This paper addresses trajectory planning for overtaking maneuvers in a dynamic, two-way, two-lane environment. The proposed novel five-stage planning algorithm comprises initial lane change, acceleration, final lane change, abort, and final lane change abort. This algorithm allows overtaking to be aborted in oncoming lane danger, reverting to the original lane. Employing a point mass model-based model predictive control (MPC) framework, separate longitudinal and lateral planning is conducted in each phase, combined to generate the overall trajectory. A practical nonlinear bicycle model is employed for trajectory tracking to address kinematic infeasibility. Furthermore, the tracking controller utilizes adaptive MPC. Numerical simulations validate the performance of the proposed algorithm.

The Patrolling Problem is a crucial feature of the surveillance task in defense and other establishments. Most of the works in the literature concentrate on reducing the Idleness value at each location in the environment. However, there are often a few prioritized locations that cannot be left unvisited beyond a certain Time Period. In this paper, we study the problem of Prioritized patrolling - the task of patrolling the given environment using multiple agents while ensuring the prioritized locations are visited within the pre-specified Time Period. We present a novel algorithm, namely, Time Period Based Patrolling (TPBP) algorithm, to solve the prioritized patrolling problem. It determines a sequence of walks for each agent online that complies with the Time Period requirement of the Priority nodes while reducing the Idleness of all the other nodes. We have tested and validated the algorithm using SUMO - a realistic simulator developed for traffic management. Since the existing strategies are not designed for Prioritized Patrolling, we show through comparison that proposed algorithm is required to solve the problem.

This paper proposes a trajectory planning strategy to simultaneously address multiple surveillance objectives such as complete area coverage, periodic surveillance, and guaranteed detection of a rogue element attempting to exit the area of interest. For agents with identical sensing capability, the proposed strategy defines a time-varying multi-agent formation on a Lissajous curve, which completes the aforementioned tasks in finite time with collision free paths for all agents in a 2-D rectangular region. This obviates the need for sensing and communication for collision avoidance among agents. A sufficient upper limit on agent dimensions that ensures collision free motion is derived. An algorithm is developed for choosing the number of agents and optimal Lissajous curve for a prescribed rectangle and agents' sensing capability. The optimal Lissajous curve is chosen to minimize the time period for repeated coverage and maximize the upper bound on agent size. The proposed algorithm is validated through computer simulations and experiments using differential drive robots.

This paper is concerned with obstacle avoidance control strategy of unicycle mobile robot in a dynamic environment. We propose a control algorithm based on collision cone approach to avoid collision with stationary obstacle. The algorithm is simple and easy to implement in the real world applications. The proposed algorithm is extended to the case of moving obstacle as well as irregular obstacle. The effectiveness of the proposed control algorithms are demonstrated through various simulations.

In this paper, analytical structures for fuzzy proportional-integral-derivative (PID) controllers are derived by using triangular membership functions for inputs, singletons, or triangular membership functions for output, minimum triangular norm, maximum or drastic sum triangular conorm, Mamdani minimum, drastic or Larsen product inference, nonlinear control rules, and center-of-sum defuzzification. It is shown that these analytical structures are not suitable for control purpose. In this context, it is extremely important to note that the analytical structures reported by Carvajal et al. are also not valid for control.