Performance optimisation of mobile robots in dynamic environments

This paper presents a robotic simulation system, that combines task allocation and motion planning of multiple mobile robots, for performance optimisation in dynamic environments. While task allocation assigns jobs to robots, motion planning generates routes for robots to execute the assigned jobs. Task allocation and motion planning together play a pivotal role in optimisation of robot team performance. These two issues become more challenging when there are often operational uncertainties in dynamic environments. We address these issues by proposing an auction-based closed-loop module for task allocation and a bio-inspired intelligent module for motion planning to optimise robot team performance in dynamic environments. The task allocation module is characterised by a closed-loop bid adjustment mechanism to improve the bid accuracy even in light of stochastic disturbances. The motion planning module is bio-inspired intelligent in that it features detection of imminent neighbours and responsiveness of virtual force navigation in dynamic traffic conditions. Simulations show that the proposed system is a practical tool to optimise the operations by a team of robots in dynamic environments. © 2012 IEEE.published_or_final_versionThe IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS 2012), Tianjin, China, 2-4 July 2012. In Proceedings of IEEE VECIMS, 2012, p. 54-5

Past, present and future of path-planning algorithms for mobile robot navigation in dynamic environments

Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more. Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased. Mobile robot navigation encompasses the following four requirements: perception, localization, path-planning and motion control. Among those, path-planning is a vital part of a fast, secure operation. During the last couple of decades, many path-planning algorithms were developed. Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited. This paper presents a qualitative comparative study of the up-to-date mobile robot path-planning methods capable of navigating robots in dynamic environments. The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm. The general advantages and disadvantages of each method are discussed. Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation. This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field. © 2020 IEEE

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

Human-robot co-navigation using anticipatory indicators of human walking motion

Mobile, interactive robots that operate in human-centric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closed-loop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot co-navigation

Probabilistic Collision Constraint for Motion Planning in Dynamic Environments

Online generation of collision free trajectories is of prime importance for autonomous navigation. Dynamic environments, robot motion and sensing uncertainties adds further challenges to collision avoidance systems. This paper presents an approach for collision avoidance in dynamic environments, incorporating robot and obstacle state uncertainties. We derive a tight upper bound for collision probability between robot and obstacle and formulate it as a motion planning constraint which is solvable in real time. The proposed approach is tested in simulation considering mobile robots as well as quadrotors to demonstrate that successful collision avoidance is achieved in real time application. We also provide a comparison of our approach with several state-of-the-art methods.Comment: Accepted for presentation at the 16th International Conference on Intelligent Autonomous Systems (IAS-16

Intelligent Hybrid Approach for Multi Robots- Multi Objectives Motion Planning Optimization

Abstract: This paper proposes enhanced approach to find multi objective optimization and obstacle avoidance of motion planning problem for multi mobile robots that have to move smoothly, safely with a shorter time and minimum distance along curvature-constrained motion planning in completely known dynamic environments. The research includes two stages: the first stage is to find an multi objective optimal path and trajectory planning for each robot individually using the Enhanced GA with modified A*. The second stage consists of designing a fuzzy logic to control the movement of the robots with collision free. The global optimal trajectory is fed to fuzzy motion controller which has ability to regenerate the local trajectory of the robot based on the probability of having another dynamic robot in the area. A simulation of the strategy has been presented and the results show that the proposed approach is able to achieve multi objective optimization of motion planning for multi mobile robot in dynamic environment efficiently. Also, it has the ability to find a solution when the environment is complex and the number of obstacles is increasing. The performance of the above mentioned approach has been found to be satisfactory of dynamic obstacle avoidance