Robotics and Autonomous Systems最新文献

Incorporating human natural features into the algorithmic functions of robots can enhance performance efficiency. One of the most popular features of human movements is the power law that defines the connection between movement speed and path curvature. To contribute to this area, by discussing how the power law can provide a reasonable balance between velocity and efficiency, we propose a novel method to design motion profiles based on the power law. The novelty of this solution lies in the adjustment approach for the power law. In this work, inspired by features of human hand movements, the overall curvature of non-circular shapes is considered as the shape-dependent criterion for motion planning. Also, a framework to apply the proposed approach to any open non-circular curvy contours is presented. To investigate the efficiency of the approach, we considered a simple drawing robot. The simulation and experimental results verify the efficacy of the proposed motion planning method.

Affected by the complex traffic environment, lane-changing and overtaking have become daily driving operations of autonomous vehicles, and providing a drivable trajectory is one of the critical tasks of planning processes. To this end, this paper aims to propose an optimization-algorithm-based double quintic polynomial trajectory planning model considering static and dynamic obstacles for lane-changing and overtaking maneuvers of the autonomous vehicle. Firstly, an improved double quintic polynomial planning model considering different motion states and sizes of obstacles is constructed by introducing the lane change transition state to ensure the autonomous vehicle’s driving safety. Secondly, a multi-objective performance function considering various influencing factors is established to improve the driving performances of the autonomous vehicle during lane-changing and overtaking. Finally, a particle swarm optimization (PSO) algorithm is used to optimize parameters of the proposed planning model, such as the lane change time, transition speed, and longitudinal displacement, to generate a driveability trajectory that meets the driving safety, comfort, stability, and low emission requirements of the autonomous vehicle during lane-changing and overtaking. The effectiveness and advantages of the proposed planning model are verified by comparing it with several existing planning models under different driving conditions.

Rhythmic motions are traditionally achieved by developing predetermined paths for the states of the space system to follow. Since these paths are obtained offline, the dynamic behavior fails to adapt to changes in environmental conditions or user command desires. The solution we propose is a new strategy called dynamic shaping, in which the paths are formed online to allow the system to create an orbit with the characteristics we need. Hereupon, this paper focuses on applying this strategy to Dynamics with One Degree of Under-actuation and Impulsive Phenomenon (DSODUIP) to adapt the characteristics of outcomes to be in line with the demands.

This research was conducted by leveraging the advantages of virtual holonomic constraints (VHCs) to establish these paths. Therefore, a novel two-level hierarchical control method is designed considering a stability criterion to avoid divergence. At the Low-Level, the controllers stabilize the output of system to follow the VHCs on the system. At the High-Level, the VHCs are modified to shape an orbit with our desired characteristic in the motion. As an illustrative example, the algorithm is implemented to adjust the average angular velocity of a devil stick and the hip velocity of a Three-Link biped robot. Their results vividly demonstrate smooth adjustments and efficient performance in achieving our desired outcomes.

Utilizing swarm robotics techniques can significantly enhance the efficiency of exploring and mapping hazardous environments, such as nuclear sites. Instead of relying on a single robot for exploration, employing multiple robots working in coordination allows for fast coverage and more comprehensive data collection. In this study, bio-inspired algorithms, specifically Lévy flight and stigmergy, are utilized to guide the robots' movements. The Lévy flight algorithm mimics the movement patterns observed in animals like sharks and honeybees during their search for food, while stigmergy involves indirect communication between agents through environmental traces. By integrating these algorithms with swarm robotics, the robots effectively explore radioactive environments, gather data, and generate detailed maps of the area. Our research delves into various aspects of exploration, including the influence of the number of deployed robots and their exposure to radiation. Comparative analysis reveals the efficacy of stigmergy as a superior approach for guiding swarm robot movements in radioactive environments. This study underscores the significant potential of employing collective robotics for exploration tasks in nuclear scenarios, highlighting the promising applications of swarm intelligence in enhancing safety and efficiency in hazardous environments.

Rational foot-end trajectory planning and control are of great significance for stable-legged walking of heavy-duty multi-legged robots. To achieve a fast, active, and compliant response of the leg actuator to disturbances for improvement of the stability and flexibility of the heavy-duty legged robot system during continuous walking on rough roads, a legged consensus control method (LCC) is proposed. Firstly, the LCC includes a foot-end trajectory planner model for designing the trajectory during the swing phase to ensure that the robot’s feet are always in a safe workspace during legged motion with continuously variable direction. Secondly, LCC constructs a consensus control method for encoding foot-end position and velocity consensus error based on variable topology networks. Six legs are treated as six intelligent agents and divided into two fully connected networks: the swing phase and stance phase, to achieve smooth and consistent motion that satisfies the geometric constraints of the robot. The foot-end agent can switch between swing and stance groups according to the state of the contact with the environment accompanied by the amendment topology, to enhance the robustness of the robot system through fast compliance control of the foot-end kinematics state. Then, the sliding mode control method based on consensus velocity and position error is deduced in LCC. The sliding mode surface is designed to make the three control variables realize stable movement with a consistent state of foot-end in three $X,Y,Z$-axis respectively, thereby enhancing the stability of foot-end state and fuselage posture. Finally, simulation and experiments have verified that the proposed LCC can assist legged-robot perform relatively steady legged motion with continuously variable direction on various rugged roads. The body attitude Root Mean Square Error ($RMSE$) is quickly reduced by 81.0% compared with independent PI control. The LCC algorithm code is publicly available at https://github.com/bjmyX/LCC_code.

In field robotics, particularly in the agricultural sector, precise localization presents a challenge due to the constantly changing nature of the environment. Simultaneous Localization and Mapping algorithms can provide an effective estimation of a robot’s position, but their long-term performance may be impacted by false data associations. Additionally, alternative strategies such as the use of RTK-GPS can also have limitations, such as dependence on external infrastructure. To address these challenges, this paper introduces a novel stability scan filter. This filter can learn and infer the motion status of objects in the environment, allowing it to identify the most stable objects and use them as landmarks for robust robot localization in a continuously changing environment. The proposed method involves an unsupervised point-wise labelling of LiDAR frames by utilizing temporal observations of the environment, as well as a regression network, called Long-Term Stability Network (LTS-NET) to learn and infer 3D LiDAR points long-term motion status. Experiments demonstrate the ability of the stability scan filter to infer the motion stability of objects on a real agricultural long-term dataset. Results show that by only utilizing points belonging to long-term stable objects, the localization system exhibits reliable and robust localization performance for long-term missions compared to using the entire LiDAR frame points.

Accurate depth estimation from monocular images is critical for various applications such as robotics, augmented reality, and autonomous navigation. However, achieving high accuracy while maintaining computational efficiency is a major challenge, particularly for resource-constrained devices. In this paper, we present LightDepth, an approach that leverages curriculum learning to estimate depth efficiently while taking into account resource constraints. It modifies the ground truth sparse depth maps from the KITTI dataset by resizing them to 31 extents during training to reduce sparsity and control complexity. The resulting model achieves comparable accuracy to state-of-the-art large models while outperforming them in response time by 71%. Our approach outperforms resource-efficient models regarding depth accuracy (measured by RMSE), achieving a 56% improvement. LightDepth is designed to be fast and resource-efficient, making it suitable for deployment in resource-constrained devices. It also balances the trade-off between accuracy and resource efficiency. All codes are available online at https://github.com/fatemehkarimii/lightdepth.

We describe an adaptive control framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The piecewise continuous interaction dynamics of such tasks make it difficult to construct and use a single dynamics model or control strategy. Also, the nonlinear dynamics during contact changes can damage the robot or the domain objects. Our framework enables the robot to incrementally improve its prediction of contact changes in such tasks, efficiently learn models for the piecewise continuous interaction dynamics, and to provide smooth and accurate trajectory tracking based on a task-space variable impedance controller. We experimentally compare the performance of our framework against that of representative control methods to establish that the adaptive control, prediction, and incremental learning capabilities of our framework are essential to achieve the desired smooth control of changing-contact robot manipulation tasks.

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacity. This paper aims to achieve deep reinforcement learning (DRL) that acquires SNN policies suitable for neurochip implementation. Since DRL requires a complex function approximation, we focus on conversion techniques from Floating Point NN (FPNN) because it is one of the most feasible SNN techniques. However, DRL requires conversions to SNNs for every policy update to collect the learning samples for a DRL-learning cycle, which updates the FPNN policy and collects the SNN policy samples. Accumulative conversion errors can significantly degrade the performance of the SNN policies. We propose Robust Iterative Value Conversion (RIVC) as a DRL that incorporates conversion error reduction and robustness to conversion errors. To reduce them, FPNN is optimized with the same number of quantization bits as an SNN. The FPNN output is not significantly changed by quantization. To robustify the conversion error, an FPNN policy that is applied with quantization is updated to increase the gap between the probability of selecting the optimal action and other actions. This step prevents unexpected replacements of the policy’s optimal actions. We verified RIVC’s effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72). The previous framework with no countermeasures against conversion errors failed to train the policies. Videos from our experiments are available: https://youtu.be/Q5Z0-BvK1Tc.