We propose a trust-based bilateral negotiation mechanism for task allocation problems under incomplete information, where a deceptive agent may disseminate false information to manipulate the outcome. Although numerous techniques have been explored to identify and reject such malicious agents, these approaches are often impractical due to insufficient prior knowledge, distributed implementation, or dependency on external entities. In contrast, the proposed mechanism enables agents to autonomously mitigate deception through trust evolution and distributed decision-making while acknowledging possible deception. The proposed mechanism integrates trust and cooperative utility into the Monotonic Concession Protocol, where the extended Zeuthen strategy forms a Nash equilibrium. A trust-weighted utility function is introduced, allowing agents to adaptively adjust their negotiation behavior based on the opponent’s behavior. We provide theoretical analysis of the mechanism, including Nash equilibrium, possible deception, and reachability. During negotiation, agents iteratively update trust, which governs both their degree of cooperation and information disclosure. A higher trust level encourages collaboration and gradual information sharing, while lower trust promotes selfish behavior and limited information exposure. Extensive Monte Carlo simulations demonstrate that the proposed mechanism effectively mitigates deception and enhances negotiation outcomes across various deception scenarios.

This paper addresses the problem of generating quadcopter minimum snap trajectories for real time applications. Previous efforts addressed this problem by either employing a gradient descent method, or by greatly sacrificing optimality for faster solutions that are amenable for onboard implementation. In this work, outputs of the gradient descent method are used offline to train a supervised neural network. We show that the use of neural networks results typically in two orders of magnitude reduction in computational time. Our proposed approach can be used for warm-starting onboard implementable iterative methods with an “educated ” initial guess. This work is motivated by the application for human-machine interface in which a human provides desired trajectory through a smart-tablet interface, which has to be translated into a dynamically feasible trajectory for a quadcopter. The proposed solution is tested in thousands of different examples, demonstrating its effectiveness as a booster for minimum snap trajectory generation for quadcopters.

The research field of swarm robotics continues to draw inspiration from the behavior of animals in nature. Obstacle avoidance and the navigation in unknown areas are major problems in control of swarming agents. In recent years, a large number of algorithms have been developed for this purpose. These algorithms are mainly based on artificial potential functions and many of the existing strategies do not enable agents to escape from non-convex obstacles. Due to the issue of local minimum, agents with a limited sensor range can often stay stuck behind concave obstacles. In this study, we propose a new algorithm to make many concave obstacles avoidable for flocks in unknown environments through sharing and evaluating the aggregated sensing information among the group.

This paper studies the problem of position-tracking adaptive control for planar robotic manipulators through visual servoing under a fixed-camera configuration. The uncertain parameters enter through two separate channels: 1) through the robot dynamics in terms of constant linearly appearing inertia values and 2) nonlinearly appearing camera calibration parameters such as the camera orientation angle and scale factor uncertainties. The associated adaptive control problem is challenged by the presence of the nonlinearly appearing uncertain camera-calibration parameters. We provide a novel solution to this problem by constructing two layers of control structures. To be specific, the inner layer is a conventional certainty-equivalence adaptive controller that stabilizes the faster robot dynamics, whereas the outer layer is a novel controller structure that is designed to handle the nonlinear parameterizations within the slower dynamics of the vision system. We prove global stability and boundedness for all of the closed-loop signals of the cascade interconnection under the relatively standard assumption of Jacobian nonsingularity. If the control objective is set-point regulation, we guarantee global asymptotic convergence for the tracking errors. On the other hand, if the desired trajectory is time-varying, we show that the closed-loop tracking errors under the action of the proposed controller converge to a residual set whose size is determined by the speed of the desired trajectory. Furthermore, we show that the size of this residual error set can be made arbitrarily small by appropriate choice of certain parameters within the controller structure. In contrast with other solutions for this problem that are documented in existing literature, we completely avoid overparameterizations and make no restrictions on the possible range of the camera orientation angle or any additional persistence of excitation requirements. The only piece of prior knowledge required for our solution is the availability of a lower bound on the camera scale factor. The effectiveness of the proposed scheme is illustrated through numerical simulations.