arXiv subjects

Rakesh Kumar Sahoo

Publications and source records attributed to Rakesh Kumar Sahoo.

6 recordsLinked to original sources

arXiv2025

Obstacle Avoidance of UAV in Dynamic Environments Using Direction and Velocity-Adaptive Artificial Potential Field

The conventional Artificial Potential Field (APF) is fundamentally limited by the local minima issue and its inability to account for the kinematics of moving obstacles. This paper addresses the critical challenge of autonomous collision avoidance for Unmanned Aerial Vehicles (UAVs) operating in dynamic and cluttered airspace by proposing a novel Direction and Relative Velocity Weighted Artificial Potential Field (APF). In this approach, a bounded weighting function, $\omega(\theta,v_{e})$, is introduced to dynamically scale the repulsive potential based on the direction and velocity of the obstacle relative to the UAV. This robust APF formulation is integrated within a Model Predictive Control (MPC) framework to generate collision-free trajectories while adhering to kinematic constraints. Simulation results demonstrate that the proposed method effectively resolves local minima and significantly enhances safety by enabling smooth, predictive avoidance maneuvers. The system ensures superior path integrity and reliable performance, confirming its viability for autonomous navigation in complex environments.

eess.SY↗

arXiv2025

Model-Less Feedback Control of Space-based Continuum Manipulators using Backbone Tension Optimization

Continuum manipulators offer intrinsic dexterity and safe geometric compliance for navigation within confined and obstacle-rich environments. However, their infinite-dimensional backbone deformation, unmodeled internal friction, and configuration-dependent stiffness fundamentally limit the reliability of model-based kinematic formulations, resulting in inaccurate Jacobian predictions, artificial singularities, and unstable actuation behavior. Motivated by these limitations, this work presents a complete model-less control framework that bypasses kinematic modeling by using an empirically initialized Jacobian refined online through differential convex updates. Tip motion is generated via a real-time quadratic program that computes actuator increments while enforcing tendon slack avoidance and geometric limits. A backbone tension optimization term is introduced in this paper to regulate axial loading and suppress co-activation compression. The framework is validated across circular, pentagonal, and square trajectories, demonstrating smooth convergence, stable tension evolution, and sub-millimeter steady-state accuracy without any model calibration or parameter identification. These results establish the proposed controller as a scalable alternative to model-dependent continuum manipulation in a constrained environment.

cs.RO↗

arXiv2025

Satellite Navigation and Control using Physics-Informed Artificial Potential Field and Sliding Mode Controller

Increase in the number of space exploration missions has led to the accumulation of space debris, posing risk of collision with the operational satellites. Addressing this challenge is crucial for the sustainability of space operations. To plan a safe trajectory in the presence of moving space debris, an integrated approach of artificial potential field and sliding mode controller is proposed and implemented in this paper. The relative 6-DOF kinematics and dynamics of the spacecraft is modelled in the framework of geometric mechanics with the relative configuration expressed through exponential coordinates. Various collision avoidance guidance algorithms have been proposed in the literature but the Artificial Potential Field guidance algorithm is computationally efficient and enables real-time path adjustments to avoid collision with obstacles. However, it is prone to issues such as local minima. In literature, local minima issue is typically avoided by either redefining the potential function such as adding vorticity or by employing search techniques which are computationally expensive. To address these challenges, a physics-informed APF is proposed in this paper where Hamiltonian mechanics is used instead of the traditional Newtonian mechanics-based approach. In this approach, instead of relying on attractive and repulsive forces for path planning, the Hamiltonian approach uses the potential field to define a path of minimum potential. Additionally, to track the desired trajectory planned by the guidance algorithm within a fixed-time frame, a non-singular fixed-time sliding mode controller (FTSMC) is used. The proposed fixed-time sliding surface not only ensures fixed-time convergence of system states but also guarantees the global stability of the closed-loop system without singularity. The simulation results presented support the claims made.

eess.SY↗

arXiv2025

Collision-Free Trajectory Planning and control of Robotic Manipulator using Energy-Based Artificial Potential Field (E-APF)

Robotic trajectory planning in dynamic and cluttered environments remains a critical challenge, particularly when striving for both time efficiency and motion smoothness under actuation constraints. Traditional path planner, such as Artificial Potential Field (APF), offer computational efficiency but suffer from local minima issue due to position-based potential field functions and oscillatory motion near the obstacles due to Newtonian mechanics. To address this limitation, an Energy-based Artificial Potential Field (APF) framework is proposed in this paper that integrates position and velocity-dependent potential functions. E-APF ensures dynamic adaptability and mitigates local minima, enabling uninterrupted progression toward the goal. The proposed framework integrates E-APF with a hybrid trajectory optimizer that jointly minimizes jerk and execution time under velocity and acceleration constraints, ensuring geometric smoothness and time efficiency. The entire framework is validated in simulation using the 7-degree-of-freedom Kinova Gen3 robotic manipulator. The results demonstrate collision-free, smooth, time-efficient, and oscillation-free trajectory in the presence of obstacles, highlighting the efficacy of the combined trajectory optimization and real-time obstacle avoidance approach. This work lays the foundation for future integration with reactive control strategies and physical hardware deployment in real-world manipulation tasks.

cs.RO↗

arXiv2025

Long-Range Rendezvous and Docking Maneuver Control of Satellite using Cross-Feedback Sliding Mode Controller

Satellite rendezvous and docking (RvD) maneuvers are essential for satellite servicing and in-orbit assembly. Traditional approaches often treat translational and rotational motions independently, simplifying control design but potentially leading to inefficiencies in maneuver time and fuel consumption. To address these challenges, a novel cross-feedback sliding mode controller has been proposed, developing an interdependent regulation system for translational and rotational motion. This method decouples the relative translational and rotational motion of chaser satellite with respect to target satellite while incorporating cross-feedback mechanisms to account for their inherent coupling. By incorporating rotational state information into translational control laws and vice versa, the approach ensures coordinated adjustments, enhancing maneuver efficiency. The chaser satellite manages both translational and rotational adjustments to rendezvous and dock with the target satellite. The stability of the cross-feedback sliding mode controller is established within the Lyapunov framework, and simulation results substantiate the effectiveness of this strategy.

eess.SY↗

arXiv2025

Coupled Rendezvous and Docking Maneuver control of satellite using Reinforcement learning-based Adaptive Fixed-Time Sliding Mode Controller

Satellite dynamics in unknown environments are inherently uncertain due to factors such as varying gravitational fields, atmospheric drag, and unpredictable interactions with space debris or other celestial bodies. Traditional sliding mode controllers with fixed parameters often struggle to maintain optimal performance under these fluctuating conditions. Therefore, an adaptive controller is essential to address these challenges by continuously tuning its gains in real-time. In this paper, we have tuned the slopes of the Fixed-time Sliding surface adaptively using reinforcement learning for coupled rendezvous and docking maneuver of chaser satellite with the target satellite in an unknown space environment. The neural network model is used to determine the optimal gains of reaching law of the fixed-time sliding surface. We have assumed that we don't have an accurate model of the system so we have added noise in the tangent space instead of directly on the manifold to preserve the geometric structure of the system while ensuring mathematically consistent uncertainty propagation. The reinforcement learning is used as an approximator to represent the value function of the agent to estimate the dynamical model of the system using the Actor-Critic method. The proposed control algorithm integrates a neural network and a sliding mode controller in a cascade loop architecture, where the tracking error dynamically tunes the sliding surface gains. Global fixed-time stability of the closed-loop feedback system is proved within the Lyapunov framework. This comprehensive approach of fixed-time sliding mode controller using a Reinforcement Learning based ensures the completion of the mission efficiently while addressing the critical challenges posed by the uncertain environment. The simulation results presented support the claims made.

eess.SY↗