Tag Archives: Robot Navigation

Deep learning RL methods for robot navigation

June 29, 2023 10:47 ,

Luong, M., Pham, C., Incremental Learning for Autonomous Navigation of Mobile Robots based on Deep Reinforcement Learning, . J Intell Robot Syst 101, 1 (2021) DOI: 10.1007/s10846-020-01262-5.

This paper presents an incremental learning method and system for autonomous robot navigation. The range finder laser sensor and online deep reinforcement learning are utilized for generating the navigation policy, which is effective for avoiding obstacles along the robot’s trajectories as well as for robot’s reaching the destination. An empirical experiment is conducted under simulation and real-world settings. Under the simulation environment, the results show that the proposed method can generate a highly effective navigation policy (more than 90% accuracy) after only 150k training iterations. Moreover, our system has slightly outperformed deep-Q, while having considerably surpassed Proximal Policy Optimization, two recent state-of-the art robot navigation systems. Finally, two experiments are performed to demonstrate the feasibility and effectiveness of our robot’s proposed navigation system in real-time under real-world settings.

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

Combination of RL with human provided models for navigation

June 26, 2023 11:58 ,

Amarildo Likmeta, Alberto Maria Metelli, Andrea Tirinzoni, Riccardo Giol, Marcello Restelli, Danilo Romano, Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving, . Robotics and Autonomous Systems, Volume 131, 2020 DOI: 10.1016/j.robot.2020.103568.

The design of high-level decision-making systems is a topical problem in the field of autonomous driving. In this paper, we combine traditional rule-based strategies and reinforcement learning (RL) with the goal of achieving transparency and robustness. On the one hand, the use of handcrafted rule-based controllers allows for transparency, i.e., it is always possible to determine why a given decision was made, but they struggle to scale to complex driving scenarios, in which several objectives need to be considered. On the other hand, black-box RL approaches enable us to deal with more complex scenarios, but they are usually hardly interpretable. In this paper, we combine the best properties of these two worlds by designing parametric rule-based controllers, in which interpretable rules can be provided by domain experts and their parameters are learned via RL. After illustrating how to apply parameter-based RL methods (PGPE) to this setting, we present extensive numerical simulations in the highway and in two urban scenarios: intersection and roundabout. For each scenario, we show the formalization as an RL problem and we discuss the results of our approach in comparison with handcrafted rule-based controllers and black-box RL techniques.

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

Selecting the best visual cues in the next future for reducing the computational cost of localization under limited computational resources

February 13, 2019 11:49 ,

L. Carlone and S. Karaman, Attention and Anticipation in Fast Visual-Inertial Navigation, IEEE Transactions on Robotics, vol. 35, no. 1, pp. 1-20, Feb. 2019 DOI: 10.1109/TRO.2018.2872402.

We study a visual-inertial navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of VIN? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate VIN while appearance-based feature selection fails to track robot’s motion during aggressive maneuvers.

Posted in: Computer vision, Mobile Robot Localization , Tagged: ,

An application of MDPs to UAV collision-free navigation with an interesting taxonomy of the state-of-the-art

February 5, 2019 10:24 ,

Xiang Yu1, Xiaobin Zhou2, Youmin Zhang, Collision-Free Trajectory Generation and Tracking for UAVs Using Markov Decision Process in a Cluttered Environment, Journal of Intelligent & Robotic Systems, 2019, 93:17–32 DOI: 10.1007/s10846-018-0802-z.

A collision-free trajectory generation and tracking method capable of re-planning unmanned aerial vehicle (UAV) trajectories can increase flight safety and decrease the possibility of mission failures. In this paper, a Markov decision process (MDP) based algorithm combined with backtracking method is presented to create a safe trajectory in the case of hostile environments. Subsequently, a differential flatness method is adopted to smooth the profile of the rerouted trajectory for satisfying the UAV physical constraints. Lastly, a flight controller based on passivity-based control (PBC) is designed to maintain UAV’s stability and trajectory tracking performance. simulation results demonstrate that the UAV with the proposed strategy is capable of avoiding obstacles in a hostile environment.

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

Including the dynamics of the environment in robot motion planning (navigation)

September 4, 2018 15:10 ,

María-Teresa Lorente, Eduardo Owen, and Luis Montano, Model-based robocentric planning and navigation for dynamic environments, The International Journal of Robotics Research Vol 37, Issue 8, pp. 867 – 889 DOI: 10.1177/0278364918775520.

This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented.

Posted in: Robot motion planning , Tagged:

A review on mobile robot navigation

July 25, 2018 08:15 ,

Tzafestas, S.G. , Mobile Robot Control and Navigation: A Global Overview,J Intell Robot Syst (2018) 91: 35 DOI: 10.1007/s10846-018-0805-9.

The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

Posted in: Robot motion planning , Tagged: ,

A robotic wheelchair navigation algorithm that plans paths taking into account the discomfort of the user

March 28, 2018 09:07 ,

Yoichi Morales, Atsushi Watanabe, Florent Ferreri, Jani Even, Kazuhiro Shinozawa, Norihiro Hagita, Passenger discomfort map for autonomous navigation in a robotic wheelchair, Robotics and Autonomous Systems, Volume 103, 2018, Pages 13-26, DOI: 10.1016/j.robot.2018.02.002.

This work presents a navigational approach that takes into consideration the perception of comfort by a human passenger. Comfort is the state of being at ease and free from stress; thus, comfortable navigation is a ride that, in addition to being safe, is perceived by the passenger as being free from anxiety and stress. This study considers how to compute passenger comfortable paths. To compute such paths, passenger discomfort is studied in locations with good visibility and those with no visibility. In locations with good visibility, passenger preference to ride in the road is studied. For locations with non-visible areas, the relationship between passenger visibility and discomfort is studied. Autonomous-navigation experiments are performed to build a map of human discomfort that is used to compute global paths. A path planner is proposed that minimizes a three-variable cost function: location discomfort cost, area visibility cost, and path length cost. Planner parameters are calibrated toward a composite trajectory histogram built with data taken from participant self-driving trajectories. Finally, autonomous navigation experiments with 30 participants show that the proposed approach is rated as more comfortable than the state-of-the-art shortest planner approach.

Posted in: Robot motion planning , Tagged: ,

On the drawbacks of RRT and how including deterministic sampling can help

January 31, 2018 08:05 ,

Lucas Janson, Brian Ichter, and Marco Pavone, Deterministic sampling-based motion planning: Optimality, complexity, and performance , The International Journal of Robotics Research Vol 37, Issue 1, pp. 46 – 61, DOI: 10.1177/0278364917714338.

Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the rapidly exploring random tree (RRT) algorithms, represent one of the most successful approaches to robotic motion planning, due to their strong theoretical properties (in terms of probabilistic completeness or even asymptotic optimality) and remarkable practical performance. Such algorithms are probabilistic in that they compute a path by connecting independently and identically distributed (i.i.d.) random points in the configuration space. Their randomization aspect, however, makes several tasks challenging, including certification for safety-critical applications and use of offline computation to improve real-time execution. Hence, an important open question is whether similar (or better) theoretical guarantees and practical performance could be obtained by considering deterministic, as opposed to random, sampling sequences. The objective of this paper is to provide a rigorous answer to this question. Specifically, we first show that PRM, for a certain selection of tuning parameters and deterministic low-dispersion sampling sequences, is deterministically asymptotically optimal, in other words, it returns a path whose cost converges deterministically to the optimal one as the number of points goes to infinity. Second, we characterize the convergence rate, and we find that the factor of sub-optimality can be very explicitly upper-bounded in terms of theℓ2 -dispersion of the sampling sequence and the connection radius of PRM. Third, we show that an asymptotically optimal version of PRM exists with computational and space complexity arbitrarily close to O(n) (the theoretical lower bound), where n is the number of points in the sequence. This is in contrast to the O(nlogn) complexity results for existing asymptotically optimal probabilistic planners. Fourth, we discuss extending our theoretical results and insights to other batch-processing algorithms such as FMT*, to non-uniform sampling strategies, to k-nearest-neighbor implementations, and to differentially constrained problems. Importantly, our main theoretical tool is the ℓ2-dispersion, an interesting consequence of which is that all our theoretical results also hold for low-ℓ2-dispersion random sampling (which i.i.d. sampling does not satisfy). In other words, achieving deterministic guarantees is really a matter of i.i.d. sampling versus non-i.i.d. low-dispersion sampling (with deterministic sampling as a prominent case), as opposed to random versus deterministic. Finally, through numerical experiments, we show that planning with deterministic (or random) low-dispersion sampling generally provides superior performance in terms of path cost and success rate.

Posted in: Robot motion planning , Tagged: ,

A theoretical framework based on hybrid models and logical verification to prove the guarantees for obstacle avoidance in mobile robot navigation

October 28, 2017 08:40 ,

Stefan Mitsch, Khalil Ghorbal, David Vogelbacher, and André Platzer, Formal verification of obstacle avoidance and navigation of ground robots, The International Journal of Robotics Research Vol 36, Issue 12, pp. 1312 – 1340, DOI: 0.1177/0278364917733549.

This article answers fundamental safety questions for ground robot navigation: under which circumstances does which control decision make a ground robot safely avoid obstacles? Unsurprisingly, the answer depends on the exact formulation of the safety objective, as well as the physical capabilities and limitations of the robot and the obstacles. Because uncertainties about the exact future behavior of a robot’s environment make this a challenging problem, we formally verify corresponding controllers and provide rigorous safety proofs justifying why the robots can never collide with the obstacle in the respective physical model. To account for ground robots in which different physical phenomena are important, we analyze a series of increasingly strong properties of controllers for increasingly rich dynamics and identify the impact that the additional model parameters have on the required safety margins. We analyze and formally verify: (i) static safety, which ensures that no collisions can happen with stationary obstacles; (ii) passive safety, which ensures that no collisions can happen with stationary or moving obstacles while the robot moves; (iii) the stronger passive-friendly safety, in which the robot further maintains sufficient maneuvering distance for obstacles to avoid collision as well; and (iv) passive orientation safety, which allows for imperfect sensor coverage of the robot, i.e., the robot is aware that not everything in its environment will be visible. We formally prove that safety can be guaranteed despite sensor uncertainty and actuator perturbation. We complement these provably correct safety properties with liveness properties: we prove that provably safe motion is flexible enough to let the robot navigate waypoints and pass intersections. To account for the mixed influence of discrete control decisions and the continuous physical motion of the ground robot, we develop corresponding hybrid system models and use differential dynamic logic theorem-proving techniques to formally verify their correctness. Since these models identify a broad range of conditions under which control decisions are provably safe, our results apply to any control algorithm for ground robots with the same dynamics. As a demonstration, we also synthesize provably correct runtime monitor conditions that check the compliance of any control algorithm with the verified control decisions.

Posted in: Robot motion planning , Tagged: , ,

Qualitative robot navigation

June 22, 2017 10:06 ,

Sergio Miguel-Tomé, Navigation through unknown and dynamic open spaces using topological notions, Connection Science, DOI: 10.1080/09540091.2016.1277691.

Until now, most algorithms used for navigation have had the purpose of directing system towards one point in space. However, humans communicate tasks by specifying spatial relations among elements or places. In addition, the environments in which humans develop their activities are extremely dynamic. The only option that allows for successful navigation in dynamic and unknown environments is making real-time decisions. Therefore, robots capable of collaborating closely with human beings must be able to make decisions based on the local information registered by the sensors and interpret and express spatial relations. Furthermore, when one person is asked to perform a task in an environment, this task is communicated given a category of goals so the person does not need to be supervised. Thus, two problems appear when one wants to create multifunctional robots: how to navigate in dynamic and unknown environments using spatial relations and how to accomplish this without supervision. In this article, a new architecture to address the two cited problems is presented, called the topological qualitative navigation architecture. In previous works, a qualitative heuristic called the heuristic of topological qualitative semantics (HTQS) has been developed to establish and identify spatial relations. However, that heuristic only allows for establishing one spatial relation with a specific object. In contrast, navigation requires a temporal sequence of goals with different objects. The new architecture attains continuous generation of goals and resolves them using HTQS. Thus, the new architecture achieves autonomous navigation in dynamic or unknown open environments.

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