In most real world applications, noisy and incomplete information about the robot proximity is inevitable due to imperfections coupled with the onboard sensors. The perception and control problems go hand in hand in order to efficiently plan safe robot maneuvers. This paper proposes a method to generate robot actions directly from a sequence of depth images. The notion of Artificial Potential Field (APF) approach is used where a robot action is obtained by combining the attractive and repulsive actions generated by the goal and the obstacles respectively. This article assumes environment perception uncertainty that relates to the estimation of an obstacle’s location relative to the robot. The repulsive action generation is formulated as a Partially Observable Markov Decision Process (POMDP). A Particle Filter (PF) approach is used to estimate and track valid scene points in the robot sensing horizon from an imperfect depth image stream. The most probable candidates for an occupied region are used to generate a velocity action that minimizes the repulsive potential at each time instant. Approximately optimal solutions to the POMDP are obtained using the QMDP technique which enables us to perform computationally expensive operations prior to a robot run. Consequently, suitable repulsive actions are generated onboard the robot, each time an image is received, in a computationally feasible way. An attractive action, obtained by solving for the negative gradient of the attractive potential is finally added to the repulsive action to generate a final robot action at every time step. Lastly, the robustness and reliability of this approach is demonstrated close-loop on a quadrotor UAV equipped with a depth camera. The experiments also demonstrate that the method is very computationally efficient and can be run on a variety of platforms that have limited resources on-board.

在大多数现实世界的应用中，由于机载传感器的缺陷，关于机器人接近度的嘈杂和不完整的信息是不可避免的。感知和控制问题齐头并进，以便有效地规划安全的机器人操作。本文提出了一种直接从深度图像序列生成机器人动作的方法。使用人工势场 (APF) 方法的概念，其中通过组合分别由目标和障碍物产生的吸引和排斥动作来获得机器人动作。本文假设与障碍物相对于机器人的位置估计相关的环境感知不确定性。排斥动作生成被公式化为部分可观察的马尔可夫决策过程（POMDP）。粒子滤波器 (PF) 方法用于从不完美的深度图像流中估计和跟踪机器人感知范围内的有效场景点。占用区域的最可能候选者用于生成速度动作，以最小化每个时刻的排斥潜力。POMDP 的近似最优解是使用 QMDP 技术获得的，该技术使我们能够在机器人运行之前执行计算成本高的操作。因此，每次接收到图像时，都会以计算上可行的方式在机器人上生成合适的排斥动作。通过求解吸引势的负梯度获得的吸引动作最终被添加到排斥动作中，以在每个时间步生成最终的机器人动作。最后，这种方法的稳健性和可靠性在配备深度相机的四旋翼无人机上进行了闭环验证。实验还表明，该方法在计算上非常高效，并且可以在机载资源有限的各种平台上运行。