Tag Archives: Navigation

Survey on robotics navigation, particularly using RL and other approaches for self-learning that task

December 12, 2024 16:07 ,

Suaib Al Mahmud, Abdurrahman Kamarulariffin, Azhar Mohd Ibrahim, Ahmad Jazlan Haja Mohideen, Advancements and Challenges in Mobile Robot Navigation: A Comprehensive Review of Algorithms and Potential for Self‐Learning Approaches, Journal of Intelligent & Robotic Systems (2024) 110:120, DOI: 10.1007/s10846-024-02149-5.

Mobile robot navigation has been a very popular topic of practice among researchers since a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous algorithms (traditional AI-based, swarm intelligence-based, self-learning-based) have been built and implemented independently, and also in blended manners. Nevertheless, the problem of efficient autonomous robot navigation persists in multiple degrees due to the limitation of these algorithms. The lack of knowledge on the implemented techniques and their shortcomings act as a hindrance to further development on this topic. This is why an extensive study on the previously implemented algorithms, their applicability, their weaknesses as well as
their potential needs to be conducted in order to assess how to improve mobile robot navigation performance. In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. The findings also insinuate that in the domain of machine learning-based algorithms, integration of knowledge representation with a neuro-symbolic approach has the capacity to improve the accuracy and performance of self-robot navigation training by a significant margin.

Posted in: Applications of reinforcement learning to robots, Robot motion planning , Tagged:

How self-learning in mobile robot navigation can tackle situations rarely coped with by other methods in spite of their long training time

October 10, 2024 09:30 ,

Al Mahmud, S., Kamarulariffin, A., Ibrahim, A.M. et al. , Advancements and Challenges in Mobile Robot Navigation: A Comprehensive Review of Algorithms and Potential for Self-Learning Approaches, J Intell Robot Syst 110, 120 (2024) DOI: 10.1007/s10846-024-02149-5.

Mobile robot navigation has been a very popular topic of practice among researchers since a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous algorithms (traditional AI-based, swarm intelligence-based, self-learning-based) have been built and implemented independently, and also in blended manners. Nevertheless, the problem of efficient autonomous robot navigation persists in multiple degrees due to the limitation of these algorithms. The lack of knowledge on the implemented techniques and their shortcomings act as a hindrance to further development on this topic. This is why an extensive study on the previously implemented algorithms, their applicability, their weaknesses as well as their potential needs to be conducted in order to assess how to improve mobile robot navigation performance. In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. The findings also insinuate that in the domain of machine learning-based algorithms, integration of knowledge representation with a neuro-symbolic approach has the capacity to improve the accuracy and performance of self-robot navigation training by a significant margin.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Classical task planning at an abstract level for achieving good low level motion planning under uncertainty

July 11, 2023 09:00 ,

Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto, MPTP: Motion-planning-aware task planning for navigation in belief space, . Robotics and Autonomous Systems, Volume 141, 2021 DOI: 10.1016/j.robot.2021.103786.

We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In this paper, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our approach by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work.

Posted in: Robot motion planning, Robot task planning , Tagged:

Physiological bases of navigation

July 11, 2023 08:54 ,

Eva Zita Patai, Hugo J. Spiers, The Versatile Wayfinder: Prefrontal Contributions to Spatial Navigation, . Trends in Cognitive Sciences, Volume 25, Issue 6, 2021, Pages 520-533 DOI: 10.1016/j.tics.2021.02.010.

The prefrontal cortex (PFC) supports decision-making, goal tracking, and planning. Spatial navigation is a behavior that taxes these cognitive processes, yet the role of the PFC in models of navigation has been largely overlooked. In humans, activity in dorsolateral PFC (dlPFC) and ventrolateral PFC (vlPFC) during detours, reveal a role in inhibition and replanning. Dorsal anterior cingulate cortex (dACC) is implicated in planning and spontaneous internally-generated changes of route. Orbitofrontal cortex (OFC) integrates representations of the environment with the value of actions, providing a ‘map’ of possible decisions. In rodents, medial frontal areas interact with hippocampus during spatial decisions and switching between navigation strategies. In reviewing these advances, we provide a framework for how different prefrontal regions may contribute to different stages of navigation.

Posted in: Psycho-physiological bases of engineering , Tagged: