Auto-calibration of the rotor thrust coefficient and estimation of ground effect are both challenging aspects of multirotor dynamics control and planning. Conventional approaches address these issues separately and typically rely on experimental rigs for bench testing. In this paper, we propose a low-cost onboard sensor array and a well-designed unified algorithm to enable fast auto-calibration of the rotor thrust coefficient alongside ground effect estimation (TRACER). Our sensor array consists of four force-sensitive resistors compactly placed under the quadrotor landing gear to measure contact force during the lift-off phase, capturing changes in thrust. The joint calibration and estimation problem is formulated to rapidly decouple ground effect influence from free-air rotor thrust based on sensor readings. Furthermore, our approach is adaptable to various flight control input formats (duty cycle, rotor throttle, or rpm), ensuring general applicability across different multirotor operations. Experimental results demonstrate that the proposed method provides reliable joint calibration and estimation of rotor thrust and ground effect in a short lift-off process, achieving less than 10% mean absolute percentage error compared to the ground truth.

Learning-based techniques hold considerable promise in achieving human-like autonomous driving. However, one deployed policy encounters difficulties in satisfying the drivers’ diverse decision-making preferences simultaneously. Meanwhile, training personalized policies for each driver from scratch is time-consuming and resource-intensive. To address these challenges, this paper proposes a human-guided continual learning framework, wherein the human drivers could real-time take over a deployed policy when it performs unsatisfactorily, and the autonomous vehicle (AV) agent would automatically acquire human demonstrations and dynamically alter itself in accordance with personalized decision-making preference. Furthermore, a priority experience memory-enabled elastic weight consolidation (PEM-EWC) mechanism is developed to prevent the AV agent from overfitting to a limited number of human demonstrations and catastrophically forgetting its acquired fundamental driving abilities. Driver-in-the-loop simulations and real-world experiments are conducted in representative autonomous driving decision-making scenarios, and experimental results demonstrate the superior equilibrium of our proposed approach in terms of driving safety, human likeness, and training efficiency, compared to other baselines, which suggests that it provides a promising solution for personalized decision-making in autonomous driving. The supplementary video is available athttps://youtu.be/HKF0ayxMycc.

Reducing undesirable path crossings among tra jectories of different robots is vital in multi-robot navigation missions, which not only reduces detours and conflict scenarios, but also enhances navigation efficiency and boosts productivity. Despite recent progress in multi-robot path-crossing-minimal (MPCM) navigation, the majority of approaches depend on the minimal squared-distance reassignment of suitable desired points to robots directly. However, if obstacles occupy the passing space, calculating the actual robot-point distances becomes complex or intractable, which may render the MPCM navigation in obstacle environments inefficient or even infeasible. In this paper, the concurrent-allocation task execution (CATE) algorithm is presented to address this problem (i.e., MPCM navigation in obstacle environments). First, the path-crossing related elements in terms of (i) robot allocation, (ii) desired-point convergence, and (iii) collision and obstacle avoidance are en coded into integer and control barrier function (CBF) constraints. Then, the proposed constraints are used in an online constrained optimization framework, which implicitly yet effectively minimizes the possible path crossings and trajectory length in obstacle environments by minimizing the desired point allocation cost and slack variables in CBF constraints simultaneously. In this way, the MPCM navigation in obstacle environments can be achieved with flexible spatial orderings. Note that the feasibility of solutions and the asymptotic convergence property of the proposed CATE algorithm in obstacle environments are both guaranteed, and the calculation burden is also reduced by concurrently calculating the optimal allocation and the control input directly without the path planning process. Finally, extensive simulations and experiments are conducted to validate that the CATE algorithm (i) outperforms the existing state-of-the-art baselines in terms of feasibility and efficiency in obstacle environments, (ii) is effective in environments with dynamic obstacles and is adaptable for per forming various navigation tasks in 2D and 3D, (iii) demonstrates its efficacy and practicality by 2D experiments with a multi-AMR onboard navigation system, and (iv) provides a possible solution to evade deadlocks and pass through a narrow gap.

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-the-art (SOTA) baselines. The demonstration video (https://www.youtube.com/watch?v=aqJCHcsj4w0) and the source code (https://github.com/OscarHuangWind/DRL-Transformer-SimtoReal-Navigation) are also provided.

Many intelligent robots use a combination of radar and camera sensors to capture environmental information. Robust and accurate perception highly relies on the result of multi-sensor calibration. Most current spatial calibration methods require a calibration board or a special marker as the target. In this paper, we provide a novel calibration method for RGBD camera and millimeter-wave radar, which automatically estimates the extrinsic parameters. Our proposed method includes the following two stages: rough extrinsic parameters are estimated by using object contours as geometric constraints, and meanwhile, the optimum is reached via optimizing based on the difference of velocity obtained from camera and radar. It only needs an object moving past sensors, but does not require for a calibration board. We validate our method through simulation experiments and real-world experiments. We construct a simulation environment in CARLA to verify the performance of our proposed method against different angles. Furthermore, different levels of zero mean Gaussian noise are added to evaluate the stability of our method. In addition, real-world experiments with different hardware setups are taken to verify the feasibility of our method in real-world conditions.

Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs) applications, but limited computing resource makes it challenging to deploy a well-behaved RL strategy with sophisticated neural networks. Meanwhile, the training of RL on navigation tasks is difficult, which requires a carefully-designed reward function and a large number of interactions, yet RL navigation can still fail due to many corner cases. This shows the limited intelligence of current RL methods, thereby prompting us to rethink combining RL with human intelligence. In this paper, a human-guided RL framework is proposed to improve RL performance both during learning in the simulator and deployment in the real world. The framework allows humans to intervene in RL's control progress and provide demonstrations as needed, thereby improving RL's capabilities. An innovative human-guided RL algorithm is proposed that utilizes a series of mechanisms to improve the effectiveness of human guidance, including human-guided learning objective, prioritized human experience replay, and human intervention-based reward shaping. Our RL method is trained in simulation and then transferred to the real world, and we develop a denoised representation for domain adaptation to mitigate the simulation-to-real gap. Our method is validated through simulations and real-world experiments to navigate UGVs in diverse and dynamic environments based only on tiny neural networks and image inputs. Our method performs better in goal-reaching and safety than existing learning- and model-based navigation approaches and is robust to changes in input features and ego kinetics. Furthermore, our method allows small-scale human demonstrations to be used to improve the trained RL agent and learn expected behaviors online.

Research on the shared control system for human-in-the-loop cooperative driving has grown steadily in the past decade. However, most proposed methodologies were focused on structural roads such as highway rather than the unstructured environment. This paper presents a novel potential hazard-aware shared steering approach for human-robot cooperative driving in unstructured environment. First, we propose a hierarchical Gaussian risk field (HGRF) to evaluate the potential hazard of the predicted path. Then an adaptive control authority allocation module is developed to engage the control in real-time. The control authority will be fully owned by the human driver when the vehicle drives in a safe manner. However, in the situation where the hazard level is predicted to be high, the authority of the human driver decrease, and the automation actively assists the driver by dynamically sharing the control authority to enhance safety and performance. The proposed methodology is experimentally verified with a steer-by-wire car-like mobile robot. The results show that our proposed approach can effectively engage and cooperatively control the vehicle in hazard cases, ensuring driving safety.

Surface diffraction noise (SDN) is a kind of coherent noise and pervasive in marine seismic data acquisition. This kind of noise is intractable during seismic data processing. We present a new method based on mesh scanning and mathematical morphological filtering (MMF) to attenuate the SDN in marine surveys. The idea of mesh scanning will be first put forward to get the energy-map of the survey. Compared with the traditional mesh scanning method, the energy-map computing formulas will be improved to protect the reflections from the recognition of SDNs, especially the shallow reflections. All the possible surface diffractors will be shown on the computed energy-map with strong energy. Then, a 2-D mathematical morphological closing operation will be innovatively used to get the upper envelope of the energy-map and reduce the number of the possible surface diffractors with a maximal value picking. After confirming all the possible surface diffractors on the energy-map, the travel-time curves of SDNs could be calculated easily according to the kinematic propagation law of seismic wave for all the related shot gathers and the MMF will be used to extract all the SDNs along the trajectories of them within appropriate time windows. Both a real 2-D and a real 3-D marine seismic data will be processed by the proposed method to show the positive effects in attenuating the SDNs.