wheeled Mobile Robots via Optimal Motion Planning

Coordinated Motion Planning for Multiple Mobile Robots Along Designed Paths With Formation Requirement

Force Modeling, Identification, and Feedback Control of Robot-Assisted Needle Insertion: A Survey of the Literature

A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

Rapidly Exploring Random Tree Algorithm-Based Path Planning for Robot-Aided Optical Manipulation of Biological Cells