arXiv subjects

Jagannath Prasad Sahoo

Publications and source records attributed to Jagannath Prasad Sahoo.

3 recordsLinked to original sources

arXiv2026

Competitive Task Allocation with Temporal Proposal Windows and Improved Artificial Potential Fields for Dual-Arm Industrial Manipulation

Modern industrial automation demands autonomous collaborative systems capable of coordinated manipulation in workspaces with heterogeneous object arrangements. Existing systems rely on predetermined trajectories and rigid allocation strategies, causing suboptimal workspace utilization and collision risks. This paper presents a distance-based competitive task allocation framework for dual-arm systems, integrating real-time vision perception with collision-free motion planning. A temporal window mechanism enables each arm to evaluate object detections over five consecutive vision frames before committing to optimal distance-based selection, featuring dynamic reallocation and spatial conflict resolution at 0.01m threshold accuracy. An improved Artificial Potential Field (iAPF) framework provides coordinated dual-arm movements through exponential goal attraction, inter-arm repulsion, home-seeking, and velocity damping forces. Simulation results validate superiority over market-based, round-robin, and fixed allocation strategies in distance efficiency, collision risk, and workspace coverage. Hardware experiments using two 6-DOF Kinova Gen3 Lite manipulators demonstrate approximately 47% average velocity improvement over fixed allocation in homogeneous scenarios with zero collisions, while mixed object scenarios confirm universal handling with approximately 11% time overhead offset by superior safety margins. The framework integrates YOLOv8 OBB detection at 30 FPS with 10 Hz control loops, demonstrating practical deployment capability for industrial assembly and sorting operations.

cs.RO↗

arXiv2026

Safe Multi-Robot Collaborative Transport Using Density Functions

This paper presents a hierarchical density-based model predictive control framework for safe collaborative manipulation by multiple quadrupedal robots. The framework enables a team of robots to push a shared object to a desired pose using only the initial and goal poses, without requiring a precomputed reference trajectory. A centralized box-level MPC optimizes contact forces while enforcing a control-density constraint for goal convergence and obstacle avoidance. Each robot then solves its own distributed robot-level whole-body MPC, under a stated shared-information assumption, to track its moving contact location while accounting for static obstacles and the time-varying positions of neighboring robots. The approach is evaluated in MuJoCo using whole-body contact dynamics for two and three Unitree Go2 quadrupeds collaboratively pushing rigid objects through narrow passages. Comparisons with matched Control Barrier Function and RRT* based tracking baselines demonstrate the effectiveness of the proposed density-based formulation for push-only, force- and torque-coupled manipulation tasks. Implementation videos are available at https://jaggu2606.github.io/go2-density-mpc-pushing/

cs.RO↗

arXiv2026

Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped

A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no hip or ankle roll. This paper presents a Hierarchical Reinforcement Learning (HRL) framework in which a High-Level (HL) policy observes the robot pose, 36 raycast proximity measurements, moving-obstacle states, and a receding-horizon local goal, and outputs a body-velocity command $(v_x, v_y, ω_{yaw})$ every ten control steps, while a velocity-conditioned Low-Level (LL) policy tracks each command through PD-controlled joint targets. Both policies are trained jointly with Soft Actor-Critic (SAC). Because the converged gait is task-agnostic, it is frozen and driven by classical planners over the same command interface, yielding three controlled baselines: SAC+A*, SAC+RRT*, and SAC+APF. Across 100 evaluation trials per method in randomized PyBullet environments, the proposed method reaches the goal in 98.0% of static and 88.0% of dynamic trials, against at most 78.0% and 68.0% for the planner hybrids, with path lengths within 4% of the A* reference, and ablations confirm that each observation channel and reward term contributes materially to this performance.

cs.RO↗