Journal papers

• B. Yousuf, Zs. Lendek, L. Busoniu, 3D exploration-based search for multiple targets using a UAV. Journal of Intelligent Robotic Systems, 2023. Submitted. »
  Abstract: Consider an unmanned aerial vehicle (UAV) that searches for an unknown number of targets at unknown positions in 3D space. A particle filter uses imperfect measurements about the targets to update an intensity function that represents the expected number of targets. We propose a receding-horizon planner that selects the next UAV position by maximizing a joint, exploration and targetrefinement objective. Confidently localized targets are saved and removed from consideration. A nonlinear controller with an obstacle-avoidance component is used to reach the desired waypoints. We demonstrate the performance of our approach through a series of simulations, as well as in real-robot experiments with a Parrot Mambo drone that searches for targets from a constant altitude. The proposed planner works better than a lawnmower and a target-refinement-only method. «
• T. Santejudean, L. Busoniu, Online learning control for path-aware global optimization with nonlinear mobile robots. Control Engineering Practice, vol. 126, 2022. »
  Abstract: Consider a robot with nonlinear dynamics that must quickly find a global optimum of an objective function defined over its operating area, e.g., a chemical concentration, physical measurement, quantity of material etc. The function is initially unknown and must be learned online from samples acquired in a single trajectory. Applying classical optimization methods in this scenario would be highly suboptimal, since they would place the next sample arbitrarily far, without taking into account robot motion constraints, and would not revise the path based on new information accumulated along it. To address these limitations, a novel algorithm called Path-Aware Optimistic Optimization (OOPA) is proposed. The decision of which robot action to apply is formulated as an optimal control problem in which the rewards are refinements of the upper bound on the objective, weighted by bound and objective values to focus the search around optima. OOPA is evaluated in extensive simulations where it is compared to path-unaware optimization baselines, and in a real experiment in which a ROBOTIS TurtleBot3 successfully searches for the lowest grayscale location on a 2D surface.

  Online at Elsevier.

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• M. Rosynski, L. Busoniu, A Simulator and First Reinforcement Learning Results for Underwater Mapping. Sensors, vol. 22, no. 14, 2022. »
  Abstract: Underwater mapping with mobile robots has a wide range of applications, and good models are lacking for key parts of the problem, such as sensor behavior. The specific focus here is the huge environmental problem of underwater litter, in the context of the Horizon 2020 SeaClear project, where a team of robots is being developed to map and collect such litter. No reinforcement-learning solution to underwater mapping has been proposed thus far, even though the framework is well suited for robot control in unknown settings. As a key contribution, this paper therefore makes a first attempt to apply deep reinforcement learning (DRL) to this problem by exploiting two state-of-the-art algorithms and making a number of mapping-specific improvements. Since DRL often requires millions of samples to work, a fast simulator is required, and another key contribution is to develop such a simulator from scratch for mapping seafloor objects with an underwater vehicle possessing a sonar-like sensor. Extensive numerical experiments on a range of algorithm variants show that the best DRL method collects litter significantly faster than a baseline lawn mower trajectory.

  Online at MDPI.

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Conference papers

• B. Yousuf, Zs. Lendek, L. Busoniu, Multi-Agent Exploration-Based Search for an Unknown Number of Targets. Accepted at Proceedings of the 22nd IFAC World Congress (IFAC-23), Yokohama, Japan, 9–14 July 2023. »
  Abstract: This paper presents an active sensor fusion technique for multiple mobile agents (robots) to detect an unknown number of static targets at unknown positions. To process and fuse sensor measurements from the agents, we use a random finite set formulation with an iterated-corrector probability hypothesis density filter. Our main contribution is to introduce two different multi-agent planners to quickly find the targets. The planners make greedy decisions for the next state of each agent by maximizing an objective function consisting of target refinement and exploration components. We demonstrate the performance of our approach through a series of simulations using homogeneous and heterogeneous agents. The results show that our framework works better than a lawnmower baseline, and that a centralized version of the planner works best. «
• B. Yousuf, Zs. Lendek, L. Busoniu, Exploration-Based Search for an Unknown Number of Targets using a UAV. In Proceedings of the 6th IFAC Conference on Intelligent Control and Automation Sciences (ICONS-22), Cluj-Napoca, Romania, 13–15 July 2022. »
  Abstract: We consider a scenario in which a UAV must locate an unknown number of targets at unknown locations in a 2D environment. A random finite set formulation with a particle filter is used to estimate the target locations from noisy measurements that may miss targets. A novel planning algorithm selects a next UAV state that maximizes an objective function consisting of two components: target refinement and an exploration. Found targets are saved and then disregarded from measurements to focus on refining poorly seen targets. The desired next state is used as a reference point for a nonlinear tracking controller for the robot. Simulation results show that the method works better than lawnmower and mutual-information baselines.

  Online at ScienceDirect.

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• M. Dragomir, V.M. Maer, L. Busoniu, The Co4AIR Marathon – A Matlab Simulated Drone Racing Competition. In Proceedings of the 2022 International Conference on Unmanned Aircraft Systems (ICUAS-22), pages 1219--1226, Dubrovnik, Croatia, 21–24 June 2022. »
  Abstract: We describe a UAV competition concept in which a Parrot Mambo drone must race over a sequence of colored markers in minimum time. The competition is implemented in Matlab, using the Simulink Support Package for Parrot Minidrones, and can be organized fully in simulation, although an optional real-drone component is included. Students with either control or computer-science backgrounds are accommodated by providing baseline solution modules for the part outside their expertise. We present the competition design, a baseline solution, and our experience with the first edition, which was held in 2021, including student feedback and lessons learned.

  Online at IEEEXplore.

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• T. Santejudean, L. Busoniu, V. Varma, C. Morarescu, A simple path-aware optimization method for mobile robots. In Proceedings of the 6th IFAC Symposium on Telematics Applications (TA-22), pages 1–6, Nancy, France, 15–17 June 2022. »
  Abstract: We present an approach for a mobile robot to seek the global maximum of an initially unknown function defined over its operating space. The method exploits a Lipschitz assumption to define an upper bound on the function from previously seen samples, and optimistically moves towards the largest upper-bound point. This point is iteratively changed whenever new samples make it clear that it is suboptimal. In simulations, the method finds the global maxima with much less computation than an existing, much more involved technique, while keeping performance acceptable. Real-robot experiments confirm the effectiveness of the approach.

  Online at ScienceDirect.

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• V.M. Maer, L. Tamas, L. Busoniu, Underwater robot pose estimation using acoustic methods and intermittent position measurements at the surface. In Proceedings of the 2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR-22), Cluj-Napoca, Romania, 19–21 May 2022. »
  Abstract: Global positioning systems can provide sufficient positioning accuracy for large scale robotic tasks in open environments. However, in underwater environments, these systems cannot be directly used, and measuring the position of underwater robots becomes more difficult. In this paper we first evaluate the performance of existing pose estimation techniques for an underwater robot equipped with commonly used sensors for underwater control and pose estimation, in a simulated environment. In our case these sensors are inertial measurement units, Doppler velocity log sensors, and ultra-short baseline sensors. Secondly, for situations in which underwater estimation suffers from drift, we investigate the benefit of intermittently correcting the position using a high-precision surface-based sensor, such as regular GPS or an assisting unmanned aerial vehicle that tracks the underwater robot from above using a camera.

  Online at IEEEXplore.

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• T. Santejudean, L. Busoniu, Path-aware optimistic optimization for a mobile robot. In Proceedings 60th IEEE Conference on Decision and Control (CDC-21), pages 3584–3590, Austin, US, 13–17 December 2021. »
  Abstract: We consider problems in which a mobile robot samples an unknown function defined over its operating space, so as to find a global optimum of this function. The path travelled by the robot matters, since it influences energy and time requirements. We consider a branch-and-bound algorithm called deterministic optimistic optimization, and extend it to the path-aware setting, obtaining path-aware optimistic optimization (OOPA). In this new algorithm, the robot decides how to move next via an optimal control problem that maximizes the long-term impact of the robot trajectory on lowering the upper bound, weighted by bound and function values to focus the search on the optima. An online version of value iteration is used to solve an approximate version of this optimal control problem. OOPA is evaluated in extensive experiments in two dimensions, where it does better than path-unaware and local-optimization baselines.

  Online at IEEEXplore.

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• I. Lal, C. Morarescu, J. Daafouz, L. Busoniu, Optimistic planning for near-optimal control of nonlinear systems with hybrid inputs. In Proceedings 60th IEEE Conference on Decision and Control (CDC-21), pages 2486–2493, Austin, US, 13–17 December 2021. »
  Abstract: We propose an optimistic planning, branch-and-bound algorithm for nonlinear optimal control problems in which there is a continuous and a discrete action (input). The dynamics and rewards (negative costs) must be Lipschitz but can otherwise be general, as long as certain boundedness conditions are satisfied by the continuous action, reward, and Lipschitz constant of the dynamics. We investigate the structure of the space of hybrid-input sequences, and based on this structure we propose an optimistic selection rule for the subset with the largest upper bound on the value, and a way to select the largest-impact action for further refinement. Together, these fully define the algorithm, which we call OPHIS: optimistic planning for hybrid-input systems. A near-optimality bound is provided together with empirical results in two nonlinear problems where the algorithm is applied in receding horizon.

  Online at IEEEXplore.

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Last update: Tuesday, 21 November 2023.